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Multi-swarm optimization

About: Multi-swarm optimization is a research topic. Over the lifetime, 19162 publications have been published within this topic receiving 549725 citations.


Papers
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Journal ArticleDOI
Qie He1, Ling Wang1, Bo Liu1
TL;DR: Numerical simulation and the comparisons demonstrate the effectiveness and robustness of PSO and the effect of population size on the optimization performances is investigated as well.
Abstract: Parameter estimation for chaotic systems is an important issue in nonlinear science and has attracted increasing interests from various research fields, which could be essentially formulated as a multi-dimensional optimization problem. As a novel evolutionary computation technique, particle swarm optimization (PSO) has attracted much attention and wide applications, owing to its simple concept, easy implementation and quick convergence. However, to the best of our knowledge, there is no published work on PSO for estimating parameters of chaotic systems. In this paper, a PSO approach is applied to estimate the parameters of Lorenz system. Numerical simulation and the comparisons demonstrate the effectiveness and robustness of PSO. Moreover, the effect of population size on the optimization performances is investigated as well.

159 citations

Journal ArticleDOI
01 Mar 2019
TL;DR: The spiral movement of moths in Moth-Flame Optimization algorithm is introduced into the Water Cycle Algorithm to enhance its exploitation ability and to increase randomization in the new hybrid method, the streams are allowed to update their position using a random walk (Levy flight).
Abstract: This paper proposes a hybrid algorithm based on Water Cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems. The spiral movement of moths in Moth-Flame Optimization algorithm is introduced into the Water Cycle Algorithm to enhance its exploitation ability. In addition, to increase randomization in the new hybrid method, the streams in the Water Cycle Algorithm are allowed to update their position using a random walk (Levy flight). The random walk significantly improves the exploration ability of the Water Cycle Algorithm. The performance of the new hybrid Water Cycle–Moth-Flame Optimization algorithm (WCMFO) is investigated in 23 benchmark functions such as unimodal, multimodal and fixed-dimension multimodal benchmark functions. The results of the WCMFO are compared to the other state-of-the-art metaheuristic algorithms. The results show that the hybrid method is able to outperform the other state-of-the-art metaheuristic algorithms in majority of the benchmark functions. To evaluate the efficiency of the WCMFO in solving complex constrained engineering and real-life problems, three well-known structural engineering problems are solved using WCMFO and the results are compared with the ones of the other metaheuristics in the literature. The results of the simulations revealed that the WCMFO is able to provide very competitive and promising results comparing to the other hybrid and metaheuristic algorithms.

159 citations

Proceedings ArticleDOI
24 Apr 2003
TL;DR: New ways an individual can be influenced by its neighbors are introduced in particle swarm optimization, where a population of candidate problem solution vectors evolves "social" norms by being influenced by their topological neighbors.
Abstract: Particle swarm optimization is a novel algorithm where a population of candidate problem solution vectors evolves "social" norms by being influenced by their topological neighbors. Until now, an individual was influenced by its best performance acquired in the past and the best experience observed in its neighborhood. In this paper, we introduce new ways an individual can be influenced by its neighbors.

159 citations

Journal ArticleDOI
Wen-Bo Du, Gao Yang, Chen Liu, Zheng Zheng1, Zhen Wang2 
TL;DR: This work presents a particle swarm optimization with limited information, which provides each particle adequate information yet avoids the waste of information and outperforms both canonical PSO and fully informed PSO, especially for multimodal test functions.

158 citations

Journal ArticleDOI
Yuhua Li1, Zhi-Hui Zhan1, Shujin Lin1, Jun Zhang1, Xiaonan Luo1 
TL;DR: The competitive and cooperative PSO with ISM (CCPSO-ISM) is capable to prevent the premature convergence when solving global optimization problems and is validated under different test environments such as biased initialization, coordinate rotated and high dimensionality.

158 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023183
2022471
202110
20207
201926
2018171