A novel hybrid multi-objective bacterial colony chemotaxis algorithm
- Publisher:
- Springer (part of Springer Nature)
- Publication Type:
- Journal Article
- Citation:
- Soft Computing, 2020, 24, (3), pp. 2013-2032
- Issue Date:
- 2020
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Lu2020_Article_ANovelHybridMulti-objectiveBac.pdf | Published version | 1.66 MB |
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© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. 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. Graphical abstract: The graphical abstract of this study.[Figure not available: see fulltext.].
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