Evolutionary dynamic multi-objective optimization algorithm based on Borda count method

Abstract

In this paper, a novel dynamic multi-objective optimization algorithm is introduced. The proposed method is composed of three parts: change detection, response to change, and optimization process. The first step is to use Sentry solutions to detect the environmental change and advises the algorithm when a change occurs. Then, to increase the diversity of solutions, the worst solutions should be elected and removed from population and re-initialized with new solutions. The main idea is to use Borda count method which is an optimal rank aggregation technique that ranks the solutions in order of preference and nominates the worst solutions that should be removed. The last step is optimization process which is done by multi-objective Cat swarm optimization (CSO) in this paper. CSO utilizes the population that has been improved from the previous step to estimate the best solutions and converges to optimal Pareto front. The performance of the proposed algorithm is tested on dynamic multi-objective benchmarks, and the results are compared with the ones achieved by previous algorithms. The simulation results indicate that the proposed algorithm can effectively track the time-varying optimal Pareto front and achieves competitive results in comparison with traditional approaches.