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Dıscrete socıal spıder algorıthm for the travelıng salesman problem

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Abstract

Heuristic algorithms are often used to find solutions to real complex world problems. These algorithms can provide solutions close to the global optimum at an acceptable time for optimization problems. Social Spider Algorithm (SSA) is one of the newly proposed heuristic algorithms and based on the behavior of the spider. Firstly it has been proposed to solve the continuous optimization problems. In this paper, SSA is rearranged to solve discrete optimization problems. Discrete Social Spider Algorithm (DSSA) is developed by adding explorer spiders and novice spiders in discrete search space. Thus, DSSA's exploration and exploitation capabilities are increased. The performance of the proposed DSSA is investigated on traveling salesman benchmark problems. The Traveling Salesman Problem (TSP) is one of the standard test problems used in the performance analysis of discrete optimization algorithms. DSSA has been tested on a low, middle, and large-scale thirty-eight TSP benchmark datasets. Also, DSSA is compared to eighteen well-known algorithms in the literature. Experimental results show that the performance of proposed DSSA is especially good for low and middle-scale TSP datasets. DSSA can be used as an alternative discrete algorithm for discrete optimization tasks.

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References

  • Akhand MAH, Akter S, Sazzadur Rahman S, Hafizur Rahman MM (2012) Particle Swarm Optimization with partial search to solve Traveling Salesman Problem. In: 2012 International conference on computer and communication engineering, ICCCE, pp 118–121

  • Akhand MAH, Akter S, Rashid MA, Yaakob SB (2015) Velocity tentative PSO: an optimal velocity implementation based particle swarm optimization to solve traveling salesman problem. IAENG Int J Comput Sci 42(3):1–12

    Google Scholar 

  • Aras N, Boyacı B, Koşucuoğlu D, Aksen D (2007) Karlı Gezgin Satıcı Problemi için Sezgisel Yöntemler, Industrial Engineering 27. National Congress, İzmir, Turkey (in the Turkish language)

  • Arora S (1998) Polynomial-time approximation schemes for Euclidean traveling salesman and other geometric problems. J ACM (JACM) 45(5):753–782

    Article  MathSciNet  Google Scholar 

  • Atashpaz-Gargari E, Lucas C, (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE congress on evolutionary computation, pp 4661–4667

  • Ateş E (2012) Karınca kolonisi optimizasyonu algoritmaları ile gezgin Satıcı probleminin çözümü ve 3 boyutlu benzetimi, License thesis, Ege Üniversity, engineering faculty, Department of Computer Engineering, Turkey (in the Turkish language)

  • Baş E, Ülker E (2020a) A binary social spider algorithm for continuous optimization task. Soft Comput. https://doi.org/10.1007/s00500-020-04718-w

    Article  Google Scholar 

  • Baş E, Ülker E (2020b) An efficient binary social spider algorithm for feature selection problem. Expert Syst Appl 146:113185

    Article  Google Scholar 

  • Bello R, Gomez Y, Nowe A, Garcia MM (2007) Two-step particle swarm optimization to solve the feature selection problem. In: Proceedings of ınternational conference on ıntelligent systems design and applications, pp 691–696

  • Brady RM (1985) Optimization strategies gleaned from biological evolution. Nature 317(6040):804–806

    Article  Google Scholar 

  • Cinar AC, Korkmaz S, Kiran MS (2019) A discrete tree-seed algorithm for solving symmetric traveling salesman problem. Eng Sci Technol Int J. https://doi.org/10.1016/j.jestch.2019.11.005

    Article  Google Scholar 

  • Cuevas E, Cienfuegos M, Zaldívar D, Pérez-Cisneros M (2013) A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst Appl 40:6374–6384

    Article  Google Scholar 

  • Cuevas E, Cienfuegos M (2014) A new algorithm inspired in the behavior of the social-spider for constrained optimization. Expert Syst Appl 4:412–425

    Article  Google Scholar 

  • Eiben AE, Smith J (2015) From evolutionary computation to the evolution of things. Nature 521(7553):476–482

    Article  Google Scholar 

  • El-Bages MS, Elsayed WT (2017) Social spider algorithm for solving the transmission expansion planning problem. Electr Power Syst Res 143:235–243

    Article  Google Scholar 

  • Elsayed WT, Hegazy YG, Bendary FM, El-Bages MS (2016) Modified social spider algorithm for solving the economic dispatch problem. Eng Sci Technol Int J 19:1672–1681

    Google Scholar 

  • Ezugwu AE, Adewumi AO (2017) The discrete symbiotic organisms search algorithm for traveling salesman problem. Expert Syst Appl 87:70–78

    Article  Google Scholar 

  • Faigl J (2018) GSOA: growing self-organizing array - unsupervised learning for the close-enough traveling salesman problem and other routing problems. Neurocomputing 312:120–134

    Article  Google Scholar 

  • Goldberg DE (1989) Genetic Algorithms in Search. Optimization, and machine learning. Addison-Wesley Publishing Company, Boston

    MATH  Google Scholar 

  • Gunduz M, Kiran MS, Ozceylan E (2014) A hierarchic approach based on swarm intelligence to solve traveling salesman problem. Turk J Electr Eng Comput Sci. https://doi.org/10.3906/elk-1210-147

    Article  Google Scholar 

  • Haskell BW, Toriello A, Poremba M, Epstein DJ (2013) A dynamic traveling salesman problem with stochastic, Arc Costs Department of Industrial and Systems Engineering University of Southern California Los Angeles, California

  • Helvig CS, Robins G, Zelikovsky A (1998) The moving-target traveling salesman problem volition Inc. J Algorithms, pp 153–174

  • Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  • Hore S, Chatterjee A, Dewanji A (2018) Improving variable neighborhood search to solve the traveling salesman problem. Appl Soft Comput 68:83–91

    Article  Google Scholar 

  • Jati GK, Suyanto (2011) Evolutionary discrete firefly algorithm for traveling salesman problem. In: Bouchachia A (ed) Adaptive and intelligent systems, vol 6943. ICAIS 2011. Lecture notes in computer science. Springer, Berlin, Heidelberg

    Google Scholar 

  • Khan I, Maiti MK (2019) A swap sequence-based artificial bee colony algorithm for traveling salesman problem. Swarm Evol Comput 44:428–438

    Article  Google Scholar 

  • Kara İ, Demir E(2006) Genelleştirilmiş gezgin satıcı poblemi için yeni tamsayılı karar modelleri, Industrial Engineering, 27. National Congress, Kocaeli University, Kocaeli Turkey (in the Turkish language)

  • Koç ÖN (2012) Zaman pencereli gezgin satıcı problemi için yeni karar modelleri, Başkent University, Science Institute, master's thesis, İstanbul, Turkey (in the Turkish thesis).

  • Kurt M, Semetay C (2001) Genetik algoritma ve uygulama alanları. Turk J Mühendis Makina 42(501):19–24 (in Turkish)

    Google Scholar 

  • Kuzu S, Önay O, Şen U, Tunçer M, Yıldırım FB, Keskintürk T (2014) Gezgin satıcı problemlerinin metasezgiseller ile çözümü. İstanb Univ J Bus Fac 43(1):1–27 (in the Turkish language)

    Google Scholar 

  • Liao Y, Yau D, Chen C (2012) Evolutionary algorithm to traveling salesman problems. Comput Math Appl 64:788–797

    Article  Google Scholar 

  • Li L, Cheng Y, Tan L, Niu B (2011) A discrete artificial bee colony algorithm for TSP problem. In: International conference on intelligent computing, Springer, Berlin

  • Mahi M, Baykan ÖK, Kodaz H (2015) A new hybrid method based on particle swarm optimization, ant colony optimization and 3-Opt algorithms for traveling salesman problem. Appl Soft Comput 30:484–490

    Article  Google Scholar 

  • Mattsson P (2010) The asymmetric traveling salesman problem. Uppsala University, Sweden

    Google Scholar 

  • Mousa A, Bentahar J (2016) An efficient QoS-aware web services selection using social spider algorithm. Im: The 13th ınternational conference on mobile systems and pervasive computing (MobiSPC 2016), Procedia Computer Science, Vol 94, pp 176–182

  • Nabiyev VV (2007) Yapay zeka-insan bilgisayar etkileşimi, Seçkin Publishing, Ankara, Turkey (in the Turkish language)

  • Osaba E, Yang X, Diaz F, Lopez-Garcia P, Carballedo R (2016) An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems. Eng Appl Artif Intell 48:59–71

    Article  Google Scholar 

  • Osaba E, Sera JD, Sadollah A, Bilbao MN, Camacho D (2018) A discrete water cycle algorithm for solving the symmetric and asymmetric traveling salesman problem. Appl Soft Comput 71:277–290

    Article  Google Scholar 

  • Ouaarab A, Ahiod B, Yang X-S (2014) The discrete cuckoo search algorithm for the traveling salesman problem. Neural Comput Appl 24(7–8):1659–1669

    Article  Google Scholar 

  • Pereira LAM, Rodrigues D, Ribeiro PB, Papa JP (2014) Social-spider optimization-based artificial neural networks training and its applications for Parkinson's disease identification. In: IEEE 27th ınternational symposium on computer-based medical systems, pp 14–17

  • Ravikumar C (1992) Parallel techniques for solving large scale traveling salesperson problems. Microprocess Microsyst 16(3):149–158

    Article  Google Scholar 

  • Reinelt G (2008) TSPLIB, Institute of information, University Heidelberg. https://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/.

  • Shukla UP, Nanda SJ (2016) Parallel social spider clustering algorithm for high dimensional datasets. Eng Appl Artif Intell 56:75–90

    Article  Google Scholar 

  • Shukla UP, Nanda SJ (2018) A binary social spider optimization algorithm for unsupervised band selection in compressed hyperspectral images. Expert Syst Appl 97:336–356

    Article  Google Scholar 

  • Sun S, Qi H, Sun J, Ren Y, Ruan L (2017) Estimation of thermophysical properties of phase change material by the hybrid SSO algorithms. Int J Therm Sci 120:121–135

    Article  Google Scholar 

  • Venkatesh P, Singh A (2019) An artificial bee colony algorithm with a variable degree of perturbation for the generalized covering traveling salesman problem. Appl Soft Comput Journal 78:481–495

    Article  Google Scholar 

  • Wang KP, Huang L, Zhou CG, Pang W (2003) Particle swarm optimization for traveling salesman problem. In: Proceedings of ınternational conference on machine learning and cybernetics, vol 3, pp 1583–1585

  • Yadlapalli S, Rathinam S, Darbha S (2012) A 3-Approximation algorithm for a two depot, heterogeneous traveling salesman problem. Optim Lett 6(1):141–152

    Article  MathSciNet  Google Scholar 

  • Yan X, Zhang C, Luo W, Li W, Chen W, Liu H (2012) Solve traveling salesman problem using particle swarm optimization algorithm. IJCSI Int J Comput Sci Issues 9(6):264

    Google Scholar 

  • Yip PP, Pao YH (1995) Combinatorial optimization with the use of guided evolutionary simulated annealing. IEEE Trans Neural Netw 6:290–295

    Article  Google Scholar 

  • Yu JJQ, Li VOK (2015) A social spider algorithm for global optimization. Appl Soft Comput 30:614–627

    Article  Google Scholar 

  • Yu JJQ, Li VOK (2016) A social spider algorithm for solving the non-convex economic load dispatch problem. Neurocomputing 171(C):955–965

    Article  Google Scholar 

  • Zhang H, Zhou J (2016) Dynamic multiscale region search algorithm using vitality selection for traveling salesman problem. Expert Syst Appl 60:81–95

    Article  Google Scholar 

  • Zhang B, Peng J (2019) Uncertain traveling salesman problem, https://orsc.edu.cn/online/110731.pdf

  • Zhong Y, Lin J, Wang L, Zhang H (2018) Discrete comprehensive learning particle swarm optimization algorithm with Metropolis acceptance criterion for traveling salesman problem. Swarm Evol Comput 42:77–88

    Article  Google Scholar 

  • Zhou Y, Luo Q, Chen H, He A, Wu J (2015) A discrete invasive weed optimization algorithm for solving traveling salesman problem. Neurocomputing 151:1227–1236

    Article  Google Scholar 

  • Zhou H, Song M, Pedrycz W (2018) A comparative study of improved GA and PSO in solving multiple traveling salesmen problem. Appl Soft Comput 64:564–580

    Article  Google Scholar 

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Authors and Affiliations

  1. Kulu Vocational School, Selçuk University, 42075, Konya, Turkey

    Emine BAŞ

  2. Department of Computer Engineering, Faculty of Engineering and Nature Sciences, Konya Technical University, 42075, Konya, Turkey

    Erkan ÜLKER

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  1. Emine BAŞ
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  2. Erkan ÜLKER
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Correspondence to Emine BAŞ.

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BAŞ, E., ÜLKER, E. Dıscrete socıal spıder algorıthm for the travelıng salesman problem. Artif Intell Rev 54, 1063–1085 (2021). https://doi.org/10.1007/s10462-020-09869-8

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