TITLE:
Generating Epsilon-Efficient Solutions in Multiobjective Optimization by Genetic Algorithm
AUTHORS:
El-Desouky Rahmo, Marcin Studniarski
KEYWORDS:
Vector Optimization, Approximate Solutions, Genetic Algorithm, Stopping Criteria
JOURNAL NAME:
Applied Mathematics,
Vol.8 No.3,
March
30,
2017
ABSTRACT: 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.