A Hybrid CS–GSA Algorithm for Optimization

  • Manoj Kumar Naik
  • Leena Samantaray
  • Rutuparna Panda
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 611)

Abstract

The chapter presents a hybridized population-based Cuckoo search–Gravitational search algorithm (CS–GSA) for optimization. The central idea of this chapter is to increase the exploration capability of the Gravitational search algorithm in the Cuckoo search (CS) algorithm. The CS algorithm is common for its exploitation conduct. The other motivation behind this proposal is to obtain a quicker and stable solution. Twenty-three different kinds of standard test functions are considered here to compare the performance of our hybridized algorithm with both the CS and the GSA methods. Extensive simulation-based results are presented in the results section to show that the proposed algorithm outperforms both CS and GSA algorithms. We land up with a faster convergence than the CS and the GSA algorithms. Thus, best solutions are found with significantly less number of function evaluations. This chapter also explains how to handle the constrained optimization problems with suitable examples.

Keywords

Cuckoo search Gravitational search algorithm Optimization 

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Copyright information

© Springer India 2016

Authors and Affiliations

  • Manoj Kumar Naik
    • 1
  • Leena Samantaray
    • 2
  • Rutuparna Panda
    • 3
  1. 1.Department of Electronics & Instrumentation Engineering, Institute of Technical Education and ResearchSiksha ‘O’ Anusandhan UniversityBhubaneswarIndia
  2. 2.Department of Electronics & Instrumentation EngineeringAjaya Binaya Institute of TechnologyCuttackIndia
  3. 3.Department of Electronics & Telecommunication EngineeringVSS University of TechnologyBurlaIndia

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