Modified Grey Wolf Optimization to Solve Traveling Salesman Problem

Sopto, Dibbendu Singha; Ayon, Safial Islam; Akhand, M. A. H.; Siddique, Nazmul

doi:10.1109/ciet.2018.8660872

Cited by 17 publications

(18 citation statements)

References 8 publications

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“…Accordingly, we have modified three main steps of SHO algorithm which were mathematically modelled in Dhiman and Kumar (2017) and Dhiman and Kumar (2019). For such a task, the concepts of swap sequence and swap operator used in Sopto et al (2018) are employed for updating positions of spotted hyenas toward the position of the best individual of the group. Experiments carried on four benchmark datasets in TSPLib (Burma14, Bays29, Att48, and Berlin52) have indicated the efficiency and effectiveness of the modified version in finding feasible solutions to TSP.…”

Section: Related Workmentioning

confidence: 99%

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An SHO-based approach to timetable scheduling: a case study

Nguyen

2021

Journal of Information and Telecommunication

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University timetable scheduling, which is a typical problem that all universities around the world have to face every semester, is an NPhard problem. It is the task of allocating the right timeslots and classrooms for various courses by taking into account predefined constraints. In the current literature, many approaches have been proposed to find feasible timetables. Among others, swarm-based algorithms are promising candidates because of their effectiveness and flexibility. This paper investigates proposing an approach to university timetable scheduling using a recent novel swarm-based algorithm named Spotted Hyena Optimizer (SHO) which is inspired by the hunting behaviour of spotted hyenas. Then, a combination of SA and SHO algorithms also investigated to improve the overall performance of the proposed method. We also illustrate the proposed method on a real-world university timetabling problem in Vietnam. Experimental results have indicated the efficiency of the proposed method in comparison to other competitive metaheuristic algorithm such as PSO algorithm in finding feasible timetables.

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Section: Related Workmentioning

confidence: 99%

Section: Sho-based Algorithm To Timetable Schedulingmentioning

confidence: 99%

An SHO-based approach to timetable scheduling: a case study

Nguyen

2021

Journal of Information and Telecommunication

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“…Because of its advantages, GWO has been successfully adapted for a wide range of optimization problems. Flow Shop Scheduling (Komaki et al, 2015), Vehicle Path Planning (Zhang et al, 2016), Feature Selection (Al-Tashi et al, 2020, Multidimensional Knapsack (Luo et al, 2019), Numeric Optimization (Long et al , 2020), Traveling Salesman Problem (Sopto et al, 2018), Signal Processing (Rao et al , 2019), andText Classification (Chantar et al, 2020) are just a few to mention.…”

Section: An Overview Of the Basic Gwomentioning

confidence: 99%

On the binarization of Grey Wolf optimizer: a novel binary optimizer algorithm

Roayaei

2021

Soft Comput

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Grey Wolf Optimizer (GWO) is a populationbased evolutionary algorithm inspired by the hunting behaviour of grey wolves. GWO, in its basic form, is a real coded algorithm, therefore, it needs modifications to deal with binary optimization problems. In this paper, we review previous works on binarization of GWO, and classify them with respect to their encoding scheme, updating strategy, and transfer function. Then, we propose a novel binary GWO algorithm (named Set-GWO), which is based on set encoding and uses set operations in its updating strategy. Experimental results on different real-world combinatorial optimization problems and different datasets, show that SetGWO outperforms other existing binary GWO algorithms in terms of quality of solutions, running time, and scalability.

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“…The omega wolf position depends only on three wolves. A study done in 2019 had a mechanism for updating omega position [13]. Its limitation is that it was locally performed only when each solution was updated based on the neighborhood search without looking for a global region.…”

Section: Related Workmentioning

confidence: 99%

“…Different combinatorial optimization problems have employed GWO, which shows promising results in optimizing the objective function of the problem and exploring the search space using the best wolves in the population. However, Sopto (2019) showed a shortcoming in the mechanism of updating the positions of omega wolves, which was locally performed [13]. Therefore, the omega wolves would update their positions based only on local changes, similar to what local search algorithms do.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Grey Wolf Optimization Algorithm with Neighborhood Search Operations: An Application for Traveling Salesman Problem

2021

IJIES

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Modified Grey Wolf Optimization to Solve Traveling Salesman Problem

Cited by 17 publications

References 8 publications

An SHO-based approach to timetable scheduling: a case study

An SHO-based approach to timetable scheduling: a case study

On the binarization of Grey Wolf optimizer: a novel binary optimizer algorithm

Adaptive Grey Wolf Optimization Algorithm with Neighborhood Search Operations: An Application for Traveling Salesman Problem

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