Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm

Weiguo Zhao, Liying Wang, Zhenxing Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

A novel nature-inspired meta-heuristic optimization algorithm, named artificial ecosystem-based optimization (AEO), is presented in this paper. AEO is a population-based optimizer motivated from the flow of energy in an ecosystem on the earth, and this algorithm mimics three unique behaviors of living organisms, including production, consumption, and decomposition. AEO is tested on thirty-one mathematical benchmark functions and eight real-world engineering design problems. The overall comparisons suggest that the optimization performance of AEO outperforms that of other state-of-the-art counterparts. Especially for real-world engineering problems, AEO is more competitive than other reported methods in terms of both convergence rate and computational efforts. The applications of AEO to the field of identification of hydrogeological parameters are also considered in this study to further evaluate its effectiveness in practice, demonstrating its potential in tackling challenging problems with difficulty and unknown search space. The codes are available at https://www.mathworks.com/matlabcentral/fileexchange/72685-artificial-ecosystem-based-optimization-aeo.

Original languageEnglish (US)
JournalNeural Computing and Applications
DOIs
StateAccepted/In press - Jan 1 2019

Keywords

  • Artificial ecosystem-based optimization
  • Constrained problems
  • Engineering design
  • Global optimization
  • Hydrogeological parameter
  • Optimization algorithm

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm'. Together they form a unique fingerprint.

Cite this