Three-learning strategy particle swarm algorithm for global optimization problems

[Display omitted] •A med-point-example learning strategy is proposed to get stronger exploitation.•A random learning strategy is introduced to obtain stronger exploration.•The two learning strategies are hybridized to balance exploitation and exploration.•A worst-best example learning strategy is em...

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Bibliographic Details
Published in:Information sciences Vol. 593; pp. 289 - 313
Main Authors: Zhang, Xinming, Lin, Qiuying
Format: Journal Article
Language:English
Published: Elsevier Inc 01.05.2022
Subjects:
Global optimization problems
Learning strategy
Optimization algorithm
Particle swarm optimization
Swarm intelligence optimization algorithm
Optimization algorithm
Global optimization problems
Swarm intelligence optimization algorithm
Learning strategy
Particle swarm optimization
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary:[Display omitted] •A med-point-example learning strategy is proposed to get stronger exploitation.•A random learning strategy is introduced to obtain stronger exploration.•The two learning strategies are hybridized to balance exploitation and exploration.•A worst-best example learning strategy is embedded to maximize the performance.•The proposed algorithm has greater advantages on test sets and engineering problems. Social Learning Particle Swarm Optimization (SL-PSO) greatly improves the optimization performance of PSO. In solving complex optimization problems, however, it still has some deficiencies, such as poor search ability and low search efficiency. Hence, an improved SL-PSO, namely, Three-Learning Strategy PSO (TLS-PSO) is proposed in this paper. Firstly, a med-point-example learning strategy and a random learning strategy are proposed to replace the imitation component and social influence component of SL-PSO to enhance the exploitation and exploration, respectively. Secondly, the two learning strategies are combined cleverly into an updating equation to balance exploration and exploitation. Finally, a worst-best example learning strategy is merged skillfully to construct TLS-PSO with hybrid learning mechanism and further enhance the search ability. The experimental results on the complex functions from CEC2013 and CEC2017 test sets indicate that TLS-PSO has better performance compared with state-of-the-art PSO variants and other algorithms. For example, TLS-PSO has an advantage over SL-PSO on 50 of the 56 functions from CEC2013, its running time is less than SL-PSO’s and it has higher search efficiency. Simulation results on the 10 engineering problems also show that TLS-PSO outperforms 7 excellent algorithms, such as IUDE and iLSHADE∊. It is expected to solve practical problems better.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2022.01.075