文献类型：期刊文献

英文题名：A Novel Genetic Algorithm for Bin Packing Problem in jMetal

作者：Luo, Fei[1]; Scherson, Isaac D.[2]; Fuentes, Joel[3]

机构：[1] School of Information and Engineering, East China University of Science and Technology, Shanghai, China; [2] Donald Bren School of Information and Computer Sciences, University of California, Irvine, United States; [3] Department of Computer Science and Information Technologies, Universidad Del Bío-Bío, Chillán, Chile

年份：2017

起止页码：17

外文期刊名：Proceedings - 2017 IEEE 1st International Conference on Cognitive Computing, ICCC 2017

收录：EI(收录号：20174404324784)

语种：英文

外文关键词：Heuristic algorithms - Multiobjective optimization

摘要：The bin packing process can be modeled on optimization problems and it is widely studied due to its various applications. However, most implementation of the problem lacks of coordination in a unified optimization framework. Therefore, based on a general framework of multi-objective optimization with metaheuristics, jMetal, a novel genetic algorithm for bin packing problems is proposed in this paper. First, it extends the base solution of jMetal to problems with dynamic variables. Then according to the flow chart of genetic algorithms in jMetal, the operators for the heuristic algorithm are devised, e.g., the crossover and mutation operators. Finally the bin packing problem is implemented with the designed operators in this framework. Experiments are carried out, to verify the performance of the algorithm, obtaining the results that are comparable with the well-known heuristics. ? 2017 IEEE.