Adaptive division of labor particle swarm optimization
作者：
Highlights：
• A new population division-based particle swarm optimization variant is proposed.
• Both swarm diversity and fitness are used to adaptively assign the search task of each particles.
• Two operators are applied on the best solution to further improve the algorithm’s convergence speed.
• A stagnation prevention module is also proposed to mitigate the premature convergence issue.
• The proposed algorithm outperforms its peers in term of searching accuracy and convergence speed.
摘要
•A new population division-based particle swarm optimization variant is proposed.•Both swarm diversity and fitness are used to adaptively assign the search task of each particles.•Two operators are applied on the best solution to further improve the algorithm’s convergence speed.•A stagnation prevention module is also proposed to mitigate the premature convergence issue.•The proposed algorithm outperforms its peers in term of searching accuracy and convergence speed.
论文关键词：Particle swarm optimization (PSO),Adaptive division of labor (ADOL),Adaptive division of labor particle swarm optimization (ADOLPSO),Convex operator,Reflectance operator
论文评审过程：Available online 6 April 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.03.025
