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An enhanced associative learning-based exploratory whale optimizer for global optimization

  • Ali Asghar HeidariEmail author
  • Ibrahim Aljarah
  • Hossam Faris
  • Huiling Chen
  • Jie Luo
  • Seyedali Mirjalili
Original Article
  • 66 Downloads

Abstract

Whale optimization algorithm (WOA) is a recent nature-inspired metaheuristic that mimics the cooperative life of humpback whales and their spiral-shaped hunting mechanism. In this research, it is first argued that the exploitation tendency of WOA is limited and can be considered as one of the main drawbacks of this algorithm. In order to mitigate the problems of immature convergence and stagnation problems, the exploitative and exploratory capabilities of modified WOA in conjunction with a learning mechanism are improved. In this regard, the proposed WOA with associative learning approaches is combined with a recent variant of hill climbing local search to further enhance the exploitation process. The improved algorithm is then employed to tackle a wide range of numerical optimization problems. The results are compared with different well-known and novel techniques on multi-dimensional classic problems and new CEC 2017 test suite. The extensive experiments and statistical tests show the superiority of the proposed BMWOA compared to WOA and several well-established algorithms.

Keywords

Nature-inspired computing Metaheuristic Optimization Swarm intelligence 

Notes

Compliance with ethical standards

Ethical standard

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

Conflict of interest

There is no conflict of interest to declare.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Surveying and Geospatial EngineeringUniversity of TehranTehranIran
  2. 2.Business Information Technology Department, King Abdullah II School for Information TechnologyThe University of JordanAmmanJordan
  3. 3.Department of Computer ScienceWenzhou UniversityWenzhouChina
  4. 4.Institute of Integrated and Intelligent SystemsGriffith UniversityNathan, BrisbaneAustralia

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