Fitness Distance Balance Based LSHADE Algorithm for Energy Hub Economic Dispatch Problem

Abstract

This paper presents an improve version of Linear Population Size Reduction Success History Based Adaptive Differential Evolution (LSHADE) algorithm for solving global optimization problems. The aim of this study was diversify the search process and to improve the convergence performance of the LSHADE algorithm. In the proposed algorithm called FDB-LSHADE, Fitness Distance Balance (FDB) selection method was used to redesign the mutation operator of the LSHADE algorithms. In order to test and validate the performance of the proposed FDB-LSHADE algorithms, a comprehensive experimental study was carried out. For this purpose, it was tested on the CEC14 and CEC17 benchmark problems, consisting of different problem types and dimensions. The results of the FDB-LSHADE were compared to the performance of 8 other up-to-date and highly preferred metaheuristic search (MHS) algorithms. In addition, the proposed algorithm was applied to solve single- and multi-objective Energy Hub Economic Dispatch (EHED) problems, which were a non-convex, a nonlinear, and high dimensional problems. In order to evaluate the performance of the proposed algorithm, two non-parametric statistical methods, which are Wilcoxon and Friedman tests, were used. The simulation results of the developed algorithm were compared to previously proposed algorithms available in the literature and the results of the MHS algorithms. The results showed that the FDB-LSHADE was a superior performance compared to other MHS algorithms for solving both benchmark problems and EHED problems.