Advertisement

Artificial Intelligence Review

, Volume 52, Issue 3, pp 1579–1627 | Cite as

Sports inspired computational intelligence algorithms for global optimization

  • Bilal AlatasEmail author
Article

Abstract

Many classical search and optimization algorithms are especially insufficient in solving very hard large scale nonlinear problems with stringent constraints. Hence, computational intelligence optimization algorithms have been proposed and used to find well-enough solutions at a reasonable computation time when the classical algorithms are not applicable or do not provide good solutions to these problems due to the unmanageable search space. Many existing algorithms are nature-inspired, which work by simulating or modeling different natural processes. Due to the philosophy of continually searching the best and absence of the most efficient method for all types of problems, novel algorithms or new variants of current algorithms are being proposed and seem to be proposed in future to see if they can cope with challenging optimization problems. Studies on sports in recent years have shown that processes, concepts, rules, and events in various sports can be considered and modelled as novel efficient search and optimization methods with effective exploration capabilities in many cases, which are able to outperform existing classical and computational intelligence based optimization methods within different types of search spaces (Kashan in Appl Soft Comput 16:171–200, 2014; Bouchekara in Oper Res 1–57, 2017; Razmjooy in J Control Autom Electr Syst 1–22, 2016; Osaba et al. in Appl Intell 41(1):145–166, 2014a, Sci World J, 2014b). These novel and interesting sports based algorithms have shown to be more effective and robust than alternative approaches in a large number of applications. In this work, all of the computational intelligence algorithms based on sports and their applications have been for the first time searched and collected. Specific modelling of real sport games for computational intelligence algorithms and their novelties in terms of comparison with alternative existing algorithms for optimization have been reviewed with specific characteristics, computational implementation details and main applications capabilities, in the frame of hard optimization problems. Information is given about these search and optimization algorithms such as League Championship Algorithm, Soccer League Optimization, Soccer Game Optimization, Soccer League Competition Algorithm, Golden Ball Algorithm, World Cup Optimization, Football Optimization Algorithm, Football Game Inspired Algorithm, and Most Valuable Player Algorithm. Performance comparison of these sports based algorithms and other popular algorithms such as Genetic Algorithm, Particle Swarm Optimization, and Differential Evolution within unconstrained global optimization benchmark problems with different characteristics has been performed for the first time. A general evaluation has also been discussed with further research directions.

Keywords

Computational intelligence Global optimization Sports inspired optimization 

References

  1. Abdulhamid SM, Abd Latiff MS (2014) League Championship Algorithm based job scheduling scheme for infrastructure as a service cloud. In: 5th international graduate conference on engineering, science and humanities (IGCESH2014), Universiti Teknologi Malaysia, Johor Bahru, MalaysiaGoogle Scholar
  2. Abdulhamid SM, Abd Latiff MS, Abdullahi M (2015) Job scheduling technique for infrastructure as a service cloud using an improved league championship algorithm. In: The second international conference on advanced data and information engineering (DaEng-2015)Google Scholar
  3. Abdulhamid SM, Abd Latiff MS, Ismaila I (2014) Tasks scheduling technique using league championship algorithm for makespan minimization in IAAS cloud. ARPN J Eng Appl Sci 9(12):2528–2533Google Scholar
  4. Abdulhamid SM, Abd Latiff MS, Abdul-Salaam G, Hussain Madni SH (2016) Secure scientific applications scheduling technique for cloud computing environment using global league championship algorithm. PLoS ONE 11(7):1–18Google Scholar
  5. Akyol S, Alatas B (2017) Plant intelligence based metaheuristic optimization algorithms. Artif Intell Rev 47(4):417–462Google Scholar
  6. Akyol S, Alatas B (2016a) Efficiency evaluation of crow search algorithm in benchmark functions for optimization. In: 2nd international conference on engineering and natural sciences (ICENS), pp 939–944Google Scholar
  7. Akyol S, Alatas B (2016b) Chaotically initiated flower pollination algorithm for search and optimization problems. In: 2nd international conference on engineering and natural sciences, pp 2934–2940Google Scholar
  8. Alatas B (2011) ACROA: artificial chemical reaction optimization algorithm for global optimization. Expert Syst Appl 38(10):13170–13180Google Scholar
  9. Alba E, Luque G, García-Nieto J, Ordonez GG, Leguizamon G (2007) Mallba: a software library to design efficient optimisation algorithms. Int J Innov Comput Appl 1:74–85Google Scholar
  10. Ali J, Saeed M, Chaudhry NA, Luqman M, Tabassum MF (2015) Artificial showering algorithm: a new meta-heuristic for unconstrained optimization. Sci Int 27(6):4939–4942Google Scholar
  11. Ashrafi SM, Dariane AB (2011) A novel and effective algorithm for numerical optimization: melody search (MS). In: 11th IEEE international conference on hybrid intelligent systems (HIS), pp 109–114Google Scholar
  12. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE congress on evolutionary computation, pp 4661–4667Google Scholar
  13. Badrloo S (2015) A new method for solving combinatorial optimization problems with permutation based solution structure using league championship algorithm. M.Sc. Thesis, Azad University, Science and Research Branch, Iran (in Persian)Google Scholar
  14. Bingol H, Alatas B (2016) Chaotic league championship algorithms. Arab J Sci Eng 41(12):5123–5147MathSciNetzbMATHGoogle Scholar
  15. Birbil SI, Fang SC (2003) An electromagnetism-like mechanism for global optimization. J Glob Optim 25:263–282MathSciNetzbMATHGoogle Scholar
  16. Borji A, Hamidi M (2009) A new approach to global optimization motivated by parliamentary political competitions. Int J Innov Comput Inf Control 5(6):1643–1653Google Scholar
  17. Bouchekara HREH (2017) Most Valuable Player Algorithm: a novel optimization algorithm inspired from sport. Oper Res 1–57Google Scholar
  18. Bouchekara HREH, Abido MA, Chaib AE, Mehasni R (2014a) Optimal power flow using the league championship algorithm: a case study of the Algerian power system. Energy Convers Manag 87:58–70Google Scholar
  19. Bouchekara H, Abdallh A, Hamza Kherrab LD, Mehasni R (2014b) Design optimization of electromagnetic devices using the League Championship Algorithm. In: International workshops on optimization and inverse problems in electromagnetism (OIPE)Google Scholar
  20. Brownlee J (2007) Oat: The Optimization Algorithm Toolkit, Technical Report, Complex Intelligent Systems Laboratory, Swinburne University of TechnologyGoogle Scholar
  21. Cai W, Yang W, Chen X (2008) A global optimization algorithm based on plant growth theory: plant growth optimization. In: 2008 international conference on intelligent computation technology and automation (ICICTA), pp 1194–1199Google Scholar
  22. Can U, Alatas B (2015) Physics based metaheuristic algorithms for global optimization. Am J Inf Sci Comput Eng 1(3):94–106Google Scholar
  23. Chagwiza G, Jaison A, Masamha T (2016) Parameter improvement of the soccer league competition algorithm by introducing stubborn players: application to water distribution network. Math Prob EngGoogle Scholar
  24. Chu S-C, Tsai P-W, Pan J-S (2006) Cat swarm optimization. In: PRICAI 2006: trends in artificial intelligence. Springer, New York, pp 854–858Google Scholar
  25. Chuang CL, Jiang JA (2007) Integrated radiation optimization: inspired by the gravitational radiation in the curvature of space-time. In: 2007 IEEE congress on evolutionary computation, pp 3157–3164Google Scholar
  26. Colak ME, Varol A (2015) A novel intelligent optimization algorithm inspired from circular water waves. Elektronika ir Elektrotechnika 21:3–6Google Scholar
  27. Daskin A, Kais S (2011) Group leaders optimization algorithm. Mol Phys 109(5):761–772Google Scholar
  28. De Castro LN, Von Zuben FJ (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evol Comput 6(3):239–251Google Scholar
  29. Dorigo M, Maniezzo V, Colorni A (1991) The ant system: an autocatalytic optimizing process. Technical ReportGoogle Scholar
  30. Duarte A, Fernández F, Sánchez Á, Sanz A (2004) A hierarchical social metaheuristic for the max-cut problem. In: European conference on evolutionary computation in combinatorial optimization. Springer, Berlin, Heidelberg, pp 84–94Google Scholar
  31. Edraki S (2014) A new approach for engineering design optimization of centrifuge pumps based on league championship algorithm. Science and Research Branch, Azad University, TehranGoogle Scholar
  32. Eyvazi M (2015) Portfolio optimization problem with multi-period investment readjustment using league championship algorithm. M.Sc. Thesis, Tarbiat Modares University, Iran (in Persian)Google Scholar
  33. Fadakar E, Ebrahimi M (2016) A new metaheuristic football game inspired algorithm. In: 2016 1st IEEE conference on swarm intelligence and evolutionary computation (CSIEC), pp 6–11Google Scholar
  34. Gálvez A, Iglesias A (2016) New memetic self-adaptive firefly algorithm for continuous optimisation. Int J Bio-Inspired Comput 8(5):300–317Google Scholar
  35. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68Google Scholar
  36. Genc HM, Eksin I, Erol OK (2010) Big bang - big crunch optimization algorithm Hybridized With Local Directional Moves and Application to Target Motion Analysis Problem. IEEE Int Conf Syst Man Cybern (SMC) 2010:881–887Google Scholar
  37. Hatamzadeh P, Khayyambashi MR (2012a) Football optimization: an algorithm for optimization inspired by football game. In: ICSll ISSSI, 2012, Kharazmi UniversityGoogle Scholar
  38. Hatamzadeh P, Khayyambashi MR (2012b) Neural network learning based on football optimization algorithm. In: Proceedings of the third international conference on contemporary issues in computer and information sciences (CICIS 2012) (8). Universal-PublishersGoogle Scholar
  39. Holland JH, Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Longman Publishing Co., Inc., BostonGoogle Scholar
  40. Hsiao YT, Chuang CL, Jiang JA, Chien CC (2005) A novel optimization algorithm: space gravitational optimization. In: 2005 IEEE international conference on systems, man and cybernetics, vol 3, pp 2323–2328Google Scholar
  41. Ibrahim A, Rahnamayan S, Martin MV (2014) Simulated raindrop algorithm for global optimization. In: IEEE 27th Canadian conference electrical and computer engineering (CCECE), pp 1–8Google Scholar
  42. Jalili S, Husseinzadeh Kashan A, Hosseinzadeh Y (2016) League championship algorithms for optimum design of pin-jointed structures. J Comput Civ Eng.  https://doi.org/10.1061/(ASCE)CP.1943-5487.0000617
  43. Jamil M, Yang XS (2013) A literature survey of benchmark functions for global optimisation problems. Int J Math Model Numer Optim 4(2):150–194zbMATHGoogle Scholar
  44. Jaramillo A, Crawford B, Soto R, Misra S, Olguín E, Rubio ÁG, Villablanca SM (2016b) An approach to solve the set covering problem with the soccer league competition algorithm. In: International conference on computational science and its applications. Springer, pp 373–385Google Scholar
  45. Jaramillo A, Crawford B, Soto R, Villablanca SM, Rubio ÁG, Salas J, Olguín E (2016a) Solving the set covering problem with the soccer league competition algorithm. In: International conference on industrial, engineering and other applications of applied intelligent systems. Springer, pp 884–891Google Scholar
  46. Jaramillo A, Gýmez A, Mansilla S, Salas J, Crawford B, Soto R, Olguýn E (2016c) Using the soccer league competition algorithm to solve the set covering problem. In: 11th Iberian conference on information systems and technologies (CISTI), pp 1–4Google Scholar
  47. Javidy B, Hatamlou A, Mirjalili S (2015) Ions motion algorithm for solving optimization problems. Appl Soft Comput 32:72–79Google Scholar
  48. Kahledan S (2014) A league championship algorithm for travelling salesman problem. Najaf Abad Branch, Azad University, TehranGoogle Scholar
  49. Kamarudin AA, Othman ZA, Sarim HM (2016) Water flow algorithm decision support tool for travelling salesman problem. In: Proceedings of the international conference on applied science and technology 2016 (ICAST’16), vol 1761(1). AIP PublishingGoogle Scholar
  50. Karci A, Alatas B (2006) Thinking capability of saplings growing up algorithm. Intelligent data engineering and automated learning–IDEAL 2006, vol 4224. Lecture notes in computer Science. Springer, Berlin, pp 386–393Google Scholar
  51. Kashan AH (2009) League Championship Algorithm: a new algorithm for numerical function optimization. In: SoCPaR, pp 43–48Google Scholar
  52. Kashan AH, Karimi B (2010) A new algorithm for constrained optimization inspired by the sport league championships. In: IEEE congress on evolutionary computation, pp 1–8Google Scholar
  53. Kashan AH, Karimiyan S, Karimiyan M, Kashan MH (2012) A modified League Championship Algorithm for numerical function optimization via artificial modeling of the “between two halves analysis”. In: IEEE joint 6th international conference on soft computing and intelligent systems (SCIS) and 13th international symposium on advanced intelligent systems (ISIS), pp 1944–1949Google Scholar
  54. Kashan AH (2011) An efficient algorithm for constrained global optimization and application to mechanical engineering design: league championship algorithm (LCA). Comput Aided Des 43(12):1769–1792Google Scholar
  55. Kashan AH (2014) League Championship Algorithm (LCA): an algorithm for global optimization inspired by sport championships. Appl Soft Comput 16:171–200Google Scholar
  56. Kaveh A (2014) Magnetic charged system search. In: Advances in metaheuristic algorithms for optimal design of structures. Springer, pp 87–134Google Scholar
  57. Kaveh A, Bakhshpoori T (2016) Water evaporation optimization: a novel physically inspired optimization algorithm. Comput Struct 167:69–85Google Scholar
  58. Keijzer M, Merelo JJ, Romero G, Schoenauer M (2002) Evolving objects: a general purpose evolutionary computation library. Artif Evol 2310:829–888zbMATHGoogle Scholar
  59. Kejani T (2013) A new approach for reliability optimization based on league championship algorithm (LCA). Najaf Abad Branch, Azad University, TehranGoogle Scholar
  60. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: IEEE international conference on neural networks. Piscataway, pp 1942–1948Google Scholar
  61. Khaji E. (2014) Soccer League Optimization: A heuristic Algorithm Inspired by the Football System in European Countries. arXiv preprint arXiv:1406.4462
  62. Kiziloluk S, Alatas B (2012) Current social-based heuristic optimization algorithms. Cumhuriyet Univ J Econ Adm Sci 13(2):39–56Google Scholar
  63. Kripka M, Kripka RML (2008) Big crunch optimization method. In: International conference on engineering optimization, Brazil, pp 1–5Google Scholar
  64. Kronfeld M, Planatscher H, Zell A (2010) The EvA2 optimization framework. Lecture Notes in Computer Science, vol 6073. Springer, Berlin, pp 247–250Google Scholar
  65. Labbi Y, Attous DB, Gabbar HA, Mahdad B, Zidan A (2016) A new rooted tree optimization algorithm for economic dispatch with valve-point effect. Int J Electr Power Energy Syst 79:298–311Google Scholar
  66. Lam AY, Li VO (2010) Chemical-reaction-inspired metaheuristic for optimization. IEEE Trans Evol Comput 14(3):381–399Google Scholar
  67. Lenin K, Reddy BR, Kalavathi MS (2013) League championship algorithm (LCA) for solving optimal reactive power dispatch problem. Int J Comput Inf Technol 1(3):254–272Google Scholar
  68. Lukasiewycz M, Glab FR, Helwig S (2009) Opt4: optimization framework for java. http://www.opt4j.org
  69. Maniezzo V, Stützle T, Voss S (2009) Matheuristics: hybridizing metaheuristics and mathematical programming, vol 10. Springer, New YorkzbMATHGoogle Scholar
  70. Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inform 1:355–366Google Scholar
  71. Meng X, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: chicken swarm optimization. In: International conference in swarm intelligence. Springer, pp 86–94Google Scholar
  72. Merrikh-Bayat F (2015) The runner-root algorithm: a metaheuristic for solving unimodal and multimodal optimization problems inspired by runners and roots of plants in nature. Appl Soft Comput 33:292–303Google Scholar
  73. Mirjalili S (2016) SCA: a Sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133Google Scholar
  74. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67Google Scholar
  75. Moosavian N (2015) Soccer league competition algorithm for solving knapsack problems. Swarm Evol Comput 20:14–22Google Scholar
  76. Moosavian N, Roodsari BK (2013) Soccer league competition algorithm, a new method for solving systems of nonlinear equations. Int J Intell Sci 4(1):7Google Scholar
  77. Moosavian N, Roodsari BK (2014) Soccer league competition algorithm: a novel meta-heuristic algorithm for optimal design of water distribution networks. Swarm Evol Comput 17:14–24Google Scholar
  78. Mora-Gutiérrez RA, Ramírez-Rodríguez J, Rincón-García EA (2014) An optimization algorithm inspired by musical composition. Artif Intell Rev 41(3):301–315Google Scholar
  79. Murase H (2000) Finite element inverse analysis using a photosynthetic algorithm. Comput Electr Agr 29:115–123Google Scholar
  80. Nedaie A, Khoshalhan F (2016) A new play-off approach in league championship algorithm for solving large-scale support vector machine problems. Int J Ind Eng Prod Res 27(1):61–68Google Scholar
  81. Osaba E, Carballedo R, López-García P, Diaz F (2016) Comparison between Golden Ball Meta-heuristic, Evolutionary Simulated Annealing and Tabu Search for the Traveling Salesman Problem. In: Proceedings of the 2016 on genetic and evolutionary computation conference companion, ACM, pp 1469–1470Google Scholar
  82. Osaba E, Diaz F, Onieva E (2014a) Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts. Appl Intell 41(1):145–166Google Scholar
  83. Osaba E, Diaz F, Carballedo R, Onieva E, Perallos A (2014b) Focusing on the golden ball metaheuristic: an extended study on a wider set of problems. Sci World JGoogle Scholar
  84. Osaba E, Diaz F, Onieva E (2013) A novel meta-heuristic based on soccer concepts to solve routing problems. In Proceedings of the 15th annual conference companion on Genetic and evolutionary computation. ACM, pp 1743–1744Google Scholar
  85. Ozbay FA, Alatas B (2015) Review of social-based artificial intelligence optimization algorithms for social network analysis. Int J Pure Appl Sci 1:33–52Google Scholar
  86. Ozbay FA, Alatas B (2016a) A simple and global physics based metaheuristic method: water evaporation optimization. In: 2nd international conference on engineering and natural sciences, pp 660–665Google Scholar
  87. Ozbay FA, Alatas B (2016b) Review of computational intelligence method inspired from behavior of water. Afyon Kocatepe Univ J Sci Eng Spec Issue 137–147Google Scholar
  88. Ozbay FA, Alatas B (2016c) Review of music based computational intelligence methods. 1st international conference on engineering technology and applied sciences (ICETAS), pp 663–669Google Scholar
  89. Parejo J. A, Racero J, Guerrero F, Kwok T, Smith K (2003) Fom: a framework for metaheuristic optimization. In: Lecture Notes in Computer Science, vol 2660, Springer, pp 886–895Google Scholar
  90. Pourali Z, Aminnayeri M (2011) A novel discrete league championship algorithm for minimizing earliness/tardiness penalties with distinct due dates and batch delivery consideration. In: International Conference on Intelligent Computing. Springer, Berlin Heidelberg, pp 139–146Google Scholar
  91. Premaratne U, Samarabandu J, Sidhu T (2009) A new biologically inspired optimization algorithm. In: international conference on industrial and information systems (ICIIS), pp 279–284Google Scholar
  92. Purnomo HD, Wee HM (2013) Soccer game optimization: an innovative integration of evolutionary algorithm and swarm intelligence algorithm. Meta-Heuristics optimization algorithms in engineering, business, economics, and finance. IGI Global, PennsylvaniaGoogle Scholar
  93. Purnomo HD (2014a) Soccer game optimization for continuous and discrete problems. Jurnal Metris 15(2):65–76Google Scholar
  94. Purnomo HD (2014b) Soccer game optimization: fundamental concept. Jurnal Sistem Komputer 4(1):25–36Google Scholar
  95. Purnomo HD, Wee HM (2015) Soccer game optimization with substitute players. J Comput Appl Math 283:79–90MathSciNetzbMATHGoogle Scholar
  96. Qi X, Zhu Y, Chen H, Zhang D, Niu B (2013) An idea based on plant root growth for numerical optimization. In: Intelligent computing theories and technology. Lecture Notes in Computer Science, vol 7996. Springer, pp 571–578Google Scholar
  97. Rabanal P, Rodríguez I, Rubio F (2007) Using river formation dynamics to design heuristic algorithms. In: International conference on unconventional computation. Springer, Berlin, Heidelberg, pp 163–177Google Scholar
  98. Ramezani F, Lotfi S (2013) Social-based algorithm (SBA). Appl Soft Comput 13(5):2837–2856Google Scholar
  99. Rao RV, Savsani VJ, Vakharia DP (2012) Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci 183(1):1–15MathSciNetGoogle Scholar
  100. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248zbMATHGoogle Scholar
  101. Razmjooy N, Khalilpour M, Ramezani M (2016) A new meta-heuristic optimization algorithm inspired by FIFA world cup competitions: theory and its application in PID designing for AVR system. J Control Autom Electr Syst 1–22Google Scholar
  102. Razmjooy N, Ramezani M (2016) Model Order Reduction based on meta-heuristic optimization methods. In: 1st international conference on new research achievements in electrical and computer engineeringGoogle Scholar
  103. Rezoug A, Boughaci D (2016) A self-adaptive harmony search combined with a stochastic local search for the 0–1 multidimensional knapsack problem. Int J Bio-Inspired Comput 8(4):234–239Google Scholar
  104. Ruttanateerawichien K, Kurutach W, Pichpibul T (2014) An improved golden ball algorithm for the capacitated vehicle routing problem. Bio-Inspired Comput-Theor Appl. Springer, Berlin Heidelberg, pp 341–356Google Scholar
  105. Ruttanateerawichien K, Kurutach W, Pichpibul T (2016) A new efficient and effective golden-ball-based technique for the capacitated vehicle routing problem. In: IEEE 15th international conference on computer and information science (ICIS), IEEE/ACIS, pp 1–5Google Scholar
  106. Sacco WF, De Oliveira CR (2005) A new stochastic optimization algorithm based on a particle collision metaheuristic. In: Proceedings of 6th WCSMOGoogle Scholar
  107. Sadollah A, Eskandar H, Bahreininejad A, Kim JH (2015) Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems. Appl Soft Comput 30:58–71Google Scholar
  108. Sajadi SM, Kashan AH, Khaledan S (2014) A new approach for permutation flow-shop scheduling problem using league championship algorithm. In: Proceedings of CIE44 and IMSS, vol 14Google Scholar
  109. Salcedo-Sanz S (2016) Modern meta-heuristics based on nonlinear physics processes: a review of models and design procedures. Phys Rep 655:1–70MathSciNetGoogle Scholar
  110. Salem SA (2012) BOA: a novel optimization algorithm. In: IEEE 2012 international conference on engineering and technology (ICET), pp 1–5Google Scholar
  111. Salhi A, Fraga ES (2011) Nature-inspired optimisation approaches and the new plant propagation algorithm. In: The international conference on numerical analysis and optimization (ICeMATH ’11). Yogyakarta, IndonesiaGoogle Scholar
  112. Saraswathi D, Srinivasan E (2017) Mammogram analysis using league championship algorithm optimized ensembled FCRN classifier. Indones J Electr Eng Comput Sci 5(2):451–461Google Scholar
  113. Sayoti F, Ri ME (2016) Golden ball algorithm for solving flow shop scheduling problem. Int J Artif Intell Interact Multim 4(1):15–18Google Scholar
  114. Sayoti F, Riffi ME (2015) Random-keys golden ball algorithm for solving traveling salesman problem. Int Rev Model Simul (IREMOS) 8(1):84–89Google Scholar
  115. Seyedhosseini SM, Badkoobehi H, Noktehdan A (2015) Machine-part cell formation problem using a group based league championship algorithm. J Promot Manag 21:55–63Google Scholar
  116. Shah-Hosseini H (2009) The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. Int J Bio-Inspir Comput 1(1–2):71–79Google Scholar
  117. Shah-Hosseini H (2011) Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous optimisation. Int J Comput Sci Eng 6:132–140Google Scholar
  118. Shahrezaee M (2017) Image segmentation based on world cup optimization algorithm. Majlesi J Electr Eng 11(2):39–45Google Scholar
  119. Shi Y (2011) Brain storm optimization algorithm. In: International conference in swarm intelligence. Springer, Berlin, Heidelberg, pp 303–309Google Scholar
  120. Stephen MJ, PV PR (2013) Simple league championship algorithm. Int J Comput Appl 75(6):28–32Google Scholar
  121. Sun J, Wang X, Li K, Wu C, Huang M, Wang X (2013) An auction and league championship algorithm based resource allocation mechanism for distributed cloud. Int Workshop Adv Parall Process Technol. Springer, Berlin Heidelberg, pp 334–346Google Scholar
  122. Surjanovic S, Bingham D (2013) Virtual Library of Simulation Experiments: Test Functions and Datasets. Retrieved May 11, 2017, from http://www.sfu.ca/ssurjano
  123. Tayarani-N MH, Akbarzadeh-T MR (2008) Magnetic optimization algorithms a new synthesis. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) 2659-2664Google Scholar
  124. Thammano A, Moolwong J (2010) A new computational intelligence technique based on human group formation. Expert Syst Appl 37(2):1628–1634Google Scholar
  125. Ventura S, Romero C, Zafra A, Delgado J, Hervás C (2008) JCLC: a java framework for evolutionary computation. Soft Comput 2(4):381–392Google Scholar
  126. Wagner S (2009) Heuristic optimization software systems modeling of heuristic optimization algorithms in the heuristic lab software environment (Ph.D. thesis), Johannes Kepler University, LinzGoogle Scholar
  127. Wang H, Wang W, Sun H, Rahnamayan S (2016) Firefly algorithm with random attraction. Int J Bio-Inspir Comput 8(1):33–41Google Scholar
  128. Xie L, Tan Y, Zeng J, Cui Z (2010) Artificial physics optimisation: a brief survey. Int J Bio-Inspir Comput 2(5):291–302Google Scholar
  129. Xing B, Gao WJ (2014) Central force optimization algorithm. In: Innovative computational intelligence: a rough guide to 134 clever algorithms. Springer, pp 333–337Google Scholar
  130. Xing B, Gao WJ (2014) Charged system search algorithm. In: Innovative computational intelligence: a rough guide to 134 clever algorithms. Springer, pp 339–346Google Scholar
  131. Xu Y, Cui Z, Zeng J (2010) Social emotional optimization algorithm for nonlinear constrained optimization problems. In: International Conference on Swarm, Evolutionary, and Memetic Computing. Springer, Berlin Heidelberg, pp 583–590Google Scholar
  132. Xu W, Wang R, Yang J (2015b) An improved league championship algorithm with free search and its application on production scheduling, Journal of Intelligent ManufacturingGoogle Scholar
  133. Xu W, Yang J, Wang R (2015a) An Intelligent Method for Evaluation of Production Scheduling Performance. International Conference on Intelligent Systems Research and Mechatronics Engineering (ISRME 2015), 1121-1126Google Scholar
  134. Yang X-S (2012) Flower Pollination Algorithm for global optimization. In: Unconventional computation and natural computation. Springer. 240–249Google Scholar
  135. Yang FC, Wang YP (2007) Water flow-like algorithm for object grouping problems. J Chin Inst Ind Eng 24:475–488Google Scholar
  136. Zhang H, Zhu Y, Chen H (2014) Root growth model: a novel approach to numerical function optimization and simulation of plant root system. Soft Comput 18:521–537Google Scholar
  137. Zhao Z, Cui Z, Zeng J, Yue X (2011) Artificial plant optimization algorithm for constrained optimization problems. In: 2011 Second international conference on innovations in bio-inspired computing and applications (IBICA), 120–123Google Scholar
  138. Zheng YJ (2015) Water wave optimization: a new nature-inspired metaheuristic. Comput Oper Res 55:1–11MathSciNetzbMATHGoogle Scholar
  139. Zhou Y, Wang Y, Chen X, Zhang L, Wu K (2016) A Novel path planning algorithm based on plant growth mechanism. Soft Comput 1–11Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Department of Software EngineeringFirat UniversityElazigTurkey

Personalised recommendations