A feasible-ratio control technique for constrained optimization

摘要: Abstract In constrained optimization problems (COPs), a crucial issue is that most constraint-handling evolutionary algorithms (EAs) approach the optimum either mainly from feasible regions or infeasible regions. This may result in bias search of and solutions. To address this issue, we propose feasible-ratio control technique which controls ratio solutions population. By using technique, an EA can maintain balance Based on EA, named FRC-CEA. It consists two-stage optimization. first stage, enhanced dynamic multi-objective algorithm (DCMOEA) with adopted to handle constraints. second commonly used differential evolution (DE) speed up convergence. The performance proposed method evaluated compared six state-of-the-art two sets benchmark test suites. Experimental results suggest outperforms highly competitive against problems.