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The particle swarm optimization (PSO) algorithm is a new population based search strat-
egy, which has exhibited good performance on well-known numerical test problems. How-
ever, on strongly multi-modal test problems the PSO tends to suffer premature
convergence. This is due to a decrease of diversity in search space that leads to a to-
tal implosion and ultimately fitness stagnation of the swarm. An accepted hypothesis
is that maintenance of high diversity is crucial for preventing premature convergence in
multi-modal optimization.-The particle swarm optimization (PSO) algorithm is a new population based search strat-
egy, which has exhibited good performance on well-known numerical test problems. How-
ever, on strongly multi-modal test problems the PSO tends to suffer premature
convergence. This is due to a decrease of diversity in search space that leads to a to-
tal implosion and ultimately fitness stagnation of the swarm. An accepted hypothesis
is that maintenance of high diversity is crucial for preventing premature convergence in
multi-modal optimization.
egy, which has exhibited good performance on well-known numerical test problems. How-
ever, on strongly multi-modal test problems the PSO tends to suffer premature
convergence. This is due to a decrease of diversity in search space that leads to a to-
tal implosion and ultimately fitness stagnation of the swarm. An accepted hypothesis
is that maintenance of high diversity is crucial for preventing premature convergence in
multi-modal optimization.-The particle swarm optimization (PSO) algorithm is a new population based search strat-
egy, which has exhibited good performance on well-known numerical test problems. How-
ever, on strongly multi-modal test problems the PSO tends to suffer premature
convergence. This is due to a decrease of diversity in search space that leads to a to-
tal implosion and ultimately fitness stagnation of the swarm. An accepted hypothesis
is that maintenance of high diversity is crucial for preventing premature convergence in
multi-modal optimization.
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下载文件列表
1-s2.0-S1877050915011965-main.pdf
A_Diversity-Guided_Particle_Swarm_Optimizer--_the_ARPSO.pdf