Exploration and Exploitation Measurement in Swarm-Based Metaheuristic Algorithms: An Empirical Analysis

  • Mohd Najib Mohd Salleh
  • Kashif Hussain
  • Shi Cheng
  • Yuhui Shi
  • Arshad Muhammad
  • Ghufran Ullah
  • Rashid Naseem
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 700)


Swarm-based metaheuristics, inspired from intelligent social behaviors in nature, have achieved wider acceptance among researchers as compared to other population-based methods. The success of any swarm-based algorithm highly depends upon the mechanism of social interaction which maintains the balance between exploration and exploitation. This research examines these two significant cornerstones of top five swarm-based metaheuristics using diversity measurement. The results show that ACO and FA maintained balance between exploration and exploitation throughout iterations thus achieved better results as compared to counterparts taken in this study.


Swarm intelligence Metaheuristics Optimization Exploration and exploitation 



The authors would like to thank Universiti Tun Hussein Onn Malaysia (UTHM), Malaysia for supporting this research under Postgraduate Incentive Research Grant, Vote No.U560.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Mohd Najib Mohd Salleh
    • 1
  • Kashif Hussain
    • 1
  • Shi Cheng
    • 2
  • Yuhui Shi
    • 3
  • Arshad Muhammad
    • 4
  • Ghufran Ullah
    • 5
  • Rashid Naseem
    • 5
  1. 1.Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein Onn MalaysiaBatu PahatMalaysia
  2. 2.School of Computer ScienceShaanxi Normal UniversityXianChina
  3. 3.Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhenChina
  4. 4.Faculty of Computing and Information TechnologySohar UniversitySoharOman
  5. 5.Department of Computer ScienceCity University of Science and Information TechnologyPeshawarPakistan

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