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Introduction

  • Aziz OuaarabEmail author
Chapter
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Part of the Springer Tracts in Nature-Inspired Computing book series (STNIC)

Abstract

The need to optimize, plan, or make decisions in real time is everywhere, even in our daily lives. At all moments and situations, we are obliged to make a decision among many options. The problem is that sometimes our decision depends on a multitude of parameters and constraints, which makes the verification of all possible choices more difficult. Replacing the decision-making context of our daily lives by that of large companies and mega-industries makes gains and losses increase proportionally. Dealing with these optimization problems is done by using a variety of methods that perform different tools.

References

  1. Arora S, Barak B (2009) Computational complexity: a modern approach. Cambridge University PressGoogle Scholar
  2. Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv (CSUR) 35(3):268–308CrossRefGoogle Scholar
  3. Chen H, Li S, Tang Z (2011) Hybrid gravitational search algorithm with random-key encoding scheme combined with simulated annealing. Int J Comput Sci Netw Secur 11:208–217Google Scholar
  4. Debels D, De Reyck B, Leus R, Vanhoucke M (2006) A hybrid scatter search/electromagnetism meta-heuristic for project scheduling. Eur J Oper Res 169(2):638–653MathSciNetCrossRefGoogle Scholar
  5. Gandomi AH, Talatahari S, Yang X-S, Deb S (2012) Design optimization of truss structures using cuckoo search algorithm. In: The structural design of tall and special buildingsGoogle Scholar
  6. Gandomi AH, Yang X-S, Alavi AH (2011) Mixed variable structural optimization using firefly algorithm. Comput Struct 89(23–24):2325–2336CrossRefGoogle Scholar
  7. Gherboudj A, Layeb A, Chikhi S (2012) Solving 0–1 knapsack problems by a discrete binary version of cuckoo search algorithm. Int J Bio-Inspired Comput 4(4):229–236CrossRefGoogle Scholar
  8. Glover F, Kochenberger GA (2003) Handbook of metaheuristics. SpringerGoogle Scholar
  9. Gomez A, Gonzalez R, Parreo J, Pino R (2006) A particle swarm-based metaheuristic to solve the travelling salesman problem. In: International conference on artificial intelligence, pp 698–702Google Scholar
  10. Huilian F (2010) Discrete particle swarm optimization for TSP based on neighborhood. J Comput Inf Syst 6(10):3407–3414Google Scholar
  11. Jati GK et al (2011) Evolutionary discrete firefly algorithm for travelling salesman problem. SpringerGoogle Scholar
  12. Lin T-L, Horng S-J, Kao T-W, Chen Y-H, Run R-S, Chen R-J, Lai J-L, Kuo I-H (2010) An efficient job-shop scheduling algorithm based on particle swarm optimization. Expert Syst Appl 37(3):2629–2636CrossRefGoogle Scholar
  13. Luo Q, Zhou Y, Xie J, Ma M, Li L (2014) Discrete bat algorithm for optimal problem of permutation flow shop scheduling. Sci World JGoogle Scholar
  14. Marichelvam MK, Prabaharan T, Yang XS (2013) A discrete firefly algorithm for the multi-objective hybrid flowshop scheduling problems. IEEE Trans Evol Comput 18(2):301–305CrossRefGoogle Scholar
  15. Osaba E, Yang X-S, Diaz F, Lopez-Garcia P, Carballedo R (2016) An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems. Eng Appl Artif Intell 48:59–71CrossRefGoogle Scholar
  16. Osaba E, Yang X-S, Diaz F, Onieva E, Masegosa AD, Perallos A (2017) A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy. Soft Comput 21(18):5295–5308CrossRefGoogle Scholar
  17. Ouaarab A, Ahiod B, Yang X-S (2014a) Discrete cuckoo search algorithm for the travelling salesman problem. Neural Comput Appl 24(7–8):1659–1669Google Scholar
  18. Ouaarab A, Ahiod B, Yang X-S (2014b) Improved and discrete cuckoo search for solving the travelling salesman problem. In: Yang X-S (ed) Cuckoo search and firefly algorithm. Studies in computational intelligence, vol 516. Springer International Publishing, pp 63–84Google Scholar
  19. Ouaarab A, Ahiod B, Yang X-S, Abbad M (2014c) Discrete cuckoo search algorithm for job shop scheduling problem. In: 2014 IEEE international symposium on intelligent control (ISIC), pp 1872–1876Google Scholar
  20. Ouaarab A, Ahiod B, Yang X-S (2015a) Discrete cuckoo search applied to job shop scheduling problem. In: Yang X-S (ed) Recent advances in swarm intelligence and evolutionary computation. Studies in computational intelligence, vol 585. Springer International Publishing, pp 121–137Google Scholar
  21. Ouaarab A, Ahiod B, Yang X-S (2015b) Random-key cuckoo search for the travelling salesman problem. Soft Comput 19(4):1099–1106Google Scholar
  22. Ouaarab A, Ahiod B, Yang X-S, Abbad M (2015c) Discrete cuckoo search for the quadratic assignment problem. In: 11th metaheuristics international conferenceGoogle Scholar
  23. Ouaarab A, Ahiod B, Yang X-S, Abbad M (2015d) Random-key cuckoo search for the quadratic assignment problem (submitted). Nat ComputGoogle Scholar
  24. Ouyang X, Zhou Y, Luo Q, Chen H (2013) A novel discrete cuckoo search algorithm for spherical traveling salesman problem. Appl Math Inf Sci 7(2)Google Scholar
  25. Rao RV, Savsani VJ, Vakharia D (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315CrossRefGoogle Scholar
  26. Sayadi MK, Hafezalkotob A, Naini SGJ (2013) Firefly-inspired algorithm for discrete optimization problems: an application to manufacturing cell formation. J Manuf Syst 32(1):78–84CrossRefGoogle Scholar
  27. Stützle T, Hoos HH (2000) Max-min ant system. Futur Gener Comput Syst 16(8):889–914CrossRefGoogle Scholar
  28. Van Laarhoven PJ, Aarts EH (1987) Simulated annealing. SpringerGoogle Scholar
  29. Wang L, Pan J, Jiao L (2000) The immune algorithm. Acta Electron Sin 28(7):74–78Google Scholar
  30. Yang X-S (2009) Firefly algorithm, lvy flights and global optimization, pp 209–218Google Scholar
  31. Yang X-S, Cui Z, Xiao R, Gandomi AH, Karamanoglu M (2013) Swarm intelligence and bio-inspired computation: theory and applications. NewnesGoogle Scholar
  32. Yang XS, Deb S (2009) Cuckoo search via lévy flights. In: World congress on nature and biologically inspired computing, 2009. NaBIC 2009. IEEE, pp 210–214Google Scholar
  33. Yang X-S, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483CrossRefGoogle Scholar
  34. Zhong W, Zhang J, Chen W (2007) A novel discrete particle swarm optimization to solve traveling salesman problem. In: IEEE congress on evolutionary computation, 2007. CEC 2007. IEEE, pp 3283–3287Google Scholar
  35. Zhou X, Zhao X, Liu Y (2018) A multiobjective discrete bat algorithm for community detection in dynamic networks. Appl Intell 48(9):3081–3093CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Ecole Supérieure de Technologie d’EssaouiraEssaouiraMorocco

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