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.

Economic dispatch (ED) is one of the most important problems in terms of energy planning, management, and operation in power systems. This study presents a snow ablation optimizer (SAO) algorithm developed with the fuzzy-based fitness–distance balance (FFDB) method for solving ED problems in small-, medium- and large-scale electric power systems and determining the optimal operating values of fossil fuel thermal generation units. The FFDB-based SAO algorithm (FFDBSAO) controls early convergence problems through balancing exploration–exploitation and improves the solving of high-dimensional optimization problems. In the light of extensive experimental studies conducted on CEC2020, CEC2022, and classical benchmark test functions, the FFDBSAO2 algorithm has shown superior performance against its competitors. Wilcoxon and Friedman’s statistical analysis results confirm the performance and efficiency of the algorithm. Moreover, the proposed algorithm significantly reduces total fuel cost by optimizing fossil fuel thermal generation units. According to the results, the scalability and robustness of the algorithm make it a valuable tool for solving large-scale optimization problems in the planning of electric power systems.

Nowadays, solar or photovoltaic energy is the most commonly used renewable energy resources in the world. Despite its advantages such as freely available, low maintenance cost, pollution-free, inexhaustible, and reliable, its low conversion efficiency is a major drawback. To increase the efficiency of the photovoltaic system, all photovoltaic modules in the array must be operated at maximum power point. Therefore, maximum power point tracking technique is used for predicting and tracking the maximum power point. In the literature, maximum power point tracking techniques are generally classified as soft computing and conventional. Soft computing techniques are more preferred from both of them, because they can accurately track maximum power point of photovoltaic systems. In this study, an extensive review of soft computing based maximum power point tracking techniques under partial shading conditions until today is presented. The techniques are compared from the point of photovoltaic array dependency, sensors required, tracking efficiency, tracking speed, algorithm complexity, and oscillation around maximum power point.