Skip to main content

Advertisement

SpringerLink
Log in

Review on Nature-Inspired Algorithms

  • Expository Reviews
  • Published:
Operations Research Forum Aims and scope Submit manuscript

Abstract

Optimization and its related solving methods are becoming increasingly important in most academic and industrial fields. The goal of the optimization process is to make a system or a design as effective and functional as possible. This is achieved by optimizing a set of objectives while meeting the system requirements. Optimization techniques are classified into exact and approximate algorithms. Nature-inspired (NI) methods, a sub-class of approximate techniques, are widely recognized for providing efficient approaches for solving a wide variety of real-world optimization problems. In this paper, we discuss many scenarios where we can or cannot use different NI methods in tackling real-world optimization problems. We also enrich our survey with many studies for the reader to prove the efficiency and efficacy of using NI methods to tackle many real-world applications. Therefore, NI methods should be considered as alternative reliable approaches in the absence of exact methods to provide satisfactory solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Rao SS (2009) Engineering optimization: theory and practice. John Wiley & Sons

  2. Boussaïd I, Lepagnot J, Siarry P (2013) A survey on optimization metaheuristics. Inf Sci 237:82–117

    Article  Google Scholar 

  3. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, Chichester, WS, UK

    Google Scholar 

  4. Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence

  5. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    Article  Google Scholar 

  6. Kennedy J, Eberhart R (1995) Particle swarm optimization. In Proc IEEE Intl Con on Neural Networks (Perth, Australia). pp. 1942–1948

  7. Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst 22(3):52–67

  8. 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. 4661–4667

  9. Cheng R, Jin Y (2014) A competitive swarm optimizer for large scale optimization. IEEE Transactions on Cybernetics 45(2):191–204

    Article  Google Scholar 

  10. Talbi EG (2009) Metaheuristics: from design to implementation, volume 74. John Wiley & Sons

  11. Archetti F, Schoen F (1984) A survey on the global optimization problem: general theory and computational approaches. Ann Oper Res 1(2):87–110

    Article  Google Scholar 

  12. Fister Jr I, Yang XS, Fister I, Brest J, Fister D (2013) A brief review of nature-inspired algorithms for optimization. arXiv preprint arXiv:1307.4186

  13. Talbi EG (2020) Machine learning into metaheuristics: a survey and taxonomy of data-driven metaheuristics

  14. Dechter R. (2003) Constraint processing. Morgan Kaufmann

  15. Fomin FV, Kratsch D (2010) Exact exponential algorithms. Springer Science & Business Media

  16. Applegate D, Bixby R, Cook W, Chvátal V (1998) On the solution of traveling salesman problems. CRPC-TR98744

  17. Cheeseman PC, Kanefsky B, Taylor WM (1991) Where the really hard problems are. In IJCAI (91)331–337

  18. Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (abc) algorithm and applications. Artif Intell Rev 42(1):21–57

    Article  Google Scholar 

  19. Zhang Y, Wang S, Ji G (2015) A comprehensive survey on particle swarm optimization algorithm and its applications. Math Probl Eng

  20. Yang XS (2010) Engineering optimization: an introduction with metaheuristic applications. John Wiley & Sons

  21. Xu L, Hutter F, Hoos H, Leyton-Brown K (2012) Evaluating component solver contributions to portfolio-based algorithm selectors. In International Conference on Theory and Applications of Satisfiability Testing. Springer, pp. 228–241

  22. Jin Y (2005) A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput 9(1):3–12

    Article  Google Scholar 

  23. Zanakis SH, Evans JR (1981) Heuristic “optimization”: Why, when, and how to use it. Interfaces 11(5):84–91

  24. Crainic TG, Toulouse M (2003) Parallel strategies for meta-heuristics. In Handbook of metaheuristics. Springer, pp. 475–513

  25. Szu HH, Hartley RL (1987) Nonconvex optimization by fast simulated annealing. Proceedings of the IEEE 75(11):1538–1540

  26. Tsallis C, Stariolo DA (1996) Generalized simulated annealing. Physica A 233(1-2):395–406

  27. Creutz M (1983) Microcanonical monte carlo simulation. Phys Rev Lett 50:1411–1414

    Article  Google Scholar 

  28. Dueck G, Scheuer T (1990) Threshold accepting: a general purpose optimization algorithm appearing superior to simulated annealing. J Comput Phys 90(1):161–175

    Article  Google Scholar 

  29. El Yafrani M, Ahiod B (2016) Population-based vs. single-solution heuristics for the travelling thief problem. In Proceedings of the Genetic and Evolutionary Computation Conference 2016. ACM, pp. 317–324

  30. Van Laarhoven PJ, Aarts EH, Lenstra JK (1992) Job shop scheduling by simulated annealing. Oper Res 40(1):113–125

  31. Delahaye D, Chaimatanan S, Mongeau M (2019) Simulated annealing: From basics to applications. In Handbook of Metaheuristics. Springer, pp. 1–35

  32. Beheshti Z, Shamsuddin SM (2013) A review of population-based meta-heuristic algorithms. Int J Adv Soft Comput Appl 5(1):1–35

    Google Scholar 

  33. Gogna A, Tayal A (2013) Metaheuristics: review and application. J Exp Theor Artif Intell 25(4):503–526

    Article  Google Scholar 

  34. Stuckman B, Evans G, Mollaghasemi M (1991) Comparison of global search methods for design optimization using simulation. In 1991 Winter Simulation Conference Proceedings. IEEE, pp. 937–944

  35. Atkinson AC (1992) A segmented algorithm for simulated annealing. Stat Comput 2(4):221–230

    Article  Google Scholar 

  36. Stokes Z, Mandal A, Wong WK (2020) Using differential evolution to design optimal experiments. Chemom Intell Lab Syst 199:103955

  37. García-Ródenas R, García-García JC, López-Fidalgo J, Martín-Baos JA, Wong WK (2020) A comparison of general-purpose optimization algorithms for finding optimal approximate experimental designs. Comput Stat Data Anal 144:106844

  38. Shi Y, Zhang Z, Wong WK (2019) Particle swarm based algorithms for finding locally and bayesian d-optimal designs. Journal of Statistical Distributions and Applications 6(1):3

  39. Mahmudy WF (2016) Improved simulated annealing for optimization of vehicle routing problem with time windows (vrptw). Kursor 7(3)

  40. Kose A, Sonmez BA, Balaban M (2017) Simulated annealing algorithm for graph coloring. arXiv preprint arXiv:1712.00709

  41. Emden-Weinert T, Proksch M (1999) Best practice simulated annealing for the airline crew scheduling problem. J Heuristics 5(4):419–436

    Article  Google Scholar 

  42. Hanafi R, Kozan E (2014) A hybrid constructive heuristic and simulated annealing for railway crew scheduling. Comput Ind Eng 70:11–19

    Article  Google Scholar 

  43. Bayram H, Şahin R (2013) A new simulated annealing approach for travelling salesman problem. Mathematical and Computational Applications 18(3):313–322

    Article  Google Scholar 

  44. Siarry P, Berthiau G, Durdin F, Haussy J (1997) Enhanced simulated annealing for globally minimizing functions of many-continuous variables. ACM Trans Math Softw (TOMS) 23(2):209–228

    Article  Google Scholar 

  45. Connolly DT (1990) An improved annealing scheme for the qap. Eur J Oper Res 46(1):93–100

  46. Misevičius A (2003) A modified simulated annealing algorithm for the quadratic assignment problem. Informatica 14(4):497–514

    Article  Google Scholar 

  47. Freitas AA (2003) A survey of evolutionary algorithms for data mining and knowledge discovery. In Advances in Evolutionary Computing. Springer, pp 819–845

  48. Deb K (1999) An introduction to genetic algorithms. Sadhana 24(4–5):293–315

    Article  Google Scholar 

  49. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning, addisson-wesley. Reading, MA

  50. Coello CC, Pulido GT (2005) Multiobjective structural optimization using a microgenetic algorithm. Struct Multidiscip Optim 30(5):388–403

    Article  Google Scholar 

  51. Krishnakumar K (1990) Micro-genetic algorithms for stationary and non-stationary function optimization. In Intelligent Control and Adaptive Systems. International Society for Optics and Photonics 1196:289–297

  52. Syswerda G (1989) Uniform crossover in genetic algorithms. In Proceedings of the third international conference on Genetic algorithms. Morgan Kaufmann Publishers, pp 2–9

  53. Ono I, Kita H, Kobayashi S (2003) A real-coded genetic algorithm using the unimodal normal distribution crossover. In Advances in Evolutionary Computing. Springer, pp 213–237

  54. Ono I, Kita H, Kobayashi S (1999) A robust real-coded genetic algorithm using unimodal normal distribution crossover augmented by uniform crossover: effects of self-adaptation of crossover probabilities. In Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation-Volume 1. Morgan Kaufmann Publishers Inc., pp 496–503

  55. Deb K, Agrawal RB (1995) Simulated binary crossover for continuous search space. Complex Systems 9(2):115–148

    Google Scholar 

  56. Sánchez AM, Lozano M, Villar P, Herrera F (2009) Hybrid crossover operators with multiple descendents for real-coded genetic algorithms: Combining neighborhood-based crossover operators. Int J Intell Syst 24(5):540–567

  57. Eshelman LJ, Schaffer JD (1993) Real-coded genetic algorithms and interval-schemata. In Foundations of genetic algorithms. Elsevier, vol 2, pp 187–202

  58. Takahashi M, Kita H (2001) A crossover operator using independent component analysis for real-coded genetic algorithms. In Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546), volume 1, pages 643–649

  59. Munteanu C, Lazarescu V (1999) Improving mutation capabilities in a real-coded genetic algorithm. In Workshops on Applications of Evolutionary Computation. Springer, pp 138–149

  60. Korejo I, Yang S, Li C (2010) A directed mutation operator for real coded genetic algorithms. In European Conference on the Applications of Evolutionary Computation. Springer, pp 491–500

  61. Srinivas N, Deb K (1994) Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248

    Article  Google Scholar 

  62. Rechenberg I (1965) Cybernetic solution path of an experimental problem. In Royal Aircraft Establishment Library Translation

  63. Schwefel HP (1981) Numerical Optimization of Computer Models. John Wiley & Sons Inc, New York, NY, USA

    Google Scholar 

  64. Fogel DB (1991) System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling. Ginn Press

  65. Fogel DB (1992) Evolving artificial intelligence. Doctoral Dissertation

  66. Fogel DB (1993) Applying evolutionary programming to selected traveling salesman problems. Cybern Syst 24(1):27–36

  67. Fogel DB (2006) Evolutionary computation: toward a new philosophy of machine intelligence. John Wiley & Sons, vol 1

  68. Yao X, Liu Y (1996) Fast evolutionary programming. Evolutionary Programming 3:451–460

    Google Scholar 

  69. Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102

    Article  Google Scholar 

  70. Lee CY, Yao X (2004) Evolutionary programming using mutations based on the lévy probability distribution. IEEE Trans Evol Comput 8(1):1–13

    Article  Google Scholar 

  71. Fogel DB (1997) The advantages of evolutionary computation. In BCEC p 1–11

  72. Wieland AP (1991) Evolving controls for unstable systems. In Connectionist Models. Elsevier, pp 91–102

  73. Schwefel HP (2000) Advantages (and disadvantages) of evolutionary computation over other approaches. Evol Comput 1:20–22

    Google Scholar 

  74. Dimopoulos C, Zalzala AMS (2000) Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons. IEEE Trans Evol Comput 4(2):93–113

    Article  Google Scholar 

  75. Simpson AR, Dandy GC, Murphy LJ (1994) Genetic algorithms compared to other techniques for pipe optimization. J Water Resour Plan Manag 120(4):423–443

  76. Chu PC, Beasley JE (1997) A genetic algorithm for the generalised assignment problem. Comput Oper Res 24(1):17–23

  77. Alba E, Troya JM et al (1999) A survey of parallel distributed genetic algorithms. Complexity 4(4):31–52

  78. Reddy GT, Reddy MP, Lakshmanna K, Rajput DS, Kaluri R, Srivastava G (2020) Hybrid genetic algorithm and a fuzzy logic classifier for heart disease diagnosis. Evol Intell 13(2):185–196

  79. Zhou Y, Wang Y, Wang K, Kang L, Peng F, Wang L, Pang J (2020) Hybrid genetic algorithm method for efficient and robust evaluation of remaining useful life of supercapacitors. Appl Energy 260:114169

    Article  Google Scholar 

  80. Back T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press

  81. Moslemipour G, Lee TS, Rilling D (2012) A review of intelligent approaches for designing dynamic and robust layouts in flexible manufacturing systems. Int J Adv Manuf Technol 60(1-4):11–27

  82. Leung Y, Gao Y, Zong-Ben X (1997) Degree of population diversity-a perspective on premature convergence in genetic algorithms and its markov chain analysis. IEEE Trans Neural Netw 8(5):1165–1176

    Article  Google Scholar 

  83. Hrstka O, Kučerová A (2004) Improvements of real coded genetic algorithms based on differential operators preventing premature convergence. Adv Eng Softw 35(3–4):237–246

    Article  Google Scholar 

  84. Fogel DB (1995) A comparison of evolutionary programming and genetic algorithms on selected constrained optimization problems. Simulation 64(6):397–404

  85. Swayamsiddha S, Thethi HP. Nonlinear system identification using evolutionary computing based training schemes. Int J Comput Appl 975:8887

  86. Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  Google Scholar 

  87. Li J, Aickelin U (2003) A bayesian optimization algorithm for the nurse scheduling problem. In The 2003 Congress on Evolutionary Computation, 2003. CEC’03., vol 3, pp 2149–2156

  88. Larranaga P, Kuijpers CMH, Murga RH, Inza I, Dizdarevic S (1999) Genetic algorithms for the travelling salesman problem: A review of representations and operators. Artif Intell Rev 13(2):129–170

  89. Hussain A, Muhammad YS, Sajid MN, Hussain I, Shoukry AM, Gani S (2017) Genetic algorithm for traveling salesman problem with modified cycle crossover operator. Comput Intell Neurosci

  90. Davis L (1985) Job shop scheduling with genetic algorithms. In Proceedings of an international conference on genetic algorithms and their applications, vol 140

  91. Chan H, Mazumder P, Shahookar K (1991) Macro-cell and module placement by genetic adaptive search with bitmap-represented chromosome. VLSI, 12(1)

  92. Boudjelaba K, Ros F, Chikouche D (2014) An efficient hybrid genetic algorithm to design finite impulse response filters. Expert Systems with Applications 41(13):5917–5937

    Article  Google Scholar 

  93. Karaboga N, Cetinkaya B (2006) Design of digital fir filters using differential evolution algorithm. Circuits Systems Signal Process 25(5):649–660

    Article  Google Scholar 

  94. Karaboga N (2005) Digital iir filter design using differential evolution algorithm. EURASIP Journal on Applied Signal Processing 1269–1276:2005

    Google Scholar 

  95. Storn S (1996) Differential evolution design of an iir-filter. In Proceedings of IEEE international conference on evolutionary computation. IEEE, pp 268–273

  96. Dasgupta D, Michalewicz Z (2013) Evolutionary algorithms in engineering applications. Springer Science & Business Media

  97. Man KF, Tang KS, Kwong Sam (1996) Genetic algorithms: concepts and applications [in engineering design]. IEEE Trans Ind Electron 43(5):519–534

    Article  Google Scholar 

  98. Bhoskar MT, Kulkarni OK, Kulkarni NK, Patekar SL, Kakandikar GM, Nandedkar VM (2015) Genetic algorithm and its applications to mechanical engineering: A review. Materials Today: Proceedings 2(4-5):2624–2630

  99. Hatanaka T, Uosaki K, Yamada Y (1997) Evolutionary approach to system identification. IFAC Proceedings Volumes 30(11):1327–1332

    Article  Google Scholar 

  100. Fahmi M, Samad A (2014) Evolutionary computation in system identification: Review and recommendations. Int J Autom Control pp 208–216

  101. Lewin DR (2005) Evolutionary algorithms in control system engineering. IFAC Proceedings Volumes 38(1):45–50

  102. Fleming PJ, Purshouse RC (2002) Evolutionary algorithms in control systems engineering: a survey. Control Eng Pract 10(11):1223–1241

  103. Alcalá-Fdez J, Sanchez L, Garcia S, del Jesus MJ, Ventura S, Garrell JM, Otero J, Romero C, Bacardit J, Rivas VM et al (2009) Keel: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13(3):307–318

  104. Bounsaythip C, Alander JT (1997) Genetic algorithms in image processing-a review. In Proceedings of the Third Nordic Workshop on Genetic Algorithms and their Applications (3NWGA) pp 173–192

  105. Paulinas M, Ušinskas A (2007) A survey of genetic algorithms applications for image enhancement and segmentation. Inf Tech Control 36(3)

  106. Omran MG, Engelbrecht AP, Salman A (2005) Differential evolution methods for unsupervised image classification. In 2005 IEEE Congress on Evolutionary Computation. IEEE 2:966–973

  107. Bonabeau E, Marco DD, Dorigo M, Theraulaz G et al (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, vol 1

  108. 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. IEEE pp 39–43

  109. Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In Evolutionary computation, 1999. CEC 99. Proceedings of the 1999 congress on. IEEE 3:1945–1950

  110. Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on. IEEE pp 69–73

  111. Zheng YL, Ma LH, Zhang LY, Qian JX (2003) Empirical study of particle swarm optimizer with an increasing inertia weight. In Evolutionary Computation, 2003. CEC’03. The 2003 Congress on. IEEE 1:221–226

  112. Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73

    Article  Google Scholar 

  113. Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8(3):204–210

    Article  Google Scholar 

  114. Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255

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

  116. Mohapatra P, Das KN, Roy S (2019) Inherited competitive swarm optimizer for large-scale optimization problems. In Harmony Search and Nature Inspired Optimization Algorithms. Springer, pp 85–95

  117. Duan H, Huang L (2014) Imperialist competitive algorithm optimized artificial neural networks for ucav global path planning. Neurocomputing 125:166–171

    Article  Google Scholar 

  118. Ahmed H, Glasgow J (2012) Swarm intelligence: concepts, models and applications. Queens University Technical Report, School Of Computing

    Google Scholar 

  119. Olariu S, Zomaya AY (2005) Handbook of bioinspired algorithms and applications. Chapman and Hall/CRC

  120. Elbeltagi E, Hegazy T, Grierson D (2005) Comparison among five evolutionary-based optimization algorithms. Adv Eng Inform 19(1):43–53

    Article  Google Scholar 

  121. Hassan R, Cohanim B, De Weck O, Venter G (2005) A comparison of particle swarm optimization and the genetic algorithm. In 46th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference p 1897

  122. Rahmat-Samii Y (2003) Genetic algorithm (ga) and particle swarm optimization (pso) in engineering electromagnetics. In 17th International Conference on Applied Electromagnetics and Communications. ICECom IEEE, pp 1–5

  123. Diaz L, Milligan TA (1996) Antenna Engineering Using Physical Optics: Practical CAD Techniques and Software, 1st edn. Artech House Inc, Norwood, MA, USA

    Google Scholar 

  124. Afandie WN, Rahman TK, Zakaria Z (2016) Comparative analysis of bacterial foraging optimization algorithm and evolutionary programming for load shedding in power system. Int J Simul Syst Sci Technol 17(41)

  125. Alsariera YA, Alamri HS, Nasser AM, Majid MA, Zamli KZ (2014) Comparative performance analysis of bat algorithm and bacterial foraging optimization algorithm using standard benchmark functions. In 2014 8th. Malaysian Software Engineering Conference (MySEC). IEEE, pp 295–300

  126. Kamalanand K, Jawahar PM (2016) Comparison of particle swarm and bacterial foraging optimization algorithms for therapy planning in hiv/aids patients. Int J Biomath 9(02):1650024

  127. Ji X, Gao Q, Yin F, Guo H (2016) An efficient imperialist competitive algorithm for solving the qfd decision problem. Math Probl Eng

  128. Huang C, Li Y, Yao X (2019) A survey of automatic parameter tuning methods for metaheuristics. IEEE Trans Evol Comput 24(2):201–216

    Article  Google Scholar 

  129. Birattari M, Stützle T, Paquete L, Varrentrapp K et al (2002) A racing algorithm for configuring metaheuristics. In Gecco, vol 2

  130. Yuan B, Gallagher M (2004) Statistical racing techniques for improved empirical evaluation of evolutionary algorithms. In International Conference on Parallel Problem Solving from Nature. Springer, pp 172–181

  131. Chen L, Xu X, Chen YX (2004) An adaptive ant colony clustering algorithm. In Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No. 04EX826), vol 3, pp 1387–1392

  132. Chen H, Zhu Y, Hu K (2011) Adaptive bacterial foraging optimization. In Abstract and Applied Analysis. Hindawi, vol 2011

  133. Dasgupta S, Das S, Abraham A, Biswas A (2009) Adaptive computational chemotaxis in bacterial foraging optimization: an analysis. IEEE Trans Evol Comput 13(4):919–941

    Article  Google Scholar 

  134. Shi Y, Eberhart RC (2001) Fuzzy adaptive particle swarm optimization. In Proceedings of the 2001 congress on evolutionary computation (IEEE Cat. No. 01TH8546), vol 1, pp 101–106

  135. Bäck T (2001) Introduction to the special issue: Self-adaptation. Evol Comput 9(2):3–4

    Article  Google Scholar 

  136. Clerc M (2006) Stagnation analysis in particle swarm optimisation or what happens when nothing happens. Tech Rep

  137. Bouhouch A, Loqman C, Bennis H, El Qadi A (2019) A comparative study of chn-mnc, ga and pso for solving constraints satisfaction problems. In Third International Conference on Computing and Wireless Communication Systems, ICCWCS 2019. European Alliance for Innovation (EAI)

  138. Abdi Y, Lak M, Seyfari Y (2017) Gica: Imperialist competitive algorithm with globalization mechanism for optimization problems. Turk J Electr Eng Comput Sci 25(1):209–221

  139. Zhou W, Yan J, Li Y, Xia C, Zheng J (2013) Imperialist competitive algorithm for assembly sequence planning. Int J Adv Manuf Technol 67(9–12):2207–2216

    Article  Google Scholar 

  140. Vijay R (2012) Intelligent bacterial foraging optimization technique to economic load dispatch problem. International Journal of Soft Computing and Engineering (IJSCE) 2(2):2231–2307

    Google Scholar 

  141. Sharvani GS, Ananth AG, Rangaswamy TM (2012) Analysis of different pheromone decay techniques for aco based routing in ad hoc wireless networks. Int J Comput Appl 56(2)

  142. Jagadeesh S, Sugumar R (2017) A comparative study on artificial bee colony with modified abc algorithm. European Journal of Applied Sciences 9(5):243–248

  143. Zhou Z, Peng Z, Cui JH, Shi Z (2010) Efficient multipath communication for time-critical applications in underwater acoustic sensor networks. IEEE/ACM Trans Networking 19(1):28–41

    Article  Google Scholar 

  144. Pal NS, Sharma S (2013) Robot path planning using swarm intelligence: A survey. Int J Comput Appl 83(12):5–12

    Google Scholar 

  145. Fornarelli G (2012) Swarm intelligence for electric and electronic engineering. IGI Global

  146. Ming L, Hai H, Aimin Z, Yingde S, Zhao L, Xingguo Z (2012) Modeling of mechanical properties of as-cast mg-li-al alloys based on pso-bp algorithm. China Foundry 9(2)

  147. Mohan SC, Maiti DK, Maity D (2013) Structural damage assessment using frf employing particle swarm optimization. Appl Math Comput 219(20):10387–10400

  148. Lu P, Chen S, Zheng Y (2012) Artificial intelligence in civil engineering. Math Probl Eng

  149. Omran MGH et al (2004) Particle swarm optimization methods for pattern recognition and image processing. PhD thesis, Citeseer

  150. Poli R (2007) An analysis of publications on particle swarm optimization applications. Department of Computer Science, University of Essex, Essex, UK

    Google Scholar 

  151. Saraswathi S, Sundaram S, Sundararajan N, Zimmermann M, Nilsen-Hamilton M (2011) Icga-pso-elm approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) 8(2):452–463

    Article  Google Scholar 

  152. Xu R, Cai X, Wunsch DC (2006) Gene expression data for dlbcl cancer survival prediction with a combination of machine learning technologies. In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference pp 894–897

  153. Mansour N, Kanj F, Khachfe H (2012) Particle swarm optimization approach for protein structure prediction in the 3d hp model. Interdisciplinary Sciences: Computational Life Sciences 4(3):190–200

    Google Scholar 

  154. Karabulut M, Ibrikci T (2012) A bayesian scoring scheme based particle swarm optimization algorithm to identify transcription factor binding sites. Appl Soft Comput 12(9):2846–2855

    Article  Google Scholar 

  155. Cedefto W, Agraflotis D (2005) Particle swarms for drug design. In 2005 IEEE Congress on Evolutionary Computation, vol 2, pp 1218–1225

  156. Yongqiang H, Wentao L, Xiaohui L (2013) Particle swarm optimization for antenna selection in mimo system. Wirel Pers Commun 68(3):1013–1029

    Article  Google Scholar 

  157. Chiu CC, Ho MH, Liao S (2013) Pso and apso for optimizing coverage in indoor uwb communication system. Int J RF Microwave Comput Aided Eng 23(3):300–308

    Article  Google Scholar 

  158. Kim YG, Lee MJ (2014) Scheduling multi-channel and multi-timeslot in time constrained wireless sensor networks via simulated annealing and particle swarm optimization. IEEE Commun Mag 52(1):122–129

  159. Das G, Pattnaik PK, Padhy SK (2014) Artificial neural network trained by particle swarm optimization for non-linear channel equalization. Expert Systems with Applications 41(7):3491–3496

  160. Goldansaz SM, Jolai F, Anaraki AHZ (2013) A hybrid imperialist competitive algorithm for minimizing makespan in a multi-processor open shop. Appl Math Model 37(23):9603–9616

  161. Lucas C, Nasiri-Gheidari Z, Tootoonchian F (2010) Application of an imperialist competitive algorithm to the design of a linear induction motor. Energy Convers Manag 51(7):1407–1411

    Article  Google Scholar 

  162. Kaveh A, Talatahari S (2010) Optimum design of skeletal structures using imperialist competitive algorithm. Comput Struct 88(21–22):1220–1229

    Article  Google Scholar 

  163. Duan H, Chunfang X, Liu S, Shao S (2010) Template matching using chaotic imperialist competitive algorithm. Pattern Recogn Lett 31(13):1868–1875

    Article  Google Scholar 

  164. Nazari-Shirkouhi S, Eivazy H, Ghodsi R, Rezaie K, Atashpaz-Gargari E (2010) Solving the integrated product mix-outsourcing problem using the imperialist competitive algorithm. Expert Systems with Applications 37(12):7615–7626

  165. Biabangard-Oskouyi A, Atashpaz-Gargari E, Soltani N, Lucas C (2009) Application of imperialist competitive algorithm for materials property characterization from sharp indentation test. International Journal of Engineering Simulation 10(1):11–12

    Google Scholar 

  166. Rajabioun R, Hashemzadeh F, Atashpaz-Gargari E, Mesgari B, Rajaei Salmasi F (2008) Identification of a mimo evaporator and its decentralized pid controller tuning using colonial competitive algorithm. In be presented in IFAC World Congress

  167. Forouharfard S, Zandieh M (2010) An imperialist competitive algorithm to schedule of receiving and shipping trucks in cross-docking systems. Int J Adv Manuf Technol 51(9-12):1179–1193

  168. Alba E, Chicano JF (2006) Evolutionary algorithms in telecommunications. In MELECON 2006-2006 IEEE Mediterranean Electrotechnical Conference, pp 795–798

  169. Veeramachaneni K, Peram T, Mohan C, Osadciw LA (2003) Optimization using particle swarms with near neighbor interactions. In Genetic and Evolutionary Computation Conference. Springer, pp 110–121

  170. Chaimatanan S, Delahaye D, Mongeau M (2014) A hybrid metaheuristic optimization algorithm for strategic planning of 4d aircraft trajectories at the continental scale. IEEE Comput Intell Mag 9(4):46–61

    Article  Google Scholar 

  171. Flores SD, Cegla BB, Cáceres DB (2003) Telecommunication network design with parallel multi-objective evolutionary algorithms. LANC 3:3–5

  172. Fogel DB (2000) Evolutionary computation: principles and practice for signal processing. SPIE Press, vol 43

  173. Fogel DB, Fogel LJ, Atmar JW (1991) Meta-evolutionary programming. In [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers. pp 540–545

  174. Higashi N, Iba H (2003) Particle swarm optimization with gaussian mutation. In Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS’03 (Cat. No. 03EX706). IEEE, pp 72–79

  175. Ilonen J, Kamarainen JK, Lampinen J (2003) Differential evolution training algorithm for feed-forward neural networks. Neural Process Lett 17(1):93–105

    Article  Google Scholar 

  176. Miller JF, Job D, Vassilev VK (2000) Principles in the evolutionary design of digital circuits–part i. Genet Program Evolvable Mach 1(1-2):7–35

  177. Wong DF, Leong HW, Liu HW (2012) Simulated annealing for VLSI design. Springer Science & Business Media, vol 42

  178. Yao X (1999) Evolving artificial neural networks. Proceedings of the IEEE 87(9):1423–1447

    Article  Google Scholar 

  179. Yao X, Liu Y (1997) A new evolutionary system for evolving artificial neural networks. IEEE Trans Neural Netw 8(3):694–713

    Article  Google Scholar 

  180. Zebulum RS, Pacheco MA, Be Vellasco MM (2018) Evolutionary electronics: automatic design of electronic circuits and systems by genetic algorithms. CRC Press

  181. Barr RS, Golden BL, Kelly JP, Resende MGC, Stewart WR (1995) Designing and reporting on computational experiments with heuristic methods. J Heuristics 1(1):9–32

  182. Hooker JN (1995) Testing heuristics: We have it all wrong. J Heuristics 1(1):33–42

  183. Tufte ER (2001) The visual display of quantitative information. Graphics press Cheshire, CT, vol 2

  184. Chiarandini M, Paquete L, Preuss M, Ridge E (2007) Experiments on metaheuristics: Methodological overview and open issues. Tech Rep DMF-2007-03-003

  185. Clerc M (1999) The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406). IEEE, vol 3, pp 1951–1957

  186. Birattari M, Kacprzyk J (2009) Tuning metaheuristics: a machine learning perspective. Springer, vol 197

  187. Blum C, Puchinger J, Raidl GR, Roli A (2011) Hybrid metaheuristics in combinatorial optimization: A survey. Appl Soft Comput 11(6):4135–4151

  188. Sörensen K (2015) Metaheuristics–the metaphor exposed. Int Trans Oper Res 22(1), 3–18

  189. Burke EK, Curtois T, Kendall G, Hyde M, Ochoa G, Vazquez-Rodriguez JA (2009) Towards the decathlon challenge of search heuristics. In Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers. pp 2205–2208

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Regina, 3737 Wascana Parkway, SK, S4S0A2, Regina, Canada

    Wael Korani & Malek Mouhoub

Authors
  1. Wael Korani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Malek Mouhoub
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Malek Mouhoub.

Ethics declarations

Conflicts of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interestOn behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Korani, W., Mouhoub, M. Review on Nature-Inspired Algorithms. SN Oper. Res. Forum 2, 36 (2021). https://doi.org/10.1007/s43069-021-00068-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s43069-021-00068-x

Keywords

Search

Navigation