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

Hari Pandey

Research output: Contribution to journalArticle (journal)peer-review

6 Citations (Scopus)
70 Downloads (Pure)


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
Early online date7 Sept 2017
Publication statusE-pub ahead of print - 7 Sept 2017


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


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