TY - JOUR
T1 - Adaptive Particle Swarm Optimizer Combining Hierarchical Learning With Variable Population
AU - Liu, Huan
AU - Zhang, Junqi
AU - Zhou, Meng Chu
N1 - Funding Information:
This work was supported in part by the Innovation Program of Shanghai Municipal Education Commission under Grant 202101070007E00098; in part by the Shanghai Industrial Collaborative Science and Technology Innovation Project under Grant 2021-cyxt2-kj10; in part by the Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX0100; in part by the Fundamental Research Funds for the Central Universities; in part by the National Natural Science Foundation of China under Grant 51775385, Grant 61703279, Grant 62073244, and Grant 61876218; in part by the Shanghai Innovation Action Plan under Grant 20511100500; and in part by the Fundo para o Desenvolvimento das Ciencias e da Tecnologia (FDCT) under Grant 0047/2021/A1.
Publisher Copyright:
© 2013 IEEE.
PY - 2023/3/1
Y1 - 2023/3/1
N2 - Particle swarm optimizer (PSO) is an optimization technique that has been applied to solve various problems. In its variants, hierarchical learning and variable population are two commonly used learning strategies. The former is used to employ more potentially good particles to lead the swarm, which is very effective in the early search phase. However, in the later search phase, such mechanism impedes PSO's convergence. This work proposes an adaptive particle swarm optimizer combining hierarchical learning with variable population (PSO-HV), in which a heap-based hierarchy is first proposed to organize particles to hierarchically learn from the ones with better fitness in the same and upper levels. The levels of particles are determined and updated according to their current fitness in each iteration. Meanwhile, an adaptive variable population strategy is introduced and eliminates redundant particles based on the population's evolution state. In this way, the swarm is more explorative upon the hierarchical structure and improves its exploitation capability due to the variable population mechanism. Ten state-of-the-art PSO contenders, including two hierarchical ones and two variable population-based ones, are compared with the proposed method on 57 benchmark functions and the experimental results verify its effectiveness and efficiency.
AB - Particle swarm optimizer (PSO) is an optimization technique that has been applied to solve various problems. In its variants, hierarchical learning and variable population are two commonly used learning strategies. The former is used to employ more potentially good particles to lead the swarm, which is very effective in the early search phase. However, in the later search phase, such mechanism impedes PSO's convergence. This work proposes an adaptive particle swarm optimizer combining hierarchical learning with variable population (PSO-HV), in which a heap-based hierarchy is first proposed to organize particles to hierarchically learn from the ones with better fitness in the same and upper levels. The levels of particles are determined and updated according to their current fitness in each iteration. Meanwhile, an adaptive variable population strategy is introduced and eliminates redundant particles based on the population's evolution state. In this way, the swarm is more explorative upon the hierarchical structure and improves its exploitation capability due to the variable population mechanism. Ten state-of-the-art PSO contenders, including two hierarchical ones and two variable population-based ones, are compared with the proposed method on 57 benchmark functions and the experimental results verify its effectiveness and efficiency.
KW - Hierarchical learning
KW - particle swarm optimization (PSO)
KW - swarm intelligence
KW - variable population
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U2 - 10.1109/TSMC.2022.3199497
DO - 10.1109/TSMC.2022.3199497
M3 - Article
AN - SCOPUS:85137857035
SN - 2168-2216
VL - 53
SP - 1397
EP - 1407
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 3
ER -