A novel evolution strategy for constrained optimization in engineering design

Abstract

Nature Inspired Algorithms (NIAs) are extensively employed to solve constrained optimization problems (COPs) in engineering design domain. Since the global optimum for almost all benchmark problems are already identified, improving the objective function value is not possible. However, an improvement in terms of number of objective function evaluations (FES) and reliability is still likely. This paper proposes an Evolution Strategy (ES) with a Covariance Matrix Adaptation (CMA)-like mutation operator and a ranking based constraint-handling method. The results indicate that the algorithm is able to find the global optimum in less FES and with high reliability when compared with the benchmarked methods.