Backtracking Search Algorithm with three constraint handling methods for constrained optimization problems

A new evolutionary algorithm, Backtracking Search Algorithm (BSA), is applied to solve constrained optimization problems. Three constraint handling methods are combined with BSA for constrained optimization problems; namely feasibility and dominance (FAD) rules, epsilon-constrained method with fixed control way of epsilon value and a proposed epsilon-constrained method with self-adaptive control way of epsilon value. The proposed method controls epsilon value according to the properties of current population. This kind of epsilon value enables algorithm to sufficiently search boundaries between infeasible regions and feasible regions. It can avoid low search efficiency and premature convergence which happens in fixed control method and FAD rules. The comparison of the above three algorithms demonstrates BSA combined epsilon-constrained method with self-adaptive control way of epsilon value (BSA-SA epsilon) is the best one. The proposed BSA-SA epsilon also outperforms other five classic and the latest constrained optimization algorithms. Then, BSA-SA epsilon has been applied to four engineering optimization instances, and the comparison with other algorithms has proven its advantages. Finally, BSA-SA epsilon is used to solve the car side impact design optimization problem, which illustrates the wide application prospects of the proposed BSA-SA epsilon. (C) 2015 Elsevier Ltd. All rights reserved.