Generating Epsilon-Efficient Solutions in Multiobjective Optimization by Genetic Algorithm

• 作者: El-Desouky Rahmo Marcin Studniarski
• 来源期刊: 应用数学(英文)
• 学科: 数学
• 关键词: VECTOR Optimization APPROXIMATE Solutions Genetic Algorithm STOPPING CRITERIA
• 年，卷（期）: 2017,（3）
• 页码范围: 395-409
• 页数: 15页
• 分类号: O1

摘要

We develop a new evolutionary method of generating epsilon-efficient solutions of a continuous multiobjective programming problem. This is achieved by discretizing the problem and then using a genetic algorithm with some derived probabilistic stopping criteria to obtain all minimal solutions for the discretized problem. We prove that these minimal solutions are the epsilon-optimal solutions to the original problem. We also present some computational examples illustrating the efficiency of our method.