ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2016, Vol. 53 ›› Issue (6): 1410-1421.doi: 10.7544/issn1000-1239.2016.20150806

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Multi-Objective Constrained Differential Evolution Using Generalized Opposition-Based Learning

Wei Wenhong1, Wang Jiahai2, Tao Ming1, Yuan Huaqiang1   

  1. 1(Computer Institute, Dongguan University of Technology, Dongguan, Guangdong 523808);2(Department of Computer Science, Sun Yat-sen University, Guangzhou 510006)
  • Online:2016-06-01

Abstract: Differential evolution is a simple and efficient evolution algorithm to deal with nonlinear and complex optimization problems. Generalized opposition-based learning (GOBL) often guides population to evolve in the evolutionary computing. However, real-world problems are inherently constrained optimization problems often with multiple conflicting objectives. Hence, for resolving multi-objective constrained optimization problems, this paper proposes a constrained differential evolution algorithm using generalized opposition-based learning. In this algorithm, firstly, the transformed population is generated using general opposition-based learning in the population initialization. Secondly, the better individuals that are selected from the initial population and the transformed population using non-dominated sorting, crowding distance sorting and constraint handling techniques compose the new initial population. Lastly, based on a jumping probability, the transformed population is calculated again after generating new populations, and the fittest individuals that are selected from the union of the current population and the transformed population compose new population using the same techniques. The solution can be evolved toward Pareto front slowly according to the generalized opposition-based learning, so that the best solutions set can be found. The proposed algorithm is tested in multi-objective benchmark problems and compared with NSGA-Ⅱ, MOEA/D and other multi-objective evolution algorithms. The experimental results show that our algorithm is able to improve convergence speed and generate solutions which approximate to the best optimal Pareto front.

Key words: differential evolution, generalized opposition-based learning (GOBL), multi-objective optimization, constrained optimization, non-dominated sorting

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