CenPSO: A Novel Center-based Particle Swarm Optimization Algorithm for Large-scale Optimization

被引：15

作者：

Mousavirad, Seyed Jalaleddin ^{[1
]}

Rahnamayan, Shahryar ^{[2
]}

机构：

[1] Sabzevar Univ New Technol, Fac Engn, Sabzevar, Iran

[2] Ontario Tech Univ, Dept Elect Comp & Software Engn, Nat Inspired Computat Intelligence NICI Lab, Oshawa, ON, Canada

来源：

2020 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | 2020年

关键词：

Particle swarm optimization; Center-based sampling; Optimization; Velocity; LSGO; Center-based PSO; DIFFERENTIAL EVOLUTION;

D O I：

10.1109/smc42975.2020.9283143

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Particle swarm optimization (PSO) has demonstrated a promising performance for solving challenging optimization problems, but its performance in solving large-scale optimization problems (LSGO) has drastically decreased. In the canonical PSO, velocity has a significant effect on the performance of PSO, which is updated based on cognitive and social factors. It can help particles to share information effectively. In this paper, a center-based velocity is proposed in which a new component, named opening "center of gravity factor", is added to velocity update rule to propose the center-based PSO (CenPSO). Center of gravity factor benefits from center-based sampling strategy, a new direction in population-based metaheuristics, especially to tackle LSGOs. The proposed method is evaluated on two benchmark functions, namely, CEC2010 and CEC2017, with dimensions 100 and 1000. The experimental results verify that CenPSO is significantly better than PSO over the majority of benchmark functions.

引用

页码：2066 / 2071

页数：6

共 35 条

[11] Liu WM, 2019, IEEE C EVOL COMPUTAT, P318, DOI [10.1109/CEC.2019.8790053, 10.1109/cec.2019.8790053]
[12] Mahdavi S, 2016, IEEE C EVOL COMPUTAT, P3557, DOI 10.1109/CEC.2016.7744240
[13] Metaheuristics in large-scale global continues optimization: A survey
Mandavi, Sedigheh
Shiri, Mohammad Ebrahim
Rahnamayan, Shahryar
[J]. INFORMATION SCIENCES, 2015, 295 : 407 - 428
[14] Mousavirad Seyed Jalaleddin, 2019, 2019 14th International Conference on Computer Science & Education (ICCSE), P929, DOI 10.1109/ICCSE.2019.8845065
[15] Mousavirad Seyed Jalaleddin, 2020, GECCO'20. Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, P1608, DOI 10.1145/3377929.3398143
[16] Mousavirad Seyed Jalaleddin, 2020, GECCO'20. Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, P1402, DOI 10.1145/3377929.3398144
[17] Automatic clustering using a local search-based human mental search algorithm for image segmentation
Mousavirad, Seyed Jalaleddin
Ebrahimpour-Komleh, Hossein
Schaefer, Gerald
[J]. APPLIED SOFT COMPUTING, 2020, 96
[18] Mousavirad SJ, 2019, IEEE C EVOL COMPUTAT, P2394, DOI [10.1109/CEC.2019.8790273, 10.1109/cec.2019.8790273]
[19] Human mental search: a new population-based metaheuristic optimization algorithm
Mousavirad, Seyed Jalaleddin
Ebrahimpour-Komleh, Hossein
[J]. APPLIED INTELLIGENCE, 2017, 47 (03) : 850 - 887
[20] Multilevel image thresholding using entropy of histogram and recently developed population-based metaheuristic algorithms
Mousavirad S.J.
Ebrahimpour-Komleh H.
[J]. Evolutionary Intelligence, 2017, 10 (1-2) : 45 - 75

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