Differential evolution using cooperative ranking-based mutation operators for constrained optimization

Abstract

Abstract Differential evolution (DE) is a widely used heuristic algorithm for numerical optimization over continuous space. As the core operator, the mutation operator has a great effect on DE's performance. In this paper, we propose a cooperative ranking-based mutation strategy (CRM) for DE when solving constrained optimization problems (COPs). In CRM, two different ranking criteria are adopted in a cooperative way to move the population towards a feasible global optimum with a faster convergence speed. The first criterion is objective function value-based criterion which is applied for feasible solutions to converge towards the global optimum. The second criterion is overall constraint violation-based criterion which is applied for infeasible solutions to search for feasible regions. The proposed CRM is integrated into the basic DE and two advanced DE variants. The comparison results showed that the DE variants with CRM have a faster convergence speed than the non-CRM-based variants in the majority of test problems. This approach is a promising strategy to improve the search ability of DE variants when solving COPs.