A multi-objective constrained optimization algorithm based on infeasible individual stochastic binary-modification

During solving the constrained multi-objective optimization problems with evolutionary algorithms, constraint handling is a principal problem. Analyzing the existing constraint handling methods, a novel constraint handling strategy based on infeasible individual stochastic binary-modification is proposed in the paper. Its key point lies in modifying randomly infeasible individual into feasible one according to predefined modification rate (R_m) during evolutionary optimization. Finally, the proposed strategy is applied to the constrained multi-objective optimization evolutionary algorithm, and then the algorithm is tested on 7 benchmark problems and the comparison between our strategy and Deb´s constrained-domination principle demonstrates that our strategy optimizes 30% faster than Deb´s in the circumstances to preserve equivalent distribution and convergence of the solutions found.