A multi-swarm particle swarm optimization algorithm based on dynamical topology and purposeful detecting

•An entire population is divided into many parallel evolved sub-swarms in the early stage.•A dynamic sub-swarm number strategy (DNS) periodically reduces the number of sub-swarms aiming to balance the exploration and the exploitation ability.•A sub-swarm regrouping strategy (SRS) regrouping these su...

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Veröffentlicht in:Applied soft computing Jg. 67; S. 126 - 140
Hauptverfasser: Xia, Xuewen, Gui, Ling, Zhan, Zhi-Hui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.06.2018
Schlagworte:
Dynamic sub-swarm number
Local-searching
Particle swarm optimization
Purposeful detecting
Sub-swarm regrouping
Dynamic sub-swarm number
Local-searching
Sub-swarm regrouping
Particle swarm optimization
Purposeful detecting
ISSN:1568-4946, 1872-9681
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Zusammenfassung:•An entire population is divided into many parallel evolved sub-swarms in the early stage.•A dynamic sub-swarm number strategy (DNS) periodically reduces the number of sub-swarms aiming to balance the exploration and the exploitation ability.•A sub-swarm regrouping strategy (SRS) regrouping these sub-swarms based on the stagnancy information of the globally best position is adopted to enhance the exploitation ability.•A purposeful detecting strategy (PDS) relying on some historical information of the search process is selected to help the population to jump out of the current local optimum for better exploration ability.•The strategies proposed in this paper have general applicability. This paper proposes a multi-swarm particle swarm optimization (MSPSO) that consists of three novel strategies to balance the exploration and exploitation abilities. The new proposed MSPSO in this work is based on multiple swarms framework cooperating with the dynamic sub-swarm number strategy (DNS), sub-swarm regrouping strategy (SRS), and purposeful detecting strategy (PDS). Firstly, the DNS divides the entire population into many sub-swarms in the early stage and periodically reduces the number of sub-swarms (i.e., increase the size of each sub-swarm) along with the evolutionary process. This is helpful for balancing the exploration ability early and the exploitation ability late, respectively. Secondly, in each DNS period with special number of sub-swarms, the SRS is to regroup these sub-swarms based on the stagnancy information of the global best position. This is helpful for diffusing and sharing the search information among different sub-swarms to enhance the exploitation ability. Thirdly, the PDS is relying on some historical information of the search process to detect whether the population has been trapped into a potential local optimum, so as to help the population jump out of the current local optimum for better exploration ability. The comparisons among MSPSO and other 13 peer algorithms on the CEC2013 test suite and 4 real applications suggest that MSPSO is a very reliable and highly competitive optimization algorithm for solving different types of functions. Furthermore, the extensive experimental results illustrate the effectiveness and efficiency of the three proposed strategies used in MSPSO.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2018.02.042