An Overview, Changes, and Uses of the Novel Social Spider Optimization Algorithm


  • B.M. Rajesh Assistant Professor, Department of Information Technology, Dr. NGP Arts and Science College, Coimbatore, Tamil Nadu, India
  • Aakash A. Student, Department of Information Technology, Dr. NGP Arts and Science College, Coimbatore, Tamil Nadu, India
  • Agustin Joseph I. Student, Department of Information Technology, Dr. NGP Arts and Science College, Coimbatore, Tamil Nadu, India


Swarm Intelligence, Social Spider Optimization, Bio-Inspired Algorithm


The rising complexity of real-world problems has prompted researchers and computer scientists to look for more effective ways to solve difficulties. Generally inspired by nature, bioinspired metaheuristics based on swarm intelligence algorithms and evolutionary computation have been widely employed to solve complicated, real-world optimization problems because of their adaptability to a broad variety of factors. The social spider optimization (SSO) approach is introduced in this article as a swarm-based algorithm, drawing inspiration from the cooperative behaviors exhibited by social spiders. Search agents in SSO describe a group of spiders that collectively move in accordance with a biological behavior in the colony. Many changes have been made to the SSO algorithm in the years since it was first introduced, improving its performance and enabling its use in other sectors. The developments and uses of the SSO are reviewed in this study.


Almufti SM. Using swarm intelligence for solving NP-hard problems. Acad J Nawroz Univ. 2017;6(3):46–50.

Almufti SM. Historical survey on metaheuristics algorithms. Int J Sci World. 2019;7(1):1.

Bonabeau E, Dorigo M, Theraulaz G. Swarm Intelligence: From Natural to Artificial Systems. Oxford, UK: Oxford University Press; 1999.

Jain M, Singh V, Rani A. A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evolutionary Comput. 2019;44:148–175.

Cuevas E, Cienfuegos M, Zaldívar D, Pérez-Cisneros M. A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst Appl. 2013;40(16):6374–6384.

Almufti S, Marqas R, Ashqi V. Taxonomy of bio-inspired optimization algorithms. J Adv Computer Sci Technol. 2019;8(2):23.

Rai D, Tyagi K. Bio-inspired optimization techniques: a critical comparative study. ACM SIGSOFT Softw Eng Notes. 2013;38(4):1–7.

Binitha S, Sathya SS. A survey of bio inspired optimization algorithms. IntJSoft ComputEng. 2012;2(2):137–151.

Almufti SM.U-Turning Ant Colony Algorithm Powered by Great Deluge Algorithm for the Solution of TSP Problem.MSc Thesis.Famagusta, North Cyprus, Turkey: Eastern Mediterranean University (EMU) –Doğu Akdeniz Üniversitesi (DAÜ); 2015.

Asaad RR, Abdulnabi NL. Using local searches algorithms with ant colony optimization for the solution of TSP problems. Acad J Nawroz Univ. 2018;7(3):1–6.

Almufti SM, Shaban AA. U-turning ant colony algorithm for solving symmetric traveling salesman problem. Acad J Nawroz Univ. 2018;7(4):45–49.

Yang XS. Metaheuristic optimization. Scholarpedia. 2011;6(8):11472.

Rajpurohit J, Sharma TK, Abraham A. Glossary of metaheuristic algorithms. Int J Computer Inform Syst Ind Manage Appl. 2017;9: 181–205.

Dorigo M. Optimization, Learning and Natural Algorithms. PhD Thesis.Milan, Italy: Politecnico di Milano; 1992.

Zebari AY, Almufti SM, Abdulrahman CM. Bat algorithm (BA): review, applications and modifications. Int J Sci World. 2020;8(1):1–7.

Yang XS. A new metaheuristic bat-inspired algorithm. In: González JR, Pelta DA, Cruz C, Terrazas G, Krasnogor N, editors. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) 2010.Heidelberg, Germany: Springer; 2010.Pp. 65–74.

Almufti SM, Zebari AY, Omer HK. A comparative study of particle swarm optimization and genetic algorithm. J Adv Computer Sci Technol. 2019;8(2):40.

Hochba DS, editor. Approximation algorithms for NP-hard problems. ACM Sigact News. 1997;28(2):40–52.

Almufti S, Asaad R, Salim B. Review on elephant herding optimization algorithm performance in solving optimization problems. Int J Eng Technol. 2018;7(1):6109–6114.

Wang GG, Deb S, Gao XZ, Coelho LD. A new metaheuristic optimisation algorithm motivated by elephant herding behaviour. Int J Bio-Inspired Comput. 2016;8(6):394–409.

Almufti S, Marqas R, Asaad R. Comparative study between elephant herding optimization (EHO) and U-turning ant colony optimization (U-TACO) in solving symmetric traveling salesman problem (STSP). J Adv Computer Sci Technol. 2019;8(2):32.

Cuevas E, Cienfuegos M. A new algorithm inspired in the behavior of the social-spider for constrained optimization. Expert Syst Appl. 2014;41(2):412–425.

Luque-Chang A, Cuevas E, Fausto F, Zaldívar D, Pérez M.Social spider optimization algorithm: modifications, applications, and perspectives.Math Problems Eng.2018; 2018:Article 6843923.doi: 10.1155/2018/6843923.

Baş E, Ülker E. A binary social spider algorithm for continuous optimization task. Soft Comput. 2020; 24 (17): 12953–12979. doi: 10.1007/s00500-020-04718-w.

Fausto F, Cuevas E, Maciel-Castillo O, Morales-Castañeda B. A real-coded optimal sensor deployment scheme for wireless sensor networks based on the social spider optimization algorithm. Int J Comput Intell Syst. 2019; 12 (2): 676–696. doi: 10.2991/ijcis.d.190614.001.

Husodo A, Jati G, Octavian A, Jatmiko W. Enhanced social spider optimization algorithm for increasing performance of multiple pursuer drones in neutralizing attacks from multiple evader drones. IEEE Access. 2020; 8: 22145–22161. doi: 10.1109/access.2020.2969021.

Nguyen T, Vo D. Improved social spider optimization algorithm for optimal reactive power dispatch problem with different objectives. Neural Comput Appl. 2019; 32 (10): 5919–5950. doi: 10.1007/s00521-019-04073-4.

Maurya L, Mahapatra PK, Kumar A. A social spider optimized image fusion approach for contrast enhancement and brightness preservation. Appl Soft Comput. 2017; 52: 575–592.

Dollaor J, Chiewchanwattana S, Sunat K, Muangkote N. The application of social-spider optimization for parameter improvement in the Lukasiewicz structure. In: Proceedings of the 8th International Conference on Knowledge and Smart Technology, KST 2016, Chiang Mai, Thailand, February 3–6, 2016. pp. 27–32.

Ouadfel S, Taleb-Ahmed A. Social spiders optimization and flower pollination algorithm for multilevel image thresholding: a performance study. Expert Syst Appl. 2016; 55: 566–584.

Singh D.A new bio-inspired social spider algorithm.Int J Appl Metaheuristic Comput.2021; 12 (1): 79–93.doi: 10.4018/ijamc.2021010105.

Yu J, Li V.A social spider algorithm for global optimization.Appl Soft Comput.2015; 30: 614–627. doi: 10.1016/j.asoc.2015.02.014.

Mahato D, Singh R.On maximizing reliability of grid transaction processing system considering balanced task allocation using social spider optimization.Swarm Evolutionary Comput.2018; 38: 202–217. doi: 10.1016/j.swevo.2017.07.011.

Abd El Aziz M, Hassanien A.An improved social spider optimization algorithm based on rough sets for solving minimum number attribute reduction problem.Neural Comput Appl.2017; 30 (8): 2441–2452. doi: 10.1007/s00521-016-2804-8.

Zhou Y, Zhou Y, Luo Q, Abdel-Basset M.A simplex method-based social spider optimization algorithm for clustering analysis. Eng Appl Artif Intell.2017; 64: 67–82.

Spendley W, Hext GR, Himsworth FR.Sequential application of simplex designs in optimisation and evolutionary operation. TechnimetricsJ Stat Phys Chem Eng Sci.2962;4: 441–461.

Klein CE, Segundo EH, Mariani VC, Coelho LdS.Modified social-spider optimization algorithm applied to electromagnetic optimization. IEEE Trans Magn.2016; 52 (3): 28–31.

Ali MM.Synthesis of the β-distribution as an aid to stochastic global optimization. Comput Stat Data Anal.2007; 52 (1): 133–149.

Zhao R, Luo Q, Zhou Y.Elite opposition-based social spider optimization algorithm for global function optimization. Algorithms.2017; 10 (1): Paper 9.

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

Tawhid MA, Ali AF.A hybrid social spider optimization and genetic algorithm for minimizing molecular potential energy function. Soft Comput.2017; 21 (21): 6499–6514.

Sun S-C, Qi H, Ren Y-T, Yu X-Y, Ruan L-M.Improved social spider optimization algorithms for solving inverse radiation and coupled radiation–conduction heat transfer problems. Int Commun Heat Mass Transfer. 2017; 87: 132–146.

Cuevas E, Osuna V, Oliva D. Evolutionary Computation Techniques: A Comparative Perspective.Studies in Computational Intelligence 686.Heidelberg, Germany: Springer; 2017.

Khorramnia R, Akbarizadeh M-R, Jahromi MK, Khorrami SK, Kavusifard F.A new unscented transform for considering wind turbine uncertainty in ED problem based on SSO algorithm. J Intell Fuzzy Syst Appl Eng Technol.2015; 29 (4): 1479–1491.

Zhou Y, Zhao R, Luo Q, Wen C.Sensor deployment scheme based on social spider optimization algorithm for wireless sensor networks. Neural Process Lett.2017; 48: 71–94.

Hejrati Z, Fattahi S, Faraji I.Optimal congestion management using the social spider optimization algorithm.In:29th International Power System Conference, Tehran, Iran, 2014.

El-Fergany AA, El-Hameed MA. “Efficient frequency controllers for autonomous two-area hybrid microgrid system using social-spider optimizer. IET Gener TransmDistrib.2017; 11 (3): 637–648.

Shayeghi H, Molaee A, Ghasemi A.Optimal design of FOPID controller for LFC in an interconnected multi-source power system. Int JTechn Phys Problems Eng.2016; 8 (26): 36–44.



How to Cite

B. . Rajesh, A. A., and A. . Joseph I., “An Overview, Changes, and Uses of the Novel Social Spider Optimization Algorithm”, JoSETTT, vol. 10, no. 3, pp. 61–71, Jan. 2024.