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

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.

• Publication year

  2017

• Venue

  IOP Conference Series: Materials Science and Engineering

• Publication date

  2017-08-01

• Fields of study

  Mathematics, Physics, Computer Science

• Identifiers
  DOI 10.1088/1757-899X/225/1/012221
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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