Advertisement

Future search algorithm for optimization

  • M. Elsisi
Research Paper
  • 39 Downloads

Abstract

This paper proposes a new optimization algorithm named future search algorithm (FSA). This algorithm mimics the person’s life. People in the world search for the best life. If any person found that his life is not good, he tries to change it and he imitates the successful persons. According to this behavior, this algorithm is built by mathematical equations. The FSA can update the random initial. Furthermore, it uses the local search between people and the global search between the histories optimal persons to achieve the best solutions. The proposed algorithm does not have tuned parameters. In addition, it has low computational complexity, fast convergence, and high local optima avoidance. The performance of the proposed algorithm is evaluated by applying it to solve some benchmarks test functions. These test functions have various characteristics necessary to evaluate the FSA. In addition, the performance of the proposed algorithm is compared with five other well-known methods. The results confirm a better performance of the proposed algorithm to get the optimal solution with fewer iterations number than other methods.

Keywords

Future search algorithm (FSA) Benchmark functions Constrained optimization Meta-heuristic algorithms 

Notes

Compliance with ethical standards

Conflict of interest

Authors state that there are no conflicts of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

References

  1. 1.
    Mitchell M (1998) An introduction to genetic algorithms. MIT, LondonzbMATHGoogle Scholar
  2. 2.
    Basnet C, Weintraub A (2009) A genetic algorithm for a bicriteria supplier selection problem. Int Trans Oper Res 16(2):173–187.‏CrossRefGoogle Scholar
  3. 3.
    Baker BM, Ayechew MA (2003) A genetic algorithm for the vehicle routing problem. Comput Oper Res 30(5):787–800.‏MathSciNetCrossRefGoogle Scholar
  4. 4.
    Gehring H, Bortfeldt A (1997) A genetic algorithm for solving the container loading problem. Int Trans Oper Res 4(5-6):401–418.‏CrossRefGoogle Scholar
  5. 5.
    Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57CrossRefGoogle Scholar
  6. 6.
    Van den Bergh F, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evolut Comput 8(3):225–239.‏CrossRefGoogle Scholar
  7. 7.
    Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst 22(3):52–67MathSciNetCrossRefGoogle Scholar
  8. 8.
    Das S, Biswas A, Dasgupta S, Abraham A (2009) Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications. In: Foundations of computational intelligence, vol 3. Springer, Berlin, pp 23–55Google Scholar
  9. 9.
    Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132.‏MathSciNetzbMATHGoogle Scholar
  10. 10.
    Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697.‏‏CrossRefGoogle Scholar
  11. 11.
    Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173.‏MathSciNetzbMATHGoogle Scholar
  12. 12.
    Montané FAT, Galvao RD (2006) A tabu search algorithm for the vehicle routing problem with simultaneous pick-up and delivery service. Comput Oper Res 33(3):595–619MathSciNetCrossRefGoogle Scholar
  13. 13.
    Grabowski J, Wodecki M (2004) A very fast tabu search algorithm for the permutation flow shop problem with makespan criterion. Comput Oper Res 31(11):1891–1909MathSciNetCrossRefGoogle Scholar
  14. 14.
    Brandão J (2011) A tabu search algorithm for the heterogeneous fixed fleet vehicle routing problem. Comput Oper Res 38(1):140–151.‏MathSciNetCrossRefGoogle Scholar
  15. 15.
    Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In Evolutionary computation, 2007. CEC 2007. IEEE congress on. IEEE, pp 4661–4667Google Scholar
  16. 16.
    Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248.‏CrossRefGoogle Scholar
  17. 17.
    Xing B, Gao WJ (2014) Gravitational search algorithm. In: Innovative computational intelligence: a rough guide to 134 clever algorithms. Springer, Cham‏, pp 355–364CrossRefGoogle Scholar
  18. 18.
    Sharafi Y, Khanesar MA, Teshnehlab M (2016) COOA: competitive optimization algorithm. Swarm Evolut Comput 30:39–63CrossRefGoogle Scholar
  19. 19.
    Shareef H, Ibrahim AA, Mutlag AH (2015) Lightning search algorithm. Appl Soft Comput 36:315–333CrossRefGoogle Scholar
  20. 20.
    Lin D, He L, Feng X, Luo W (2018) Niching pareto ant colony optimization algorithm for bi-objective pathfinding problem. IEEE Access 6:21184–21194CrossRefGoogle Scholar
  21. 21.
    Saremi S, Mirjalili S, Lewis A (2017) Grasshopper optimisation algorithm: theory and application. Adv Eng Softw 105:30–47CrossRefGoogle Scholar
  22. 22.
    Xia X, Gui L, He G, Xie C, Wei B, Xing Y et al (2018) A hybrid optimizer based on firefly algorithm and particle swarm optimization algorithm. J Comput Sci 26:488–500CrossRefGoogle Scholar
  23. 23.
    Brabazon A, Cui W, O’Neill M (2016) The raven roosting optimisation algorithm. Soft Comput 20(2):525–545.‏‏CrossRefGoogle Scholar
  24. 24.
    Yazdani M, Jolai F (2016) Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm. J Comput Des Eng 3(1):24–36.‏Google Scholar
  25. 25.
    Yao X, Liu Y, Lin G (1999) Evolutionary programming made faster. IEEE Trans Evolut Comput 3(2):82–102.‏CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Electrical Engineering Department, Faculty of Engineering (Shoubra)Benha UniversityCairoEgypt

Personalised recommendations