A hybridizing-enhanced differential evolution for optimization

Ghasemi, Maryam; Zare, Mohsen; Trojovský, Pavel; Zahedibialvaei, Amir; Trojovská, Eva

doi:10.7717/peerj-cs.1420

Cited by 6 publications

(3 citation statements)

References 64 publications

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“…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.…”

Section: Discussionmentioning

confidence: 99%

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

Baroud,

Eghtesad,

Ahmed Mahdi

et al. 2023

Symmetry

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Recently, symmetric networks have received much attention in various applications. They are a single route for incoming and outgoing network traffic. In symmetric networks, one of the fundamental categories of wide-ranging scheduling problems with several practical applications is the FSSP. Strictly speaking, a scheduling issue is found when assigning resources to the activities to maximize goals. The difficulty of finding solutions in polynomial time makes the flow shop scheduling problem (FSSP) NP-hard. Hence, the utilization of a hybrid optimization technique, a new approach to the flow shop scheduling issue, on symmetric networks is given in the current research. In order to address this issue, each party’s strengths are maximized and their weaknesses reduced, and this study integrates the Ant Colony Algorithm with Particle Swarm Optimization (ACO-PSO). Even though these methods have been employed before, their hybrid approach improves their resilience in a variety of sectors. The ACO-PSO is put to the test by contrasting it with innovative algorithms in the literature. The search space is first filled with a variety of solutions by the algorithm. Using pheromones in the mutual region, the ACO algorithm locally controls mobility. Moreover, the PSO-based random interaction among the solutions yields the global maximum. The PSO’s random interaction among the solutions typically results in the global maximum. The computational research demonstrates that the recommended ACO-PSO method outperforms the existing ones by a large margin. The Friedman test also shows that the average algorithm ranks for ACO and PSO are 1.79 and 2.08, respectively. The proposed method has an average rank of 2.13 as well. It indicates that the suggested algorithm’s effectiveness increased.

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Section: Discussionmentioning

confidence: 99%

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

Baroud,

Eghtesad,

Ahmed Mahdi

et al. 2023

Symmetry

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“…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 ).…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

Multi-strategy synthetized equilibrium optimizer and application

Sun,

Zhang,

Jin

et al. 2024

PeerJ Computer Science

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Background Improvement on the updating equation of an algorithm is among the most improving techniques. Due to the lack of search ability, high computational complexity and poor operability of equilibrium optimizer (EO) in solving complex optimization problems, an improved EO is proposed in this article, namely the multi-strategy on updating synthetized EO (MS-EO). Method Firstly, a simplified updating strategy is adopted in EO to improve operability and reduce computational complexity. Secondly, an information sharing strategy updates the concentrations in the early iterative stage using a dynamic tuning strategy in the simplified EO to form a simplified sharing EO (SS-EO) and enhance the exploration ability. Thirdly, a migration strategy and a golden section strategy are used for a golden particle updating to construct a Golden SS-EO (GS-EO) and improve the search ability. Finally, an elite learning strategy is implemented for the worst particle updating in the late stage to form MS-EO and strengthen the exploitation ability. The strategies are embedded into EO to balance between exploration and exploitation by giving full play to their respective advantages. Result and Finding Experimental results on the complex functions from CEC2013 and CEC2017 test sets demonstrate that MS-EO outperforms EO and quite a few state-of-the-art algorithms in search ability, running speed and operability. The experimental results of feature selection on several datasets show that MS-EO also provides more advantages.

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