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Many objective cooperative bat searching algorithm
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DOI：10.1016/j.asoc.2019.01.033.png)
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Abstract
En 中文
In this paper, a many objective cooperative bat searching algorithm (MOCBA) is proposed to solve many-objective optimization problems by using the balanceable fitness estimation method. Similar to the particle swarm optimization (PSO) algorithm and the evolutionary algorithm (EA), the cooperative bat searching algorithm (CBA) is a recently developed swarm intelligence optimization algorithm to efficiently solve single-objective optimization problems. With the balanceable fitness estimation method, the MOCBA balances the diversity ability and convergence ability of the algorithm during searching process. Moreover, the convergence issue for MOCBA is also studied. The results on convergence in mean and convergence in probability of the MCOBA are presented. Experimental results are provided to demonstrate the effectiveness of the proposed MOCBA by comparing with fourteen state-of-the-art many-objective optimization algorithms by solving benchmark functions: DTLZ1-DTLZ5 and WFG1-WFG9. By calculating the means, standard deviations and running the Wilcoxon rank sum tests and the Friedmans tests of 100 algorithm executions, the proposed MOCBA shows superior performance among all the fifteen algorithms. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Swarm intelligence
Many-objective optimization
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