A robust and adaptive fuzzy logic based differential evolution algorithm using population diversity tuning for multi-objective optimization

This article presents an improved Multi-objective Differential Evolution based algorithm to solve multi-objective optimization problems. In the proposed algorithm named as Fuzzy Adaptive Multi-objective Differential Evolution with Diversity Control (FAMDE-DC), fuzzy system is used to control population diversity at decision variable space by self-adapting the crossover rate control parameter at various stages of evolution. Techniques such as non-dominated sorting, controlled elitism and dynamic crowding distance is used for selecting potential individuals. This control parameter adaptation and improved selection procedure results in controlling population diversity in decision space and identifying potential candidates in objective space, attaining true Pareto-optimal front with better convergence and diversity metrics. These properties make it robust and to be applied to varied problem domains without manual fine-tuning of parameters. The performance of FAMDE-DC algorithm is analysed using a set of benchmark test functions DTLZ and CEC2009 problems. Further the results are compared with other popular evolutionary based multi-objective algorithms. FAMDE-DC had a better Inverted Generational Distance (IGD) measure towards true Pareto-optimal front. The outcome of FAMDE-DC is also validated through nonparametric statistical tests Friedman and Wilcoxon signed rank test.

Automated summary

This article presents an improved Multi-objective Differential Evolution based algorithm named FAMDE-DC, which utilizes fuzzy system to control population diversity and identifies potential candidates in decision and objective spaces, achieving true Pareto-optimal solutions. The algorithm exhibits good performance and does not require manual parameter tuning.