Transformation operators based grey wolf optimizer for travelling salesman problem

Panwar, Karuna; Deep, Kusum

doi:10.1016/j.jocs.2021.101454

Cited by 23 publications

(8 citation statements)

References 42 publications

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“…It has a strong search ability, few control parameters, and is easy to implement. This metaheuristic algorithm has been successfully applied in parameter optimization [25], image classification [26,27], path identification [28], scheduling [29][30][31][32], and other domains. Therefore, the tugboat scheduling problem studied in this paper is solved using the GWO algorithm, and an encoding method based on random probability is used to represent individuals in the groups of wolves.…”

Section: Solution Methodsmentioning

confidence: 99%

Tugboat Scheduling with Multiple Berthing Bases under Uncertainty

Li,

Chen,

Lau

et al. 2023

JMSE

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This study proposes a novel fuzzy programming optimization model for tugboat scheduling, directly considering multiple berthing bases, time windows, and operational uncertainties. The uncertainties in the required number of tugboats, the earliest start time, the latest start time, the processing time, and the start and end locations of each task are directly captured in the proposed fuzzy optimization model. The objective of the presented formulation is to minimize the total cost of fuel and delays. According to the characteristics of the problem, a Grey Wolf Optimization algorithm based on random probability encoding and custom genetic operators is proposed. The proposed algorithm, LINGO, the canonical Grey Wolf Optimization algorithm, and particle swarm optimization were used to compare and analyze the results of several examples. The results validate the efficiency of the proposed algorithm against the alternative exact and metaheuristics methods. Moreover, the findings from the conducted sensitivity analysis show the applicability of the developed fuzzy programming model for different confidence interval levels.

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Section: Solution Methodsmentioning

confidence: 99%

Tugboat Scheduling with Multiple Berthing Bases under Uncertainty

Li,

Chen,

Lau

et al. 2023

JMSE

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“…Also, the discrete search space has been well-defined and the GWO operators have adjusted accordingly [75], [140], [164]. In dealing with permutation search space like the traveling salesman problem where the decision variables are not duplicated and not missed, the operator of GWO has been also modified to preserve the integrity of the solution during the inheritance process [302].…”

Section: Critical Analysis Of Grey Wolf Optimizer Theorymentioning

confidence: 99%

Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

et al. 2024

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The Grey Wolf Optimizer (GWO) has emerged as one of the most captivating swarm intelligence methods, drawing inspiration from the hunting behavior of wolf packs. GWO's appeal lies in its remarkable characteristics: it is parameter-free, derivative-free, conceptually simple, user-friendly, adaptable, flexible, and robust. Its efficacy has been demonstrated across a wide range of optimization problems in diverse domains, including engineering, bioinformatics, biomedical, scheduling and planning, and business. Given the substantial growth and effectiveness of GWO, it is essential to conduct a recent review to provide updated insights. This review delves into the GWO-related research conducted between 2019 and 2022, encompassing over 200 research articles. It explores the growth of GWO in terms of publications, citations, and the domains that leverage its potential. The review thoroughly examines the latest versions of GWO, categorizing them based on their contributions. Additionally, it highlights the primary applications of GWO, with computer science and engineering emerging as the dominant research domains. A critical analysis of the accomplishments and limitations of GWO is presented, offering valuable insights. Finally, the review concludes with a brief summary and outlines potential future developments in GWO theory and applications. Researchers seeking to employ GWO as a problem-solving tool will find this comprehensive review immensely beneficial in advancing their research endeavors.

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“…Sun et al [16] proposed an optimal control strategy for a permanent magnet synchronous hub motor drive based on the GWO and control method. Panwar et al [17] proposed a GWO method based on swap and symmetric exchange in order to obtain an efficient solution to permutation-coded traveling salesman problem. Ahmad et al [18] proposed a GWO-based gene ranking method to provide selectable genes for the hybrid filter-wrapper approach.…”

Section: Introductionmentioning

confidence: 99%

Gray Wolf Optimizer with Communication Strategy Based on DV-Hopfor Nodes Location ofWireless Sensor Networks

Yuan

Qing

et al. 2023

Preprint

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Node localization has been one of the critical and significant research problems in wireless sensornetworks (WSN). The DV-Hop method determines the location of an unknown node based onthe anchor node location and the multi-hop forwarding principle, but the method falls far shortof satisfactory localization accuracy. Therefore, an improved Gray Wolf Optimizer (GWO) withcommunication strategy (IGWO-CS) is proposed in this paper. A convergence function based onthe sine function is proposed to eliminate the limitation of the convergence factor 𝑎 on the searchability of the GWO algorithm. A communication strategy based on Lévy flight is designed, which canimprove the efficiency of information interaction between wolves 𝛼, 𝛽 and 𝛿 while also improving thesearch accuracy and convergence speed. Extensive experimental results on 23 well-known benchmarkfunctions and unknown nodes location in WSN demonstrate the effectiveness of the proposed IGWO-CS method and also show sufficient competitiveness compared with other state-of-the-art algorithms,while the proposed IGWO-CS algorithm can achieve satisfactory performance in a short time.

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Transformation operators based grey wolf optimizer for travelling salesman problem

Cited by 23 publications

References 42 publications

Tugboat Scheduling with Multiple Berthing Bases under Uncertainty

Tugboat Scheduling with Multiple Berthing Bases under Uncertainty

Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

Gray Wolf Optimizer with Communication Strategy Based on DV-Hopfor Nodes Location ofWireless Sensor Networks

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