A review of particle swarm optimization for multimodal problems

2018 ◽  
pp. 443-473
Author(s):  
Jian-Ping Li ◽  
Shanxin Yuan ◽  
Qing Sheng Li ◽  
Bo Li ◽  
Yim Fun Hu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multimodal Problems

Particle Swarm Optimization by Improved Chaos Immune Genetic Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 2621-2624
Author(s):  
Shao Song Wan ◽  
Jian Cao ◽  
Qun Song Zhu
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Single Chip ◽  
Immune Genetic Algorithm ◽  
Swarm Optimization ◽  
Local Optima ◽  
Chaos Sequence ◽  
Multimodal Problems

In order to resolve these problems, we put forward a new design of the intelligent lock which is mainly based on the technology of wireless sensor network. Particle swarm optimization (PSO) is a recently proposed intelligent algorithm which is motivated by swarm intelligence. PSO has been shown to perform well on many benchmark and real-world optimization problems; it easily falls into local optima when solving complex multimodal problems. To avoid the local optimization, the algorithm renews population and enhances the diversity of population by using density calculation of immune theory and adjusting new chaos sequence. The paper gives the circuit diagram of the hardware components based on single chip and describe how to design the software. The experimental results show that the immune genetic algorithm based on chaos theory can search the result of the optimization and evidently improve the convergent speed and astringency.

scholarly journals A Novel Crow Swarm Optimization Algorithm (CSO) Coupling Particle Swarm Optimization (PSO) and Crow Search Algorithm (CSA)

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ying-Hui Jia ◽  
Jun Qiu ◽  
Zhuang-Zhuang Ma ◽  
Fang-Fang Li
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Algorithm Design ◽  
Particle Swarm ◽  
Population Based ◽  
High Dimensional ◽  
Swarm Optimization ◽  
Optimization Efficiency ◽  
Multimodal Problems

The balance between exploitation and exploration essentially determines the performance of a population-based optimization algorithm, which is also a big challenge in algorithm design. Particle swarm optimization (PSO) has strong ability in exploitation, but is relatively weak in exploration, while crow search algorithm (CSA) is characterized by simplicity and more randomness. This study proposes a new crow swarm optimization algorithm coupling PSO and CSA, which provides the individuals the possibility of exploring the unknown regions under the guidance of another random individual. The proposed CSO algorithm is tested on several benchmark functions, including both unimodal and multimodal problems with different variable dimensions. The performance of the proposed CSO is evaluated by the optimization efficiency, the global search ability, and the robustness to parameter settings, all of which are improved to a great extent compared with either PSO and CSA, as the proposed CSO combines the advantages of PSO in exploitation and that of CSA in exploration, especially for complex high-dimensional problems.

scholarly journals Dynamic Neighborhood-Based Particle Swarm Optimization for Multimodal Problems

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xu-Tao Zhang ◽  
Biao Xu ◽  
Wei Zhang ◽  
Jun Zhang ◽  
Xin-fang Ji
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Space ◽  
Multimodal Optimization ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Benchmark Instances ◽  
Multimodal Problems

Various black-box optimization problems in real world can be classified as multimodal optimization problems. Neighborhood information plays an important role in improving the performance of an evolutionary algorithm when dealing with such problems. In view of this, we propose a particle swarm optimization algorithm based on dynamic neighborhood to solve the multimodal optimization problem. In this paper, a dynamic ε-neighborhood selection mechanism is first defined to balance the exploration and exploitation of the algorithm. Then, based on the information provided by the neighborhoods, four different particle position updating strategies are designed to further support the algorithm’s exploration and exploitation of the search space. Finally, the proposed algorithm is compared with 7 state-of-the-art multimodal algorithms on 8 benchmark instances. The experimental results reveal that the proposed algorithm is superior to the compared ones and is an effective method to tackle multimodal optimization problems.

Dynamic Neighborhood Particle Swarm Optimization Based on External Archive

2013 ◽  
Vol 333-335 ◽  
pp. 1374-1378
Author(s):  
Shu Xia Dong ◽  
Liang Tang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Optimal Position ◽  
Von Neumann ◽  
External Archive ◽  
Local Optimal Solution ◽  
Multimodal Problems

According to the defect of falling into a local optimum when dealing with multimodal problems with basic particle swarm optimization, a dynamic neighborhood particle swarm optimization with external archive (EA-DPSO) is proposed. The Ring topology, All topology and Von Neumann topology are adopted, and dynamically refining particle history optimal position, and then store them on the external archive. In terms of particles characteristics in the external archive, a kind of effective extract mechanism method is designed to choose learning sample. Three peak problems as simulation function are chosen and the results show that EA-DPSO can effectively jump out of local optimal solution. Therefore, it can be seen as an effective algorithm for solving multimodal problems.

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