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Improved social spider algorithm for large scale optimization

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Abstract

Heuristic algorithms can give optimal solutions for low, middle, and large scale optimization problems in an acceptable time. The social spider algorithm (SSA) is one of the recent meta-heuristic algorithms that imitate the behaviors of the spider to perform global optimization. The original study of this algorithm was proposed to solve low scale continuous problems, and it is not be solved to middle and large scale continuous problems. In this paper, we have improved the SSA and have solved middle and large scale continuous problems, too. By adding two new techniques to the original SSA, the performance of the original SSA has been improved and it is named as an improved SSA (ISSA). In this paper, various unimodal and multimodal standard benchmark functions for low, middle, and large-scale optimization are studied for displaying the performance of ISSA. ISSA’s performance is also compared with the well-known and new evolutionary methods in the literature. Test results show that ISSA displays good performance and can be used as an alternative method for large scale optimization.

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References

  • Acılar AM (2013) Yapay Bağışıklık Algoritmaları Kullanılarak Bulanık Sistem Tasarımı, Konya, Turkey, Ph.D. thesis. (in Turkish)

  • Akay B (2013) A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Appl Soft Comput 13:3066–3091

    Google Scholar 

  • 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

    Google Scholar 

  • Blum C, Li X (2008) Swarm intelligence in optimization. In: Swarm intelligence. Springer, pp 43–85

  • 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

    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

    Google Scholar 

  • Dell RF, Ewing PL, Tarantino WJ (2008) Optimally stationing army forces. Interfaces 38(6):421–435

    Google Scholar 

  • Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. Comput Intell Mag IEEE 1(4):28–39

    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

    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 

  • Gandomi AH, Yang X-S, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35

    Google Scholar 

  • Garcia-Martinez C, Lozano M, Herrera F, Molina D, Sanchez AM (2008) Global and local real-coded genetic algorithms based on parent-centric crossover operators. Eur J Oper Res 185(3):1088–1113

    MATH  Google Scholar 

  • Garden RW, Engelbrecht AP (2014) Analysis and classification of optimization benchmark functions and benchmark suites. In: Proceedings of IEEE CEC 2014, pp 1641–1649

  • Goh CK, Lim D, Ma L, Ong YS, Dutta P (2011) A surrogate-assisted memetic co-evolutionary algorithm for expensive constrained optimization problems. In: IEEE congress on paper presented at the evolutionary computation (CEC)

  • Hansen N, Ostermeier A (2001) Completely derandomized self-adaptation in evolution strategies. Evol Comput 9(2):159–195

    Google Scholar 

  • Hussain K, MohdSalleh MN, Cheng S, Naseem R (2017) Common benchmark functions for metaheuristic evaluation: a review. Int J Inform Vis 1(4):2

    Google Scholar 

  • Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06. Erciyes University, Engineering Faculty, Computer Engineering Department

  • Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony. J Glob Optim 39(3):459–471

    MathSciNet  MATH  Google Scholar 

  • Kennedy J (2010) Particle swarm optimization. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, Boston, pp 760–766

    Google Scholar 

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Perth, WA, pp 1942–1948

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

    Google Scholar 

  • Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10:281–295

    Google Scholar 

  • Liao TW, Kuo RJ, Hu JTL (2012) Hybrid ant colony optimization algorithms for mixed discrete-continuous optimization problems. Appl Math Comput 219:3241–3252

    MathSciNet  MATH  Google Scholar 

  • Liu Y, Chen S, Guan B, Xu P (2019) Layout optimization of large-scale oil–gas gathering system based on combined optimization strategy. Neurocomputing 19:10

    Google Scholar 

  • Long W (2016) Grey wolf optimizer based on nonlinear adjustment control parameter. In: 4th International conference on sensors, mechatronics, and automation (ICSMA 2016), advances in intelligent systems research, p 136

  • Long Q, Wu C, Wang X, Wu Z (2017) A modified quasisecant method for global optimization. Appl Math Model 51:21–37

    MathSciNet  MATH  Google Scholar 

  • Long W, Jiao J, Liang X, Tang M (2018) Inspired grey wolf optimizer for solving large-scale function optimization problems. Appl Math Model 60:112–126

    MathSciNet  MATH  Google Scholar 

  • Long W, Wu T, Liang X, Xu S (2019) Solving high-dimensional global optimization problems using an improved sine cosine algorithm. Expert Syst Appl 123:108–126

    Google Scholar 

  • Masutti TAS, Castro LN (2016) TSPoptBees: a bee-inspired algorithm to solve the traveling salesman problem. In: Proceedings of the 2016 5th IIAI international congress on advanced applied informatics, IIAI-AAI), 2016, pp 593–598

  • Maucec MS, Brest J (2019) A review of the recent use of differential evolution for large-scale global optimization: an analysis of selected algorithms on the CEC 2013 LSGO benchmark suite. Swarm Evolut Comput 50:1–18

    Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69(3):46–61

    Google Scholar 

  • Mittal N, Singh U, Sohi BS (2016) Modified grey wolf optimizer for global engineering optimization. Appl Comput Intell Soft Comput 2016:1–16

    Google Scholar 

  • Mohapatra P, Das KN, Roy S (2017) A modified competitive swarm optimizer for large scale optimization problems. Appl Soft Comput 59:340–362

    Google Scholar 

  • Mousa A, Bentahar J (2016) An efficient QoS-aware web services selection using social spider algorithm. In: The 13th international conference on mobile systems and pervasive computing (MobiSPC 2016), procedia computer science, vol 94, pp 176–182

  • Nakib A, Ouchraa S, Shvai N, Souquet L, Talbi E-G (2017) Deterministic metaheuristic based on fractal decomposition for large-scale optimization. Appl Soft Comput 61:468–485

    Google Scholar 

  • Parpinelli RS, Lopes HS (2011) New inspirations in swarm intelligence: a survey. Int J Bio-inspired Comput 3(1):1–16

    Google Scholar 

  • Ray T, Yao X (2009) A cooperative coevolutionary algorithm with correlation-based adaptive variable partitioning. In: IEEE congress on paper presented at the evolutionary computation, CEC’09

  • Sayed E, Essam D, Sarker R, Elsayed S (2015) A decomposition-based evolutionary algorithm for large scale constrained problems. Inf Sci 316:457–486

    Google Scholar 

  • Shayanfar H, Gharehchopogh FH (2018) Farmland fertility: a new metaheuristic algorithm for solving continuous optimization problems. Appl Soft Comput 71:728–746

    Google Scholar 

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

    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

    Google Scholar 

  • Singh D, Agrawal S (2016) Self-organizing migrating algorithm with quadratic interpolation for solving large scale global optimization problems. Appl Soft Comput 38:1040–1048

    Google Scholar 

  • Sun G, Zhao R, Lan Y (2016) Joint operations algorithm for large-scale global optimization. Appl Soft Comput 38:1025–1039

    Google Scholar 

  • Sun Y, Wang X, Chen Y, Liu Z (2018) A modified whale optimization algorithm for large-scale global optimization problems. Expert Syst Appl 114:563–577

    Google Scholar 

  • Surjanovic S, Bingham D (2019) Virtual library of simulation experiments: test functions and datasets. http://www.sfu.ca/ssurjano

  • Tawhid MA, Dsouza KB (2018) Hybrid binary bat enhanced particle swarm optimization algorithm for solving feature selection problems. Appl Comput Inform

  • Trunfio A-G, Topa P, Was J (2016) A new algorithm for adapting the configuration of subcomponents in large-scale optimization with cooperative coevolution. Inf Sci 372:773–795

    Google Scholar 

  • Wang C-F, Song W-X (2019) A novel firefly algorithm based on gender difference and its convergence. Appl Soft Comput J 80:107–124

    Google Scholar 

  • Wang Y, Cai ZX, Zhang QF (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput 15(1):55–66

    Google Scholar 

  • Wang H, Wang W, Zhou X, Sun H, Zhao J, Yu X, Cui Z (2017) Firefly algorithm with neighborhood attraction. Inform Sci 382:374–381

    Google Scholar 

  • Wong LP, Low MYH, Chong CS (2008) A bee colony optimization algorithm for traveling salesman problem. In: Proceedings of the second asia international conference on modeling and simulation, pp 818–823

  • Yang XS (2009) Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms. Springer, pp 169–178

  • Yang XS (2010a) A new metaheuristic bat-inspired algorithm. In: Gonzalez JR, Pelta DA, Cruz C, Terrazas G, Krasnogor N (eds) Nature inspired cooperative strategies for optimization (NICSO 2010). Springer, Berlin, pp 65–74

    Google Scholar 

  • Yang X-S (2010b) Firefly algorithm, stochastic test functions and design optimization. Int J Bio-Inspired Comput 2(2):78–84

    Google Scholar 

  • Yang XS, Deb S (2009) Cuckoo search via Levy flights. In: Proceedings of the world congress on nature and biologically inspired computing (NaBIC 2009, India). IEEE Publications, New York, pp 210–214

  • Yildiz YE, Topal AO (2019) Large scale continuous global optimization based on micro differential evolution with local directional search. Inf Sci 477:533–544

    MathSciNet  MATH  Google Scholar 

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

    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

    Google Scholar 

Download references

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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. Improved social spider algorithm for large scale optimization. Artif Intell Rev 54, 3539–3574 (2021). https://doi.org/10.1007/s10462-020-09931-5

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