Multi-Objective Firefly Algorithm Based on Multiply Cooperative Strategies
XIE Cheng-wang1,2, ZHANG Fei-long2, LU Jian-bo1, XIAO Chi2, LONG Guang-lin1
1. School of Computer and Information Engineering, Nanning Normal University, Nanning, Guangxi 530299, China;
2. School of Software, East China Jiaotong University, Nanchang, Jiangxi 330013, China
Abstract：More and more complex multi-objective optimization problems have emerged in the real world,and the novel heuristic algorithms need to be developed to meet the challenge.A multi-objective firefly algorithm based on multiply cooperative strategies (MOFA-MCS) is proposed in the paper.MOFA-MCS uses the method of homogenization and randomization to generate the initial population,adopts the elite solutions in the external archive to lead the firefly to move,exerts Lévy flights to add random disturbance in the moving process,and finally,the ε-three-point shortest path strategy is also applied to maintain the diversity of the archive solutions.MOFA-MCS is compared with other six representative multi-objective evolutionary algorithms on 12 benchmark multi-objective test problems,and the experimental results show that MOFA-MCS has significant performance advantages in terms of convergence and diversity.
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2019, Vol. 47 
