Performance Review of Harmony Search,Differential Evolution and Particle Swarm Optimization

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Abstract

Metaheuristic algorithms are effective in the design of an intelligent system. These algorithms are widely applied to solve complex optimization problems, including image processing, big data analytics, language processing, pattern recognition and others. This paper presents a performance comparison of three meta-heuristic algorithms, namely Harmony Search, Differential Evolution, and Particle Swarm Optimization. These algorithms are originated altogether from different fields of meta-heuristics yet share a common objective. The standard benchmark functions are used for the simulation. Statistical tests are conducted to derive a conclusion on the performance. The key motivation to conduct this research is to categorize the computational capabilities, which might be useful to the researchers.
Original languageEnglish
JournalIOP Conference Series: Materials Science and Engineering
Volume225
Early online date7 Sep 2017
DOIs
Publication statusE-pub ahead of print - 7 Sep 2017

Keywords

  • Differential Evolution
  • Harmony Search
  • Optimization
  • Particle Swarm Optimization.

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