Multi-Parent Crossover Evolutionary Algorithm for Constrained Optimization

A new approach(MMEA) is presented to handle constrained function optimization problems using evolutionary algorithms.It designs three novel multi-parent crossover operators which can speedup the constringency dramatically because of their strong direction.Meanwhile,the complementarity among these crossover operators can maintain population diversity,which makes MMEA more likely to find the global optimum than other evolutionary algorithms.The new approach is compared against other evolutionary optimization techniques in several benchmark functions.The results show that the new approach is a general,effective and robust method.Its performance outperforms some other techniques.