Supply-Demand-Based Optimization: A Novel Economics-Inspired Algorithm for Global Optimization

Weiguo Zhao, Liying Wang, Zhenxing Zhang

Research output: Contribution to journalArticle

Abstract

A novel metaheuristic optimization algorithm, named supply-demand-based optimization (SDO), is presented in this paper. SDO is a swarm-based optimizer motivated by the supply-demand mechanism in economics. This algorithm mimics both the demand relation of consumers and supply relation of producers. The proposed algorithm is compared with other state-of-the-art counterparts on 29 benchmark test functions and six engineering optimization problems. The results on the unconstrained test functions prove that SDO is able to provide very promising results in terms of exploration, exploitation, local optima avoidance, and convergence rate. The results on the constrained engineering problems suggest that SDO is considerately competitive in terms of computational expense, convergence rate, and solution accuracy. The codes are available at https://www.mathworks.com/matlabcentral/fileexchange/71764-supply-demand-based-optimization.

Original languageEnglish (US)
Article number8721125
Pages (from-to)73182-73206
Number of pages25
JournalIEEE Access
Volume7
DOIs
StatePublished - May 23 2019

Keywords

  • constrained problems
  • engineering design
  • global optimization
  • optimization algorithm
  • particle swarm optimization
  • Supply-demand-based optimization
  • swarm intelligence

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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