A new constrained optimization evolutionary algorithm by using good point set

Solving constrained optimization problems (COPs) via evolutionary algorithms (EAs) has attracted much attention recently. A new constrained optimization evolutionary algorithm by using good point set (COEAGP) is presented in this paper. In the process of population evolution, multi-objective optimization techniques and good point set in number theory are integrated into our algorithm. The approach transforms COP into a bi-objective optimization problem firstly. Then the crossover operator is designed by using the principle of good point set The purpose of the new crossover is to enrich the exploration and exploitation abilities of the approach proposed. The new crossover operator can produce a small but representative set of points as the potential offspring. After that the BGA mutation operator is applied to potential offspring for enhancing the diversity of the potential offspring population. Furthermore, the update operator incorporates Pareto dominance and the tournament selection operator to choose the best individuals in the current offspring for the next generation. The new approach is tested on 8 well-known benchmark functions, and the empirical evidence suggests that it is robust and efficient when handling linear/nonlinear equality/inequality constraints and that COEAGP outperforms or performs similarly to the other techniques referred in this paper in terms of the quality of the resulting solutions.