An Improved Constrained Optimization Multi-Objective Genetic Algorithm and its Application in Engineering

Abstract:

An improved multi-objective genetic algorithm is proposed to solve constrained optimization problems. The constrained optimization problem is converted into a multi-objective optimization problem. In the evolution process, our algorithm is based on multi-objective technique, where the population is divided into dominated and non-dominated subpopulation. Arithmetic crossover operator is utilized for the randomly selected individuals from dominated and non-dominated subpopulation, respectively. The crossover operator can lead gradually the individuals to the extreme point and improve the local searching ability. Diversity mutation operator is introduced for non-dominated subpopulation. Through testing the performance of the proposed algorithm on 3 benchmark functions and 1 engineering optimization problems, and comparing with other meta-heuristics, the result of simulation shows that the proposed algorithm has great ability of global search. Keywords: multi-objective optimization;genetic algorithm;constrained optimization problem;engineering application

• Periodical: Advanced Materials Research (Volumes 962-965)
• Pages: 2903-2908
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.962-965.2903
• Online since: June 2014
• Authors: Yun Lian Liu, Wen Li, Tie Bin Wu, Yun Cheng, Tao Yun Zhou, Gao Feng Zhu
• Keywords: Constrained Optimization Problem, Engineering Application, Genetic Algorithm (GA), Multi-Objective Optimization (MOP)

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