A Simple Cell Mapping and Genetic Algorithm Hybrid Method for Multi-Objective Optimization Problems

摘要：

　　In this paper,a hybrid multi-objective optimization (MOP) algorithm based on the genetic algorithm (GA) and the simple cell mapping (SCM) is proposed.The GA converges quickly toward a solution neighborhood but it takes a considerable amount of time to find the global solution.The SCM can find the global solution because it sweeps the whole space of interest,but the computational effort grows exponentially with the dimension.In this work,GA is used to initially find a rough solution for the MOP.Then,SCM takes over to find the nondominated solutions in each region returned by GA.It should be indicated that one point is enough for SCM to recover the rest of the solution in each zone.For comparison purpose,the hybrid algorithm,GA and SCM methods are utilized to solve some of benchmarking problems.With the Hausdorff distance as a performance metric,the results show that the hybrid approach outperforms other methods,although it does not guarantee finding the global solution.

关键词： Simple Cell Mapping Multi-Objective Optimization Hybrid method

作者: Yousef Naranjani Yousef Sardahi Jesús Fernández Oliver Schütze J.Q.Sun

作者单位: School of Engineering, University of California Merced, CA 95343, USA CINVESTAV-IPN, Depto de Computacion Mexico City, 07360 Mexico

会议类型: 国际会议

会议名称: Evolve International Conference-2014(2014年演变算法国际会议)

会议地点: 北京

会议语种:英文

页码: 1-5

在线出版日期: 2014-07-01（万方平台首次上网日期，不代表论文的发表时间）