Constrained Optimization Solution Based on an Improved Genetic Algorithm

Abstract

A hybrid adaptive genetic algorithm is proposed for solving constrained optimization problems. The algorithm combines adaptive penalty method and smoothing technique in order to get no parameter tuning and easily escaping from the local optimal solutions. Meanwhile, local line search technique is introduced and a new crossover operator is designed for getting much faster convergence. The performance of the algorithm is tested on thirteen benchmark functions and the results indicate that the proposed algorithm is robust and effective.