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Success history intelligent optimizer


Swarm intelligence (SI) is part of artificial intelligence that is based on the collective behavior of particles in a self-organized and decentralized intelligent systems which is inspired from either the human or animal social behavior. SI has a great involvement in solving optimization problems. As optimization-based systems are complicated, so powerful decentralized algorithms supported by SI are used to solve such problems. Intelligent algorithms of SI can solve the challenging issues of optimization due to their different properties; thus, there is an increased demand to enhance the performance and propose new and novel SI algorithms. This paper presents a novel stochastic swarm intelligence algorithm called success history intelligent optimizer (SHIO). It offers a solution for single-objective optimization problems by proposing a new exploration and exploitation movement strategy based on the three best solutions found in the search space to create a new movement vector, where each best solution is stored in the memory and subtracted from the average of the three best solutions found so far during the optimization process. The proposed SHIO ensures the efficiency of search space exploration and use. In order to confirm SHIO performance, several performance measurements (search history, trajectory and convergence curves) have been tested and SHIO was used to solve (23) single-objective optimization benchmarking functions. These functions have been classified to unimodal, multimodal and multimodal fixed. Various metrics such as mean, standard deviation, minimum and maximum have been utilized, and quantitative findings have been recorded. Further, trajectory and search history of the qualitative result were visualized. The results of test functions and performance metrics demonstrate that the proposed algorithm can explore various search area locations, make use of potential search space locations while optimizing, avoid local optimism and converge to the global best efficiently. SHIO delivers highly competitive and superior results in the evaluated unimodal and multimodal benchmarks over the compared algorithms. Note that SHIO algorithm source code is available on and open for public use.

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  1. 1.

    Wang D, Tan D, Liu L (2018) Particle swarm optimization algorithm: an overview. Soft Comput 22(2):387–408

    Article  Google Scholar 

  2. 2.

    Tangherloni A, Rundo L, Nobile MS (2017) Proactive particles in swarm optimization: a settings-free algorithm for real-parameter single objective optimization problems. In: 2017 IEEE congress on evolutionary computation (CEC), pp 1940–1947. IEEE

  3. 3.

    Al-Sayyed RM, Fakhouri HN, Rodan A, Pattinson C (2017) Polar particle swarm algorithm for solving cloud data migration optimization problem. Mod Appl Sci 11(8):98

    Article  Google Scholar 

  4. 4.

    Arora S, Singh S (2019) Butterfly optimization algorithm: an improved approach for global optimization. Soft Comput 23(3):715–734

    Article  Google Scholar 

  5. 5.

    Bansal JC (2019) Particle swarm optimization. In: Bansal JC, Singh PK, Pal NR (eds) Evolutionary and swarm intelligence algorithms. Springer, Cham, pp 11–23

    Google Scholar 

  6. 6.

    Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249

    Article  Google Scholar 

  7. 7.

    Khachaturyan A, Semenovskaya S, Vainshtein B (1979) Statistical-thermodynamic approach to determination of structure amplitude phases. Sov Phys Crystallogr 24(5):519–524

    MathSciNet  Google Scholar 

  8. 8.

    Liang JJ, Suganthan PN (2005) Dynamic multi-swarm particle swarm optimizer with local search. In: Proceedings of the 2005 IEEE congress on evolutionary computation, vol 2. IEEE Press, pp 522–528

  9. 9.

    Abbass HA (2001) MBO: marriage in honey bees optimization-A haplometrosis polygynous swarming approach. In: Evolutionary computation, 2001. Proceedings of the 2001 Congress on, vol 1, pp 207–214. IEEE

  10. 10.

    Li XL (2003) A new intelligent optimization-artificial fish swarm algorithm. Doctor thesis, Zhejiang University of Zhejiang, China

  11. 11.

    Roth M (2005) Termite: a swarm intelligent routing algorithm for mobile wireless ad-hoc networks

  12. 12.

    Pinto PC, Runkler TA, Sousa JM (2007) Wasp swarm algorithm for dynamic MAX-SAT problems. In: International Conference on Adaptive and Natural Computing Algorithms, pp 350–357

  13. 13.

    Mucherino A, Seref O (2007) Monkey search: a novel metaheuristic search for global optimization. In: Seref O, Kundakcioglu OE, Pardalos P (eds) AIP Conference Proceedings, vol 953, No 1, pp 162–173. AIP

  14. 14.

    Lu X, Zhou Y (2008) A novel global convergence algorithm: bee collecting pollen algorithm. In: International Conference on Intelligent Computing, pp 518–525. Springer Berlin Heidelberg

  15. 15.

    Yang XS, Deb S (2010) Eagle strategy using Levy walk and firefly algorithms for stochastic optimization. In: Gonzalez JR et al (eds) Nature inspired cooperative strategies for optimization, NICSO 2010, vol 284, pp 101–111

  16. 16.

    Shiqin Y, Jianjun J, Guangxing Y (2009). A dolphin partner optimization. In: Intelligent systems, 2009. GCIS'09. WRI Global Congress on, vol 1, pp 124–128. IEEE

  17. 17.

    Yang XS, Deb S, Fong S (2011) Accelerated particle swarm optimization and support vector machine for business optimization and applications. In: NDT2011, CCIS 136, Springer, pp 53–66

  18. 18.

    Askarzadeh A, Rezazadeh A (2013) A new heuristic optimization algorithm for modeling of proton exchange membrane fuel cell: bird mating optimizer. Int J Energy Res 37(10):1196–1204

    Article  Google Scholar 

  19. 19.

    Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl Based Syst 26:69–74

    Article  Google Scholar 

  20. 20.

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

    Article  Google Scholar 

  21. 21.

    Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl Based Syst 96:120–133

    Article  Google Scholar 

  22. 22.

    Mirjalili S, Mirjalili SM, Hatamlou A (2016) Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Comput Appl 27(2):495–513

    Article  Google Scholar 

  23. 23.

    Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Micro machine and human science, 1995. MHS'95., Proceedings of the Sixth International Symposium on, pp 39–43. IEEE

  24. 24.

    Pradhan R, Kabat MR, Sahoo SP (2013) A bacteria foraging-particle swarm optimization algorithm for QoS multicast routing. In: Panigrahi BK, Suganthan PN, Das S, Dash SS (eds) Swarm, evolutionary, and memetic computing. SEMCCO 2013. Lecture notes in computer science, vol 8297. Springer, Cham

    Google Scholar 

  25. 25.

    Yang X-S, He X (2013) Bat algorithm: literature review and applications. Int J Bio-Inspired Comput 5(3):141–149

    Article  Google Scholar 

  26. 26.

    Teodorovic D, Lucic P, Markovic G (2006) Bee colony optimization: principles and applications, neural network applications in electrical engineering, 2006. NEUREL 2006. 8th Seminar, Doi:

  27. 27.

    Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S, Zaidi M (2005) The bees algorithm. Technical note. Manufacturing Engineering Centre, Cardiff University, UK, pp 1–57

  28. 28.

    Hudaib AA, Fakhouri HN (2018) Supernova optimizer: a novel natural inspired meta-heuristic. Mod Appl Sci 12(1):32–50

    Article  Google Scholar 

  29. 29.

    Drias H, Sadeg S, Yahi S (2005) Cooperative bees swarm for solving the maximum weighted satisfiability problem. In: Cabestany J, Prieto AG, Sandoval F (eds) IWANN 2005. LNCS, vol 3512. Springer, Heidelberg, pp 318–325

    Google Scholar 

  30. 30.

    Chu S-C, Tsai P-W, Pan J-S (2006) Cat swarm optimization. pp 854–858.

  31. 31.

    Serban Iordache SCOOP Software GmbH, Köln, Germany (2010) Consultant-guided search: a new metaheuristic for combinatorial optimization problems. In: Proceeding GECCO '10 Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation Pages 225–232, Portland, Oregon, USA—July 07–11, 2010 ACM New York, NY, USA ©2010 ISBN: 978-1-4503-0072-8 doi>

  32. 32.

    Chu Y, Mi H, Liao H, Ji Z, Wu QH (2008) A fast bacterial swarming algorithm for high-dimensional function optimization. In: 2008 IEEE congress on evolutionary computation, CEC 2008. pp 3135–3140.

  33. 33.

    Li LX et al (2002) An optimizing method based on autonomous animals: fish swarm algorithm. In: Presented at the proc. of systems engineering theory & practice

  34. 34.

    Fakhouri HN, Hudaib A, Sleit A (2020) Hybrid particle swarm optimization with sine cosine algorithm and nelder-mead simplex for solving engineering design problems. Arab J Sci Eng 45(4):3091–3109

    Article  Google Scholar 

  35. 35.

    Fakhouri HN, Hudaib A, Sleit A (2020) Multivector particle swarm optimization algorithm. Soft Comput 24(15):11695–11713

    Article  Google Scholar 

  36. 36.

    Al-Sayyed R, Fakhouri HN, Rodan A, Pattinson C (2017) Particle swarm algorithm for solving cloud data migration optimization problem. Mod Appl Sci 11(8):98

    Article  Google Scholar 

  37. 37.

    Zhai Y-K, Xu Y (2012) A novel artificial fish swarm algorithm based on multi-objective optimization. In: ICIC'12 Proceedings of the 8th International Conference on Intelligent Computing Theories and Applications, Pages 67–73 Huangshan, China Springer-Verlag Berlin, Heidelberg ©2012 ISBN: 978-3-642-31575-6. doi:

  38. 38.

    Su S, Jiwen W, Fan W, Yin X (2007) Good lattice swarm algorithm for constrained engineering design optimization. In: 2007 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2007.

  39. 39.

    Krishnanand KN, Ghose D (2009) Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions. Swarm Intell 3:87.

    Article  Google Scholar 

  40. 40.

    Albashish D, Hammouri AI, Braik M, Atwan J, Sahran S (2021) Binary biogeography-based optimization based SVM-RFE for feature selection. Appl Soft Comput 101:107026

    Article  Google Scholar 

  41. 41.

    Rahman MA, Chandren Muniyandi R, Albashish D, Rahman MM, Usman OL (2021) Artificial neural network with Taguchi method for robust classification model to improve classification accuracy of breast cancer. PeerJ Comput Sci 7:e344

    Article  Google Scholar 

  42. 42.

    Baleanu D, Sadat R, Ali MR (2020) The method of lines for solution of the carbon nanotubes engine oil nanofluid over an unsteady rotating disk. Eur Phys J Plus 135(10):1–13

    Article  Google Scholar 

  43. 43.

    Sabir Z, Ali MR, Raja MAZ, Shoaib M, Núñez RAS, Sadat R (2021) Computational intelligence approach using Levenberg–Marquardt backpropagation neural networks to solve the fourth-order nonlinear system of Emden–Fowler model. Engineering with Computers, pp 1–17

  44. 44.

    Ayub A, Sabir Z, Altamirano GC, Sadat R, Ali MR (2021) Characteristics of melting heat transport of blood with time-dependent cross-nanofluid model using Keller–Box and BVP4C method. Engineering with Computers, pp 1–15

  45. 45.

    Hamad F, Al-Aamr R, Jabbar SA, Fakhuri H (2021) Business intelligence in academic libraries in Jordan: opportunities and challenges. IFLA J 47(1):37–50

    Article  Google Scholar 

  46. 46.

    Reid KE, Olsson N, Schlosser J, Peng F, Lund ST (2006) An optimized grapevine RNA isolation procedure and statistical determination of reference genes for real-time RT-PCR during berry development. BMC Plant Biol 6(1):1–11

    Article  Google Scholar 

  47. 47.

    Eberhart RC, Shi Y, Kennedy J (2001) Swarm intelligence. Elsevier, Amsterdam

    Google Scholar 

  48. 48.

    Rosner B, Glynn RJ, Ting Lee ML (2003) Incorporation of clustering effects for the Wilcoxon rank sum test: a large-sample approach. Biometrics 59(4):1089–1098

    MathSciNet  Article  Google Scholar 

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Correspondence to Hussam N. Fakhouri.

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Fakhouri, H.N., Hamad, F. & Alawamrah, A. Success history intelligent optimizer. J Supercomput (2021).

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  • Optimization
  • Swarm intelligence
  • Metaheuristics
  • Single-objective optimization
  • Decentralized intelligent systems