Self-organizing migrating algorithm using covariance matrix adaptation evolution strategy for dynamic constrained optimization

The dynamic constrained optimization problems can be a challenge for the algorithms. They must tackle global optimum detection, as well change of environment. Recently, novel test suite was introduced. Furthermore, three well-performed evolutionary algorithms were compared based on it. experimental results show that each algorithm performed best different type problem. objective our work to develop an reflecting requirements arising from and regarding provided by tested In this work, we present optimization. hybridizes self-organizing migrating covariance matrix adaptation evolution strategy with constraints handling approach. To avoid premature convergence, solutions representing feasible regions do not affect rest population. Two clustering methods, exclusion radius, quantum particles are used preserve population diversity. performance is evaluated recently published state-of-the-art presented outperformed these in most cases, which indicates efficiency utilized mechanisms.