An Improved Backtracking Search Algorithm for Constrained Optimization Problems

摘要

Backtracking search algorithm is a novel population-based stochastic technique. This paper proposes an improved backtracking search algorithm for constrained optimization problems. The proposed algorithm is combined with differential evolution algorithm and the breeder genetic algorithm mutation operator. The differential evolution algorithm is used to accelerate convergence at later iteration process, and the breeder genetic algorithm mutation operator is employed for the algorithm to improve the population diversity. Using the superiority of feasible point scheme and the parameter free penalty scheme to handle constrains, the improved algorithm is tested on 13 well-known benchmark problems. The results show the improved backtracking search algorithm is effective and competitive for constrained optimization problems.