A hybridizing-enhanced differential evolution for optimization

Abstract

Differential evolution (DE) belongs to the most usable optimization algorithms, presented in many improved and modern versions in recent years. Generally, the low convergence rate is the main drawback of the DE algorithm. In this article, the gray wolf optimizer (GWO) is used to accelerate the convergence rate and the final optimal results of the DE algorithm. The new resulting algorithm is called Hunting Differential Evolution (HDE). The proposed HDE algorithm deploys the convergence speed of the GWO algorithm as well as the appropriate searching capability of the DE algorithm. Furthermore, by adjusting the crossover rate and mutation probability parameters, this algorithm can be adjusted to pay closer attention to the strengths of each of these two algorithms. The HDE/current-to-rand/1 performed the best on CEC-2019 functions compared to the other eight variants of HDE. HDE/current-to-best/1 is also chosen as having superior performance to other proposed HDE compared to seven improved algorithms on CEC-2014 functions, outperforming them in 15 test functions. Furthermore, jHDE performs well by improving in 17 functions, compared with jDE on these functions. The simulations indicate that the proposed HDE algorithm can provide reliable outcomes in finding the optimal solutions with a rapid convergence rate and avoiding the local minimum compared to the original DE algorithm.

A New Method for Solving the Flow Shop Scheduling Problem on Symmetric Networks Using a Hybrid Nature-Inspired Algorithm

“…In addition, utilizing the provided method in other distributed environments, such as the NSGA-III algorithm [73], distributionally robust optimization (DRO) [74], and Deep Belief Networks [75], is a very interesting line for future research. Furthermore, employing learning analytics and epistemic network analysis [76], grey wolf optimization [77], and Kalman filtering [78,79] for solving the FSSP can be investigated in the future. Finally, considering the dynamics approach [80,81] and human resources [82] in the symmetric networks can be investigated in future research.…”

Multi-strategy synthetized equilibrium optimizer and application

“…They include genetic algorithm (GA) ( Zhou et al, 2021 ), simulated annealing (SA) ( Kirkpatrick, Gelatt & Vecchi, 1983 ), particle swarm optimization (PSO) ( Chen & Lin, 2009 ), differential evolution (DE) ( Mohamed, Hadi & Jambi, 2019 ), Shuffled Frog Leaping algorithm (SFLA) ( Houssein et al, 2021 ), Artificial Bee Colony (ABC) ( Altay & Varol Altay, 2023 ), biogeography-based optimization (BBO) ( Simon, 2008 ), Cuckoo Search (CS) ( Gandomi, Yang & Alavi, 2013 ), Grey Wolf Optimizer (GWO) ( Mirjalili, Mirjalili & Lewis, 2014 ), etc . MAs are applied in many fields, such as feature selection ( Ghasemi et al, 2023b ), economic dispatch ( Ayedi, 2023 ) due to their simple structure, easy application, and no derivative information on OPs. However, more and more OPs need solving urgently as modern society evolves and the OPs are more and more complex ( Liu et al, 2021 ; Ghasemi et al, 2023c ).…”

“…A-EO solved the problem that search agents were randomly scattered to nonperformer search agents in EO. Biller et al (2016) proposed an improved EO through linear classification reduction diversity technique and local minima elimination method, while the variant of EO proposed by Ghasemi et al (2023a) , Ghasemi et al (2023b) and Ghasemi et al (2023c) used different probabilities to select equilibrium candidate solutions. They were applied to photovoltaic parameter estimation, and effectively improved the optimization accuracy and reliability.…”