Constrained Optimization by α Constrained Genetic Algorithm (αGA)

概要

論文の詳細を見る
In this study, the α constrained genetic algorithm (αGA) which solves constrained optimization problems is proposed. The αGA is the method which combines α constrained methods with GAs. In the α constrained methods, the satisfaction level of constraints to express how much each search point satisfies the constraints is introduced. The α level comparison which compares the search points based on the satisfaction level of constraints is also introduced. The α constrained methods can convert constrained problems to unconstrained problems using the α level comparison. In the αGA, the individuals who satisfy the constraints evolve to optimize the objective function and the individuals who don't satisfy the constraints evolve to satisfy the constraints, naturally. In this paper, the effectiveness of the αGA is shown by comparing the αGA with GENO-COP5.0 on the various types of test problems, such as a linear programming problem, nonlinear programming problems, and problems with non-convex constraints.
社団法人電子情報通信学会の論文
2003-04-01