Improving a multi-objective differential evolution optimizer using fuzzy adaptation and K-medoids clustering

The research presented in this article focuses on the development of a multi-objective optimization algorithm based on the differential evolution (DE) concept combined with Mamdani-type fuzzy logic controllers (FLCs) and $K$ -medoids clustering. The FLCs are used for adaptive control of the DE parameters; $K$ -medoids clustering enables the algorithm to perform a more guided search by evolving neighboring vectors, i.e., vectors that belong to the same cluster. A modified version of the $DE/best/1/bin$ algorithm is adopted as the core search component of the multi-objective optimizer. The FLCs utilize Pareto dominance and cluster-related information as input in order to adapt the algorithmic parameters dynamically. The proposed optimization algorithm is tested using a number of problems from the multi-objective optimization literature in order to investigate the effect of clustering and parameter adaptation on the algorithmic performance under various conditions, e.g., problems of high dimensionality, problems with non-convex Pareto fronts, and problems with discontinuous Pareto fronts. A detailed performance comparison between the proposed algorithm with state-of-the-art multi-objective optimizers is also presented.