Comparative analysis of Three Metaheuristics for Solving the Travelling Salesman Problem

Bouzidi, Safaa; Riffi, M. E.; Bouzidi, Abdelhamid

doi:10.14738/tmlai.54.3211

Cited by 5 publications

(6 citation statements)

References 17 publications

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“…The lognormal and power law distributionsand, indeed, the Pareto distribution found by Szary, Kello, and Dale (2015) are heavy-tailed and may promote search efficiency (e.g., Aks, Zelinsky, & Sprott, 2002;Bouzidi, Riffi, & Bouzidi, 2017;Humphries, et al, 2016;Humphries & Sims, 2014;Lin, Chou, Yang, & Tsai, 2012;Raichlen, et al, 2013;Ratcliff, Gomez, & McKoon, 2004;Reynolds, 2011;Reynolds, Smith, Reynolds, Carreck, & Osborne, 2007;Rhodes & Turvey, 2007;Spivey & Dale, 2004;Stephen & Mirman, 2010;Stephen, Mirman, Magnuson, & Dixon, 2009;Viswanathan, et al, 1999). Taken together, the measures of skew and goodness of fit mathematically denote what can be seen in a single participant's data in Fig.…”

Section: Resultsmentioning

confidence: 68%

“…Heavy-tailed distributions also promote efficiency in reaching the maximum number of targets in the shortest total distance (Humphries & Sims, 2014). Several possible solutions for a common mathematical issue that involves optimization of travel distance between several locationsthe traveling salesman problemhave relied on Lévy flights (Bouzidi, Riffi, & Bouzidi, 2017;Yang, 2010). The traveling salesman problem poses the situation of a salesman who needs to stop in numerous cities while minimizing travel distance, as travel time is time spent not making sales.…”

Section: Heavy-tailed Distributionsmentioning

confidence: 99%

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The trajectory of thought: Heavy-tailed distributions in memory foraging promote efficiency

et al. 2020

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Free-recall tasks suggest human memory foraging may follow a heavy-tailed distribution, such as a Lévy flight, patch foraging, or area-restricted searchwalk procedures that are common in other activities of cognitive agents, such as food foraging in both animals and humans. To date, research merely equates memory foraging with hunting in the physical world based on similarities in statistical structure. The current work supports that memory foraging follows a heavy-tailed distribution by using categories with quantitative distances between each item: countries, which have physical distances, and animals, from which cognitive distances can be derived using a multidimensional scaling (MDS) procedure. Likewise, inter-item lag times follow a heavy-tailed distribution. The current work also demonstrates that inter-item distances and times are positively correlated, suggesting the organization of items in memory may be akin to the organization of a physical landscape. Finally, both studies show that participants' original, heavy-tailed lists of countries and animal names produce shorter overall distances traveled than random selection. Human memory foraging follows the same pattern as foraging in the natural worldperhaps because exposure to ecological settings informs our inner cognitive experienceleading to a processing and retrieval time benefit.

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Section: Resultsmentioning

confidence: 68%

Section: Heavy-tailed Distributionsmentioning

confidence: 99%

The trajectory of thought: Heavy-tailed distributions in memory foraging promote efficiency

et al. 2020

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“…[77] Comparison between CSO, cuckoo search, and batinspired algorithm to solve TSP problem e benchmark instances are taken from STPLIB. e results show that CSO falls behind the other algorithms [78] Applied CSO and MCSO on workflow scheduling in cloud systems CSO performs better than PSO [79] Applied BCSO on workflow scheduling in cloud systems BCSO performs better than PSO and BPSO [80] Applied BCSO on SCP BCSO performs better than ABC [81] Applied BCSO on SCP BCSO performs better than binary teaching-learningbased optimization (BTLBO) [82,83] Used a CSO as a clustering mechanism in web services. CSO performs better than K-means [84] Applied hybrid CSO-GA-SA to find the overlapping community structures.…”

Section: Purpose Resultsmentioning

confidence: 99%

“…Bouzidi and Riffi applied CSO algorithm on the traveling salesman problem (TSP) and the quadratic assignment problem (QAP), which are two combinatorial optimization problems [77]. Bouzidi et al also made a comparative study between CSO algorithm, cuckoo search algorithm, and batinspired algorithm for addressing TSP [78]. In cloud computing, minimizing the total execution cost while allocating tasks to processing resources is a key problem.…”

Section: System Management and Combinatorial Optimizationmentioning

confidence: 99%

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Ahmed

Rashid

Saeed

2020

Computational Intelligence and Neuroscience

101

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This paper presents an in-depth survey and performance evaluation of cat swarm optimization (CSO) algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems, and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely, dragonfly algorithm (DA), butterfly optimization algorithm (BOA), and fitness dependent optimizer (FDO). These algorithms are then ranked according to Friedman test, and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

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“…Riffi applied CSO Algorithm on the Traveling Salesman Problem (TSP) and the Quadratic Assignment Problem (QAP), which are two combinatorial optimization problems [78]. Bouzidi et al also made a comparative study between CSO algorithm, cuckoo search algorithm, and bat-inspired algorithm for addressing TSP [79]. In cloud computing, minimizing the total execution cost while allocating tasks to processing resources is a key problem.…”

Section: Computer Vision: Facial Emotionmentioning

confidence: 99%

Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

Ahmed¹,

Rashid²,

Saeed³

2020

Preprint

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This paper presents an in-depth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely Dragonfly algorithm (DA), Butterfly optimization algorithm (BOA) and Fitness Dependent Optimizer (FDO). These algorithms are then ranked according to Friedman test and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

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Comparative analysis of Three Metaheuristics for Solving the Travelling Salesman Problem

Cited by 5 publications

References 17 publications

The trajectory of thought: Heavy-tailed distributions in memory foraging promote efficiency

The trajectory of thought: Heavy-tailed distributions in memory foraging promote efficiency

Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

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