吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (02): 397-403.

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Constraint multi-objective evolutionary algorithm based on artificial bee colony algorithm

BI Xiao-jun, WANG Yan-jiao   

  1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
  • Received:2012-02-27 Online:2013-03-01 Published:2013-03-01

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Abstract: To improve the convergence and diversity of constraint multi-objective evolutionary algorithms, a novel constraintd multi-objective evolutionary algorithm, named CMABC, based on Artificial Bee Colony (ABC) algorithm is proposed. CMABC treats constraint conditions using external population storing feasible and unfeasible solutions. In addition, according to the feature of constraint multi-objective optimal problems, the update method of external and iterative population, and the ABC algorithm are improved. Experiment results show that the CMABC outperforms the state-of-the-art Multi-Objective Artificial Bee Colony (MOABC) algorithm and Hybrid Particle Swarm Optimization (HPSO) algorithm in terms of convergence and diversity matrics, which proves the superiority of CMABC in solving constraint multi-objective optimal problems.

Key words: artificial intelligence, constraint multi-objective optimization, artificial bee colony algorithm, search strategy

CLC Number: 

  • TP18
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