Advertisement

Adaptive Krill Herd Algorithm for Global Numerical Optimization

  • Indrajit N. TrivediEmail author
  • Amir H. Gandomi
  • Pradeep Jangir
  • Arvind Kumar
  • Narottam Jangir
  • Rahul Totlani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 553)

Abstract

A recent bio-inspired optimization algorithm, that is, based on the Lagrangian and evolutionary behavior of krill individuals in nature is called the Krill Herd (KH) Algorithm. Randomization has a key role in both exploration and exploitation of a problem using KH algorithm. A new randomization technique termed adaptive technique is integrated with Krill Herd algorithm and tested on several global numerical functions. The KH uses Lagrangian movement which includes induced movement, random diffusion, and foraging motion, and therefore, it covers a vast area in the exploration phase. And then adding the powerful adaptive randomization technique potent the adaptive KH (AKH) algorithm to attain global optimal solution with faster convergence as well as less parameter dependency. The proposed AKH outperforms the standard KH in terms of both statistical results and best solution.

Keywords

Meta-heuristic Krill Herd algorithm Adaptive Krill Herd Numerical optimization Benchmark function 

References

  1. 1.
    A.H. Gandomi, A.H. Alavi, Krill Herd: a new bio-inspired optimization algorithm, Common Nonlinear Sci. Numer. Simul. 17 (12) (2012) 4831–4845.Google Scholar
  2. 2.
    Das, S. Mandal, A. Mukherjee, R. An Adaptive Differential Evolution Algorithm for Global Optimization in Dynamic Environments. IEEE Transactions on Cybernetics, 44, 6, 966–978, 2014.Google Scholar
  3. 3.
    Costa L, Oliveira P, An Adaptive Sharing Elitist Evolution Strategy for Multiobjective Optimization. Evolutionary Computation, 2003, 11, 4, 417–438.Google Scholar
  4. 4.
    Dai Y, Li Y; Wei L; Wang J; Zheng D. Adaptive immune-genetic algorithm for global optimization to multivariable function. Journal of Systems Engineering and Electronics, 18, 3, 655–660, 2007.Google Scholar
  5. 5.
    Lim WH, Isa NAM, An adaptive two-layer particle swarm optimization with elitist learning strategy. Information Sciences, 273, 49–72, 2014.Google Scholar
  6. 6.
    P. Ong, “Adaptive Cuckoo search algorithm for unconstrained optimization,” The Scientific World Journal, Hindawi Publication, vol. 2014, pp. 1–8, 2014.Google Scholar
  7. 7.
    Naik MK, Panda R, A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition, Applied Soft Computing, 38, 661–675, 2016.Google Scholar
  8. 8.
    A.H. Gandomi, X.S. Yang, S. Talatahari, A.H. Alavi, Metaheuristic Applications in Structures and Infrastructures, Elsevier, 2013.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Indrajit N. Trivedi
    • 1
    Email author
  • Amir H. Gandomi
    • 2
  • Pradeep Jangir
    • 3
  • Arvind Kumar
    • 4
  • Narottam Jangir
    • 3
  • Rahul Totlani
    • 5
  1. 1.Electrical Engineering DepartmentG.E. CollegeGandhinagarIndia
  2. 2.Department of Civil EngineeringBEACON Center for the Study of Evolution in Action, Michigan State UniversityEast LansingUSA
  3. 3.Electrical Engineering DepartmentLECMorbiIndia
  4. 4.Electrical Engineering DepartmentS.S.E.CBhavnagarIndia
  5. 5.Electrical Engineering DepartmentJECRCJaipurIndia

Personalised recommendations