Solving the IEEE CEC 2015 Dynamic Benchmark Problems Using Kalman Filter Based Dynamic Multiobjective Evolutionary Algorithm
Evolutionary algorithms have been extensively used to solve static and dynamic single objective optimization problems, and static multiobjective optimization problems. However, there has only been tepid interest to solve multiobjective optimization problems in dynamic environments. It is only in the past few years that evolutionary algorithms have been used to solve dynamic multiobjective optimization problems and comprehensive benchmark suites have been proposed for testing the performance of algorithms. Prediction based algorithms may be able to provide information about the location of the changed optima and thereby assisting the evolutionary algorithm in the non trivial task of tracking the changing Pareto Optimal Front or Set. Kalman filter is one of the widely used techniques in prediction scenarios for state estimation. A Dynamic Multi objective Evolutionary algorithm was proposed in which the Kalman Filter was applied to the whole population to direct the search for Pareto Optimal Solutions in the decision space after a change in the problem has occurred. In this work, the Kalman Filter assisted Evolutionary Algorithm is tested on the IEEE CEC 2015 Benchmark problems set and the results are presented. It is observed that while the proposed algorithm performs well on some problems, more efficient strategies are required to supplement the algorithm in cases of high change severity, isolated and deceptive fronts.