This paper presents a variant of particle swarm optimizers (PSOs) that we call the comprehensive learning particle swarm optimizer (CLPSO), which uses a novel learning strategy whereby all other particles' histori...
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This paper presents a variant of particle swarm optimizers (PSOs) that we call the comprehensive learning particle swarm optimizer (CLPSO), which uses a novel learning strategy whereby all other particles' historical best information is used to update a particle's velocity. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. Experiments were conducted (using codes-available from http://***/epnsugan) on multimodal test functions such as Rosenbrock, Griewank, Rastrigin, Ackley, and Schwefel and compositionfunctions both with and without coordinate rotation. The results demonstrate good performance of the CLPSO in solving multimodal problems when compared with eight other recent variants of the PSO.
An Adaptive Cooperative Particle Swarm Optimizer (ACPSO) is introduced in this paper, which facilitates cooperation technique through the usage of the Learning Automata (LA) algorithm. The cooperative strategy of ACPS...
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An Adaptive Cooperative Particle Swarm Optimizer (ACPSO) is introduced in this paper, which facilitates cooperation technique through the usage of the Learning Automata (LA) algorithm. The cooperative strategy of ACPSO optimizes the problem collaboratively and evaluates it in different contexts. In the ACPSO algorithm, a set of learning automata associated with dimensions of the problem are trying to find the correlated variables of the search space and optimize the problem intelligently. This collective behavior of ACPSO will fulfill the task of adaptive selection of swarm members. Simulations were conducted on four types of benchmark suites which contain three state-of-the-art numerical optimization benchmarkfunctions in addition to one new set of active coordinate rotated test functions. The results demonstrate the learning ability of ACPSO in finding correlated variables of the search space and also describe how efficiently it can optimize the coordinate rotated multimodal problems, compositionfunctions and high-dimensional multimodal problems.
Based on swarm intelligence principles and an enhanced mapping scheme, the extension of the original single-particle mean-variance mapping optimization (MVMO) to its swarm variant (MVMOS) is investigated in this paper...
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ISBN:
(纸本)9781467360043
Based on swarm intelligence principles and an enhanced mapping scheme, the extension of the original single-particle mean-variance mapping optimization (MVMO) to its swarm variant (MVMOS) is investigated in this paper. Numerical experiments and comparisons with other heuristic optimization methods, which were conducted on several composition test functions, demonstrate the feasibility and effectiveness of MVMOS when solving multimodal optimization problems. Sensitivity analysis of the algorithm parameters highlights its robust performance.
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