Research Article Open Access

Using the Cuckoo Search for Generating New Particles in Particle Swarm Optimization Algorithm

Fariaa Abdalmajeed Hameed1, Harith Raad Hasan1, Ahmed Abdullah Ahmed2 and Gulala Ali Hama Amin1
  • 1 Sulaimani Polytechnic University, Iraq
  • 2 Qaiwan International University (QIU) Raparin, Iraq
Journal of Computer Science
Volume 16 No. 4, 2020, 430-438

DOI: https://doi.org/10.3844/jcssp.2020.430.438

Submitted On: 27 January 2020 Published On: 30 April 2020

How to Cite: Hameed, F. A., Hasan, H. R., Ahmed, A. A. & Amin, G. A. H. (2020). Using the Cuckoo Search for Generating New Particles in Particle Swarm Optimization Algorithm. Journal of Computer Science, 16(4), 430-438. https://doi.org/10.3844/jcssp.2020.430.438

Abstract

This study is focused on as Cuckoo Search (CS), one of the current meta-heuristic optimization algorithm. The CS algorithm is useful in generating and searching for the most optimum particles of important meta-heuristic optimization algorithm, known as the Particle Swarm Optimization (PSO), to enhance its performance. This optimization is confirmed through a benchmark online optimization and actual problems. The PSO algorithm performance is also compared with differing algorithms representative of the area. The CS optimal solutions outperform alternative current solutions as CS has distinct search features. The study findings have implications for future studies and practice.

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Keywords

  • Optimization
  • Cuckoo Search (CS)
  • Particle Swarm Optimization