Novel Constrained Optimization Evolutionary Algorthm Based on Self-adaptive Good Points Set
Novel constrained optimization evolutionary algorithm based on self-adaptive good points set(COEAAGP) is proposed to tackle constrained optimization problems(COPs) in this paper.A many points crossover operater is redesigned by using the good points set principle in number theory to adjust the crossover points locality and the amount of the offspring freely by the similarity degree of the population,so that it can produce a small but representative set of points as the potential offsping.A new fitness function,where feasible solutions and infeasible solutions are compared based on their constraint violation is proposed.The new approach is compared against other evolutionary optimization techniques in thirteen benchmark functions.The results obtained show that the new approach is a general,effective and robust method.