The salp swarm algorithm (SSA) [29] has only one control parameter and is simple and easy to implement. Using the particle swarm optimization technique to train a recurrent neural model. The performance of the. benchmark engineering optim function genetic algorithm global optimization grey wolf optimizer heuristic metaheuristic optimality optimisation optimization optimum particle swarm op problem solving salp swarm algorithm ssa. Improved multi-objective particle swarm optimization algorithm[J]. 49,inertia weight ω= 0. In order to avoid the individual into local extremum, the new algorithm uses the swarm and following behavior, meanwhile it combines with the adaptive parameter adjustment. In particular, the invention relates to a system and a method for sequence optimization for improved recombinant protein expression using a particle swarm optimization algorithm. Title: Improved adaptive multi-objective particle swarm algorithm under big data. Abstract: A preemption MO particle swarm optimisation algorithm is designed and realised. First, we observe the comparison between the traditional fish-swarm algorithm and the improved fish-swarm algorithm in the iteration times, as shown in Table 1 and Figure 1. If a position cannot be improved over a predefined number (called limit) of cycles, then the food source is abandoned. Improved particle swarm optimization algorithm GA and PSO are both population based algorithms that have proven to be successful in solving very difficult optimization problems (Kennedy and Eberhart. The water is pushed salps bodies to progress[16]. The algorithm dynamically evaluates the optimum number of controllers and the optimal connections between switches and controllers in large scale SDN networks. The non-dominated solutions found by swarm are stored in an external archive and nearest neighbor density estimator method is used to select a leader for the individual particles in the swarm. Therefore, the SSA-GWO algorithm has. Finally,in section 6 conclusions are discussed. The objective of this journal is to communicate recent and projected advances in computer-based engineering techniques. The performance of the. In this present work, we have defined a PSO improved safe routing approach to transfer data from congestion free and attack safe path. This chapter presents an improved multi-particle swarm co-evolution optimization algorithm (IMPSCO) to detect structural damage. According to a new paper submitted by four authors at the Laboratory of Intelligent Systems in Lausanne, Switzerland, the use of collective behavior in drone swarms can be fully controlled, improved and further developed in the coming years. The non-dominated solutions found by swarm are stored in an external archive and nearest neighbor density estimator method is used to select a leader for the individual particles in the swarm. A hybrid method of particle swarm optimization (PSO) and a genetic algorithm (GA) used to perform the genes selection. PubMed Central. speed of learning, simplicity of rules, visualizes the data and predictive accuracy. This architecture has a data pre-processing mechanism which consists of a normalization module and a data-shuffling module. An Improved Particle Swarm Algorithm for Optimal Design of Plate-Fin Heat Exchangers Hao Peng * † , Xiang Ling † and En Wu ‡ School of Mechanical and Power Engineering, Nanjing University of Technology, Nanjing 210009, China, and School of Mechanical and Electrical Engineering, Jinling Institute of Technology, Nanjing 210001, China. Comparisons between GA and PSOs have been performed by both Eberhart (Angeline. Examples of swarm intelligence in natural systems include ant colonies, bird flocking, hawks hunting, animal herding, bacterial growth, fish schooling and microbial intelligence. The food source F is the swarm's target. Its performance has been compared with existing standard optimization algorithms, namely particle swarm optimization, ant–lion optimization and salp swarm algorithm. a Civil Engineering, Iran University of Science and Technology, Tehran, Iran (e-mail: [email protected] Wang, Chun-Feng; Liu, Kui. Ewees 2,3 · Diego Oliva 4 · Mohamed Abd Elaziz 5 · Songfeng Lu 1,6. Eberhart simulated the bird locking and ish schooling foraging behaviors, they have used this simula-. The main paper is: This is the source codes of the paper:. Choosing to study in Australia will provide you with a unique perspective on the world that will prepare you for your role as a global citizen. , have constrained the application to PV systems. Its performance has been compared with existing standard optimization algorithms, namely particle swarm optimization, ant–lion optimization and salp swarm algorithm. The sectio nis divided into ve subsections, namely, a general description of BSO, stochastic ada ptive step. Downloadable! A novel methodology based on the recent metaheuristic optimization algorithm Salp Swarm Algorithm (SSA) for locating and optimal sizing of renewable distributed generators (RDGs) and shunt capacitor banks (SCBs) on radial distribution networks (RDNs) is proposed. Salp swarm algorithm (SSA) The position of salps is defined in an d- dimensional search space where d is the number of variables of a given problem. Tentative 18 Special Session Proposals List. It is a kind of stochastic optimized algorithm developed by Eberhart and Kennedy in 1995. Wei Yang, Qianglai Xie, and Ming Li "Inventory Control Method of Reverse Logistics for Shipping Electronic Commerce Based on Improved Multi-objective Particle Swarm Optimization Algorithm," Journal of Coastal Research 83(sp1), 786-790, (4 May 2019). A hybrid method of particle swarm optimization (PSO) and a genetic algorithm (GA) used to perform the genes selection. An Adaptive Cultural Algorithm with Improved Quantum-behaved Particle Swarm Optimization for Sonar Image Detection Skip to main content Thank you for visiting nature. proposed model is tested and compared with several machine. In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. 0, security is improved through the centralized distribution and management of Group Managed Service Account(gMSA) credentials using Docker Config functionality. Furthermore, the initial swarm of bacteria is generated in three groups depending on the boundaries of each decision variable so. 1Optimizationbasedontheparameter In formula 1, the parameter which affects the search ability of the algorithm is. In order to solve the above problems, an improved particle swarm optimization algorithm is proposed, and a method to resist premature convergence is designed so as to be able to better solve the logistics network service global optimization problem. SSA emulates the behavior of salps of the family Salpidae. , – In view of the local convergence problem with basic DPSO in ASP, this paper presents an IDPSO, in which a chosen strategy of global optimal particle is introduced in, to solve the ASP problems in the assembly process of. Improved particle swarm optimization algorithm GA and PSO are both population based algorithms that have proven to be successful in solving very difficult optimization problems (Kennedy and Eberhart. Moreover, it can converge to the global optimum due to its adaptive mechanism. proposed model is tested and compared with several machine. With the addition of an external memory, embodied. Authors: Tao Wang. Salps are part of Salpidae family with the limpid cylinder design body. Awarded to Seyedali Mirjalili on 20 Jul 2017. benchmark engineering optim function genetic algorithm global optimization grey wolf optimizer heuristic metaheuristic optimality optimisation optimization optimum particle swarm op problem solving salp swarm algorithm ssa. speed of learning, simplicity of rules, visualizes the data and predictive accuracy. The performance of existing variant has been tested on several standard and real life applications. The main paper is: This is the source codes of the paper:. Finally,in section 6 conclusions are discussed. Southwest Electronics and Telecommunication Technology Research Institute, Chengdu 610041, China; 2. a Civil Engineering, Iran University of Science and Technology, Tehran, Iran (e-mail: [email protected] To solve these problems, this paper proposes an improved adaptive inertia weight particle swarm optimization (AIWPSO) algorithm. The Artificial Bee Colony (ABC) algorithm is a swarm based meta-heuristic algorithm that was introduced by Karaboga in 2005 (Karaboga, 2005) for optimizing numerical problems. Professional Interests: Multi-objective optimization, Robust optimization, Swarm intelligence, Computational intelligence. Cite this article: Liu Baoning,Zhang Weiguo,Li Guangwen, et al. Civil Infrastructure; Emergency Response & Search and Rescue; For a few years now, the National Oceanic and Atmospheric Administration (NOAA) has been investing in unmanned aircraft and other technologies to increase weather observations designed to improve the accuracy of hurricane forecasting. Generally, the biological research about Salps is still in its early stages because their living environments are hardly accessible, and it is very difficult to keep them in laboratory environment. Therefore, energy proficiency is a crucial design issue in WSNs. An improved particle swarm optimization (IPSO) algorithm is proposed to solve reliability problems in this paper. When Microgrid is under the grid connected operation, this paper first sets the interactive transmission power between direct transmission line of. The Multi-Constrained Multicast Routing Improved by Hybrid Bacteria Foraging-Particle Swarm Optimization To solve multicast routing under multiple constraints, it is required to generate a multicast tree that ranges from a source to the destinations with minimum cost subject to several constraints. im facing a problem with the salp swarm algorithm the results that i requred from the code doesnt give me exact number the solution can be off by 0. Design/methodology/approach – This paper introduces a hybrid approach based on a modified ring neighborhood with two new perturbation operators designed to keep diversity. The performance of PSO can be improved in a variety of ways including adopting a multi-swarm strategy in which each swarm can explore a different portion of the solution space (Blackwell). An Improved Quantum-behaved Particle Swarm Optimization Algorithm Based on Chaos Theory www. The learning factors and of the improved algorithm are obtained from the chaotic sequences generated by the classical Logistic map [28]. This paper proposes a salp swarm algorithm (SSA) for optimal allocation of DGs and CBs. Quantum Particle Swarm optimization Algorithm (QPSO) Quantum particle swarm optimization (QPSO) algorithm is a kind of particle swarm algorithm. The traditional methods and models are based on explicit equations which neglect seepage and evaporation losses with low accuracy. AU - Liu, Zhao. The shape of a salp is shown below:. The working mechanism of SSA starts by producing a random set of \(N_S\) solutions (X) with dimension D. Convergence condition of PSO is obtained through solving and analyzing the differential equation. The social spider optimization algorithm is emulates the behavior of cooperation between spiders based on the biological laws of the cooperative colony. It is not the case with the present version thanks to two techniques: hyperspheres instead of hyperparallelepipeds for proximity areas, and adaptation of the swarm size as well as the relationships between the particles. 7 improved binary version of the artificial fish swarm algorithm (IbAFSA) for solving 8 the 0-1 MKP (1). Improved Glowworm Swarm Optimization Algorithm applied to Multi-level Thresholding Simone A. To solve these problems, this paper proposes an improved adaptive inertia weight particle swarm optimization (AIWPSO) algorithm. Regarding the nature and complexity of the employed non-algebraic equations in the optimization problem for achieving the optimal angle in the multi-level inverter, a recent developed meta-heuristic method known as Salp Swarm Algorithm (SSA) is presented. But what about the technology? Unanimous AI has developed a sophisticated distributed architecture that enables groups of people, from locations all around the world, to log into our Swarm® platform and participate as part of a real-time closed-loop intelligence moderated by AI algorithms. Salp swarm algorithm (SSA) is a recently created bio-inspired optimization algorithm presented in 2017 which is based on the swarming mechanism of salps. In PSO, the swarm changes its direction during its movement and therefore there are velocity update and position update. 2 The convergence curve of the functions using the SS A and the SSAPSO algorithms. Read "Improved particle swarm algorithm for hydrological parameter optimization, Applied Mathematics and Computation" on DeepDyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips. Hebei University of Engineering, Handan 056038, China ** College of Water Resource & Hydropower, Chengdu 610065, China. The sectio nis divided into ve subsections, namely, a general description of BSO, stochastic ada ptive step. An improved cockroach swarm optimization (ICSO) is proposed in this paper. 4021 martensitic stainless steel has been obtained and then used as one of the objective functions in the Multi-objective Improved Self- Adaptive Particle Swarm Optimization (MISAPSO) algorithm. ZHIMIN WANG et al: AN IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM USED FOR DOI 10. Generally, the biological research about Salps is still in its early stages because their living environments are hardly accessible, and it is very difficult to keep them in laboratory environment. Photo by University of Oklahoma. Salps are part of Salpidae family with the limpidcylinder-design body, they look jellyfishes in texture and like movement. In combination with an improved particle swarm optimization method, which improves the efficiency and convergence rate by introducing a new parameter called velocity acceptability probability, this scheme optimizes the wavelengths and power levels for the pumps quickly and accurately. According to the formula (3), the position of each dimension of the current particle is mapped to the [0,1] interval : (4) where, the interval denotes the definition domain of the d-dimensional variable (). The bee swarming the reproductive process and their collective decision-making have inspired this algorithm; it has no algorithmic connection with the honey bee algorithm or the artificial bee colony algorithm. Used the particles algorithm combined with the membrane to form a community, particles use wheel-type structure to communicate the current best particle within the community. Furthermore, the performance analysis is. 2, wherein by halving the salp chain into leaders and followers, the global search capability of the proposed SSA-GWO algorithm is improved and the introduction of GWO algorithm in leader group enables the SSA-GWO to converge faster in the course of iteration. Salp Swarm Algorithm (SSA) is one of the most recently proposed algorithms driven by the simulation behavior of salps. The voltage profile of the networks after optimizing DG locations and sizes using GA-IPSO method were also found to be much improved with the lowest bus voltage improved to 1. If a position cannot be improved over a predefined number (called limit) of cycles, then the food source is abandoned. Addresses: School of Science, Hubei University for Nationalities, Enshi 445000, China. A user can select one of eight transfer functions for the binary conversion. Improved multi-objective particle swarm optimization algorithm[J]. 959pu for the IEEE 30-bus and 33-bus test systems respectively. Hence, the algorithm can computationally be inefficient as measured by the number of function evaluations (FEs) required [11]. In a multiobjective particle swarm optimization algorithm, selection of the global best particle for each particle of the population from a set of Pareto optimal solutions has a significant impact on the convergence and diversity of solutions, especially when optimizing problems with a large number of objectives. Abstract—An improved algorithm—multi-objective particle swarm optimization with swarm energy conservation (SEC-MOPSO) is proposed, which is aimed to solve the problem of convergence and distribution in multi-objective particle swarm optimization (MOPSO) algorithm. The learning factors and of the improved algorithm are obtained from the chaotic sequences generated by the classical Logistic map [28]. Using a Swarm of Drones to Research Hurricanes. Presented a new hybrid particle swarm algorithm based on P systems, through analyzing the working principle and improved strategy of the elementary particle swarm algorithm. Used the particles algorithm combined with the membrane to form a community, particles use wheel-type structure to communicate. Therefore, Improved Particle Swarm Optimization (IPSO) algorithm is proposed in the paper, nonlinear dynamic inertia weight coefficient is introduced in the operational process. Salp Swarm Algorithm: Salps belong to the family of Salpidae and have transparent barrel-shaped body. Convergence condition of PSO is obtained through solving and analyzing the differential equation. Improved salp swarm algorithm for feature selection 1. SciTech Connect. Used the particles algorithm combined with the membrane to form a community, particles use wheel-type structure to communicate the current best particle within the community. An improved particle swarm optimizer for mechanical design optimization problems. In order to overcome the premature property and improve the global optimization performance of PSO algorithm, this paper proposes an improved particle. 7 improved binary version of the artificial fish swarm algorithm (IbAFSA) for solving 8 the 0-1 MKP (1). Social spider optimization algorithm is a new meta-heuristic algorithm of the swarm intelligence field to global solution. According to the formula (3), the position of each dimension of the current particle is mapped to the [0,1] interval : (4) where, the interval denotes the definition domain of the d-dimensional variable (). Despite high performance of SSA, slow convergence speed and getting stuck in local optima are two disadvantages of SSA. In the end,section five draws the conclusions and future work. WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS Yuanhua Wang, Qiang Zhang, Dongsheng Zhou E-ISSN: 2224-3402 26 Volume 11, 2014. , all of which have certain defects in terms of stability and precision. An improved cockroach swarm optimization (ICSO) is proposed in this paper. The bee swarming the reproductive process and their collective decision-making have inspired this algorithm; it has no algorithmic connection with the honey bee algorithm or the artificial bee colony algorithm. 1–23, 2019. In this study, a new FS approach applies the native SSA in machine learning domain to select the optimal feature group on the basis of wrapper mode. Where is the fitness value of the solution in the swarm. visual and step, artificial fish swarm move faster toward the global optimum and will be more capable of passing the local optimums. SSA-FS has been compared with Particle Swarm Optimization and Differential Evolution performance with criteria of accuracy and runtime. A novel particle swarm and genetic algorithm hybrid method for improved heuristic optimization of diesel engine performance By Aaron Michael Bertram A thesis submitted to the graduate faculty in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Major: Mechanical Engineering Program of Study Committee:. The algorithm is an improvement of the original floorplanning algorithm based on Simulated Annealing (SA) algorithm to. Choosing to study in Australia will provide you with a unique perspective on the world that will prepare you for your role as a global citizen. An Improved Quantum-behaved Particle Swarm Optimization Algorithm Based on Chaos Theory Exerting to Particle Position Pan Dazhi1, He Min2, Chen Youjun3, Yang Shuang4 College of Mathematic and Information, China West Normal University, Nanchong 637009, China Abstract: In this paper, we propose an improved quantum-behaved particle swarm. Parameter expansion accelerates convergence of iterative sampling algorithms by increasing the parameter space. Data fusion algorithm of wireless sensor network based on Improved Particle Swarm Optimization BP neural network Aiming at the problem of slow convergence speed, sensitive to initial value and easy to fall into local optimal solution of traditional back propagation (BP) neural network in data fusion algorithm of wireless sensor network, this. Salp chain has the ability to move toward the global optimum that changes during the iterations, and finally reach the. As an example,the three dimensional slip rate of faults was calculated in the middle and eastern segments of Qilianshan fault with PSO algorithm and improved one and the results show that the consuming time with improved PSO is decreased by 36. Examples of swarm intelligence in natural systems include ant colonies, bird flocking, hawks hunting, animal herding, bacterial growth, fish schooling and microbial intelligence. Salp Swarm Algorithm (SSA) is a new metaheuristic algorithm that emulates the inbred behaviour of the Salp chain. Swarm Intelligence (SI), was inspired by the biological behaviour of animals, and is an innovative distributed intelligent paradigm for solving optimization problems [3]. The use of high resolution and multiple gear types allows improved quantification of the spatial extent, abundance and physical processes driving an usually dense salp swarm. The definitions of variables FC, EC, SC require an indexing expression in {. The TPSO algorithm is developed to take into account multiple objective functions using a Pareto-Based approach. An Improved Quantum-behaved Particle Swarm Optimization Algorithm Based on Chaos Theory www. Hebei University of Engineering, Handan 056038, China ** College of Water Resource & Hydropower, Chengdu 610065, China. Multi-Robot, Multi-Target Particle Swarm Optimization Search in Noisy Wireless Environments. Jun Wu, Ruijie Nan, and Lei Chen, "Improved salp swarm algorithm based on weight factor and adaptive mutation," Journal of Experimental & Theoretical Artificial Intelligence, pp. From water bottles and food containers to toys and tubing, many modern. A hybrid method of particle swarm optimization (PSO) and a genetic algorithm (GA) used to perform the genes selection. In this paper, a new dynamic distributed particle swarm optimization (D2PSO) algorithm is proposed for trajectory path planning of multiple robots in order to find collision-free optimal path for each robot in the environment. The improved systematic optimization method can be incorporated into a software for more efficient optimization. Addresses: School of Science, Hubei University for Nationalities, Enshi 445000, China. The algorithm was tested on a small set of 10 problems. In this paper, a new dynamic distributed particle swarm optimization (D2PSO) algorithm is proposed for trajectory path planning of multiple robots in order to find collision-free optimal path for each robot in the environment. Therefore, satisfactory result is hereby obtained. Used the particles algorithm combined with the membrane to form a community, particles use wheel-type structure to communicate the current best particle within the community. Salp swarm algorithm (SSA) The position of salps is defined in an d- dimensional search space where d is the number of variables of a given problem. 2, wherein by halving the salp chain into leaders and followers, the global search capability of the proposed SSA-GWO algorithm is improved and the introduction of GWO algorithm in leader group enables the SSA-GWO to converge faster in the course of iteration. A preliminary binary version of the artificial fish swarm algorithm 9 (bAFSA) has been presented in [43]. The algorithm is an improvement of the original algorithm based on simulated annealing algorithm to optimize the floorplans. The bee swarming the reproductive process and their collective decision-making have inspired this algorithm; it has no algorithmic connection with the honey bee algorithm or the artificial bee colony algorithm. The main aim of the proposed algorithm is to attain technical, economic and environmental benefits. net Liu Zhao. Loading Unsubscribe from MATLAB For Engineers?. of particle swarm optimization will be described and in section 4, we applied it to the two-stage hybrid flow shop problems. im facing a problem with the salp swarm algorithm the results that i requred from the code doesnt give me exact number the solution can be off by 0. The main inspiration of SSA and MSSA is the swarming behaviour of salps when navigating and foraging in oceans. A hybrid method of particle swarm optimization (PSO) and a genetic algorithm (GA) used to perform the genes selection. This paper proposes a novel bio-inspired optimization method named memetic salp swarm algorithm (MSSA). Email: [email protected] Y1 - 2015/10/1. Ewees 2,3 · Diego Oliva 4 · Mohamed Abd Elaziz 5 · Songfeng Lu 1,6. Abstract—An improved algorithm—multi-objective particle swarm optimization with swarm energy conservation (SEC-MOPSO) is proposed, which is aimed to solve the problem of convergence and distribution in multi-objective particle swarm optimization (MOPSO) algorithm. To solve these problems, this paper proposes an improved adaptive inertia weight particle swarm optimization (AIWPSO) algorithm. Its performance has been compared with existing standard optimization algorithms, namely particle swarm optimization, ant-lion optimization and salp swarm algorithm. Particularly, the outstanding. View at Publisher · View at Google Scholar. Jun Wu, Ruijie Nan, and Lei Chen, “Improved salp swarm algorithm based on weight factor and adaptive mutation,” Journal of Experimental & Theoretical Artificial Intelligence, pp. Mppt Algorithm In Matlab Code Download. To overcome the standard AFSA's slow convergence speed and limited optimizing accuracy problem, an improved AFSA is presented in this paper. Free Online Library: Multivariable generalized predictive control using an improved particle swarm optimization algorithm. Presented a new hybrid particle swarm algorithm based on P systems, through analyzing the working principle and improved strategy of the elementary particle swarm algorithm. The SCA works by using a local search approach to improve the performance of traditional SSA by avoiding trapping in a local optimal solution and by increasing convergence speed. BSSA is applied for feature selection. We proposed an improved algorithm and introduced it to optimize the design of truss structures after the establishment of the corresponding optimization model. Parameter expansion accelerates convergence of iterative sampling algorithms by increasing the parameter space. However, similar to most of the meta-heuristic algorithms, it suffered from stagnation in local optima and low convergence rate. maximum value with fish-swarm algorithm. Based on our analysis and understanding. Convergence condition of PSO is obtained through solving and analyzing the differential equation. I've been browsing through the web and was reading that this algorithm was very effective. All of Griffith Research Online. In this work, a chaotic version of Salp Swarm Algorithm (SSA) is proposed, which is considered one of the recent metaheuristic algorithms. Huwang proposed an improved algorithm, which adjusted individual optimal value and global optimal value and make particle converge to the new position. The use of high resolution and multiple gear types allows improved quantification of the spatial extent, abundance and physical processes driving an usually dense salp swarm. This paper proposes an adaptive cultural algorithm with improved quantum-behaved particle swarm optimization (ACA-IQPSO) to detect the underwater sonar image. Download Citation. The inspiration of this method is the swarming behavior of salps when foraging and navigating in oceans. Towards the weakness of BP neural network, an efficient PSC ship-selecting model combining improved PSO and SVM algorithm is developed in this paper. agprofessional. To solve these problems, this paper proposes an improved adaptive inertia weight particle swarm optimization (AIWPSO) algorithm. https://www. In this regard, researchers have worked to improve the problem computationally, creating efficient solutions that lead to better data analysis through the K-means Clustering algorithm. The Harris hawks optimization algorithm, salp swarm algorithm, grasshopper optimization algorithm and dragonfly algorithm for structural design optimization of vehicle components. Chaos, with the properties of ergodicity and stochasticity, is definitely a good candidate, but currently only the well-known logistic map is prevalently used. Read "Improved particle swarm algorithm for hydrological parameter optimization, Applied Mathematics and Computation" on DeepDyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips. Salp Swarm Algorithm: Salps belong to the family of Salpidae and have transparent barrel-shaped body. Generally, the biological research about Salps is still in its early stages because their living environments are hardly accessible, and it is very difficult to keep them in laboratory environment. Materials Testing: Vol. The AIWPSO algorithm includes two strategies: (1) An inertia weight adjustment method based on the optimal fitness value of individual particles is proposed, so that different particles have different inertia weights. Huwang proposed an improved algorithm, which adjusted individual optimal value and global optimal value and make particle converge to the new position. Presented a new hybrid particle swarm algorithm based on P systems, through analyzing the working principle and improved strategy of the elementary particle swarm algorithm. Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection @article{Sayed2018ChaoticDA, title={Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection}, author={Gehad Ismail Sayed and Alaa Tharwat and Aboul Ellah Hassanien}, journal={Applied Intelligence}, year={2018}, volume={49}, pages={188-205} }. algorithm (HS), sine-cosine algorithm (SCA) and improved particle swarm optimization (IPSO), the four optimization techniques are employed in this paper for tuning PID controller. An improved particle swarm optimization is proposed to overcome inherent tendency of local trappings in PSO. Professional Interests: Multi-objective optimization, Robust optimization, Swarm intelligence, Computational intelligence. (k-NN is used a base classifier). The hybridization between both approaches creates an algorithm called SSAPSO, in which the efficacy of the exploration and the exploitation steps is improved. MATLAB Central contributions by Seyedali Mirjalili. This article presents a hybrid optimization method for the FS problem; it combines the slap swarm algorithm (SSA) with the particle swarm optimization. Hence, the algorithm can computationally be inefficient as measured by the number of function evaluations (FEs) required [11]. Since James Kennedy (a social psychologist) and Russell C. WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS Yuanhua Wang, Qiang Zhang, Dongsheng Zhou E-ISSN: 2224-3402 26 Volume 11, 2014. The improved artificial fish swarm algorithm proposed above has been effective in improving the convergence and precision of the algorithm, but not taking into account the impact of the size of the artificial fish size on the algorithm and the problem of the global planning and local planning balance of the artificial fish swarm algorithm. In this regard, researchers have worked to improve the problem computationally, creating efficient solutions that lead to better data analysis through the K-means Clustering algorithm. Robots in the simulation environment complete the task that rescuing the victim with the algorithm in a disaster. The swarm in PSO contains a lot of candidate solutions,which are treated as birds. 3 Search for the Magic Formulas for Optimization. The results validate the analysis in Section 2. Salp chain has the ability to move toward the global optimum that changes during the iterations, and finally reach the. Chaos, with the properties of ergodicity and stochasticity, is definitely a good candidate, but currently only the well-known logistic map is prevalently used. (k-NN is used a base classifier). Design/methodology/approach - This paper introduces a hybrid approach based on a modified ring neighborhood with two new perturbation operators designed to keep diversity. Improved salp swarm algorithm based on particle swarm optimization for feature selection 1 3 Fig. In the population space, to improve. Salps compose a swarm in profound oceans; this swarm named salp chain. Salp swarm algorithm (SSA) is a recently created bio-inspired optimization algorithm presented in 2017 which is based on the swarming mechanism of salps. Abstract—In order to overcome the weakness that particle swarm optimization algorithm is likely to fall into local minimum when the complex optimization problems are solved, a new adaptive dynamic particle swarm optimization algorithm is proposed. Convergence condition of PSO is obtained through solving and analyzing the differential equation. In this paper an improved particle swarm optimization (IPSO) is used to train the functional link artificial neural network (FLANN) for classification and we name it ISO-FLANN. algorithm complexity, etc. effectiveness of the algorithm being verified through simulation. The original CPSO algorithm [1] suffers from major drawback—redundant encoding. speed of learning, simplicity of rules, visualizes the data and predictive accuracy. Source: SSA: Salp Swarm Algorithm – File Exchange – MATLAB Central Dr. size distribution of a salp swarm. The shape of a Salp is shown in Figure 1(a). Chaos, with the properties of ergodicity and stochasticity, is definitely a good candidate, but currently only the well-known logistic map is prevalently used. So, the energy efficiency is improved to extend the network lifetime. proposed model is tested and compared with several machine. However, like other swarm-based algorithms, it has insufficiencies of low convergence precision and slow convergence speed when dealing with high-dimensional complex optimisation problems. The expression for Prob should be 1/(1+ exp(-v[i,j,t]))-- exp is a function and does not need a ^ after it. As seen, the better the solution , the higher the probability of the food source selected. The feasibility of the improved particle swarm optimization algorithm for multi-objective model is verified, which provides an important reference for multi-objective optimization of independent micro-grid. First, we observe the comparison between the traditional fish-swarm algorithm and the improved fish-swarm algorithm in the iteration times, as shown in Table 1 and Figure 1. N2 - Particle swarm optimization (PSO) is a relatively new global optimization algorithm. λ= The acceleration coefficients and inertia weight correspond to the values obtained using Clerc’s constriction models [10]. 1 ISSN: 1473-804x online, 1473 -8031 print An Improved Particle Swarm Optimization Algorithm Used for Unmanned Aerial Vehicle Fixed-point Reconnaissance Route Planning Zhimin Wang 1, Xun Chen 1,2,*, Lisheng Xu 2. AU - Liu, Zhao. to propose an improved bacterial foraging optimizer to solve CNOPs, which takes MBFOA as the search algorithm and two types of swims are proposed to deal with the sensitivity to the stepsize value. Based on the analyzing inertia weight of the standard particle swarm optimization (PSO) algorithm, an improved PSO algorithm is presented. The Multi-Constrained Multicast Routing Improved by Hybrid Bacteria Foraging-Particle Swarm Optimization To solve multicast routing under multiple constraints, it is required to generate a multicast tree that ranges from a source to the destinations with minimum cost subject to several constraints. View at Publisher · View at Google Scholar. For the sake of simplicity, while describing the proposed binary AFSA. method is called Salp Swarm Algorithm (SSA), introduced by Mirjalili and et al. Their tissues are highly similar to jelly fishes. Introduction. Salp Swarm Algorithm (SSA) is a novel swarm intelligent algorithm with good performance. The algorithm is an improvement of the original floorplanning algorithm based on Simulated Annealing (SA) algorithm to. An improved particle swarm optimization is proposed to overcome inherent tendency of local trappings in PSO. The non-dominated solutions found by swarm are stored in an external archive and nearest neighbor density estimator method is used to select a leader for the individual particles in the swarm. Salps are part of Salpidae family with the limpidcylinder-design body, they look jellyfishes in texture and like movement. Population Diversity in Particle Swarm Optimization (DPSO) algorithm can effectively balance the “exploration” and “exploitation” ability of the PSO optimization algorithm and improve the Improved DPSO Algorithm with Dynamically Changing Inertia Weight | Springer for Research & Development. ** * School of Civil Engineering, Tianjin University, Tianjin 300072, China. Feature selection is playing a significant role in any classification task. Moreover, it can converge to the global optimum due to its adaptive mechanism. In this study, SSA is hybridised with a simulated annealing (SA). Gravitational Search Algorithm (BGSA) feature selection approach was proposed in [9]. particle swarm initialization will completely destroy the structure of the particle swarm, which will greatly slow down the rate of convergence of the algorithm. 4104-4109, 1997. All of Griffith Research Online. Improved Multi-objective Particle Swarm Optimization Algorithm for Synthesizing Conformal Arrays with Excitations Restricted: ZHAO Fei 1,2, CHAI Shunlian 2, YE Liangfeng 2, QI Huiying 2, MAO Junjie 2: 1. Mppt Algorithm In Matlab Code Download. algorithms Article An Improved Multiobjective Particle Swarm Optimization Based on Culture Algorithms Chunhua Jia * and Hong Zhu School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611371,. 72,and velocity clamping percentage 0. Most Cited Swarm and Evolutionary Computation Articles an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning. Downloadable! A novel methodology based on the recent metaheuristic optimization algorithm Salp Swarm Algorithm (SSA) for locating and optimal sizing of renewable distributed generators (RDGs) and shunt capacitor banks (SCBs) on radial distribution networks (RDNs) is proposed. The shape of a salp is shown below:. simulated annealing algorithm, the improved particle swarm optimization algorithm can avoid the local optimum and the deviation from the optimal solution. First, the Binary Salp Swarm Algorithm (BSSA) is obtained from the original Salp Swarm Algorithm (SSA) using the S-Shaped transfer function (Sigmoid function) and the binarization method. 7 improved binary version of the artificial fish swarm algorithm (IbAFSA) for solving 8 the 0-1 MKP (1). Naidua, , H. To facilitate understanding, an example is described in Figure 1. To verify the performance of the proposed algorithm, it is tested over two experimental series, in the first one, it is compared with other similar approaches using benchmark functions. benchmark engineering optim function genetic algorithm global optimization grey wolf optimizer heuristic metaheuristic optimality optimisation optimization optimum particle swarm op problem solving salp swarm algorithm ssa. This submission includes the source codes of the multi-objective version of the Salp Swarm Algorithm (SSA) called Multi-objective Salp Swarm Algorithm (MSSA). Salp Swarm Algorithm to Minimize Functions with Continuous Variables - Valdecy/Metaheuristic-Salp_Swarm_Algorithm. PSC Ship-selecting model based on improved particle swarm optimization and support vector machine algorithm. In the end, this paper verifies the proposed algorithm's feasibility through a real example. In this paper we present a general genetic algorithm to address a wide variety of sequencing and optimization problems including multiple machine scheduling, resource allocation, and the quadratic assignment problem. The proposed PSO-GA approach is compared with several learning algorithms. And selecting lower values for these parameters causes artificial fish to act better in local searching. Abstract: In this paper, a novel swarm intelligent algorithm is proposed, known as the fitness dependent optimizer (FDO). The feasibility of the improved particle swarm optimization algorithm for multi-objective model is verified, which provides an important reference for multi-objective optimization of independent micro-grid. Training Neural Networks Using Salp Swarm Algorithm for Patter Classification ICFNDS 2018, June 2018, Amman, Jordan vital role in the performance of SSA, since it is the only parameter that controls the balance between exploration and exploitation. Robots in the simulation environment complete the task that rescuing the victim with the algorithm in a disaster. Improved salp swarm optimization (I-SSO) algorithm The original SSO algorithm though solves different optimization problems efficiently but it is inefficient to solve multi-objective oriented problems due to following limitations. Ai-ming at the phenomenon that Salp Swarm Algorithm has slow convergence and low precision, an improved version of SSA algorithm based on. This work proposes two novel optimization algorithms called Salp Swarm Algorithm (SSA) and Multi-objective Salp Swarm Algorithm (MSSA) for solving optimization problems with single and multiple objectives. Firefly algorithm (FFA) , bacterial foraging optimization algorithm (BFOA) , ant colony optimization (ACO) , artificial bee colony (ABC) , and cuckoo search (CS) are some of the algorithms using swarm intelligence improved in the last decade. Used the particles algorithm combined with the membrane to form a community, particles use wheel-type structure to communicate. Debugging can be used, according to PCA's scale-invariant feature transform (SIFT) algorithm is the basis of signal processing based on MATLAB GUI interface design and study on properties of optical fiber transmission in a wireless communication system, ESPRIT algorithm for frequency estimation with interference signals. A node sensitivity-based guided search algorithm (GSA) is suggested to enhance overall performance of optimizing tool. Particle Swarm Optimization Algorithm Algorithm Outline. Bakarb a b ⇑ ⇑ Department ofElectrical Engineering,Engineering Summit, Faculty University Malaya, 50603 Kuala Lumpur, Malaysia. SciTech Connect. Improved chicken swarm optimization Abstract: Considering the problem that the original chicken swarm optimization algorithm is easy to fall into local optimum because of premature convergence for high-dimensional complex problems, an improved chicken swarm optimization was proposed. 2 The convergence curve of the functions using the SS A and the SSAPSO algorithms. Particle Swarm Optimization: Algorithm for an improved computational performance to solving Economic Dispatch Problems Rakesh Sharma, Bharat Bhushan Jain, Ravinder Singh Maan, Vivek Prakash Department of Electrical Engineering Arya institute of engineering & technology, Jaipur. (2017) Enhancing robustness of the inverted PBI scalarizing method in MOEA/D. three describethe improved quantum particle s swarm algorithm. MATLAB software emulation test: the improved foraging behavior of artificial fish-swarm algorithm to improve the rapid convergence of the algorithm and stability, improve fish swarm algorithm to the adaptability of the robot global path planning. It can be clearly seen that the improved fish-swarm algorithm has fewer iterations, that is, it does not fall into the local minimum and can not find the global optimum. The aim of this paper is to come out with an Improved Particle Swarm Optimization (IPSO) algorithm for. Presented a new hybrid particle swarm algorithm based on P systems, through analyzing the working principle and improved strategy of the elementary particle swarm algorithm. An Improved Threshold Selection Algorithm Based on Particle Swarm Optimization for Image Segmentation Kaiping Wei1, Tao Zhang2, Xianjun Shen2, Jingnan Liu1 1 National Engineering Research Center of GPS; Wuhan University,. When Microgrid is under the grid connected operation, this paper first sets the interactive transmission power between direct transmission line of. So, the energy efficiency is improved to extend the network lifetime.