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Soft Computing

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09.05.2019 | Methodologies and Application

A novel hybrid multi-objective bacterial colony chemotaxis algorithm

verfasst von: Zhigang Lu, Lijun Geng, Guanghao Huo, Hao Zhao, Weitao Yao, Guoqiang Li, Xiaoqiang Guo, Jiangfeng Zhang

Erschienen in: Soft Computing | Ausgabe 3/2020

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Abstract

In this article, a novel hybrid multi-objective bacterial colony chemotaxis (HMOBCC) algorithm is proposed to solve multi-objective optimization problems. A mechanism of particle swarm optimization is introduced to multi-objective bacterial colony chemotaxis (MOBCC) algorithm to improve the performance of MOBCC algorithm. Also, three other techniques, including dynamic reverse learning operator, external archive multiplying operator and adaptive diversity maintenance operator, are further applied to improve the diversity and convergence of the algorithm. The proposed algorithm is validated using 12 benchmark problems, and three performance measures are implemented for 5 benchmark problems to compare its performance with existing popular algorithms such as MOBCC, multi-objective bacterial colony chemotaxis based on grid algorithm, non-dominated sorting genetic algorithm (NSGA-II) and multi-objective evolutionary algorithm based on decomposition. The results show that the proposed HMOBCC is very effective against existing algorithms.

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Titel: A novel hybrid multi-objective bacterial colony chemotaxis algorithm
verfasst von: Zhigang Lu
Lijun Geng
Guanghao Huo
Hao Zhao
Weitao Yao
Guoqiang Li
Xiaoqiang Guo
Jiangfeng Zhang
Publikationsdatum: 09.05.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 3/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-019-04034-y

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