Composite Particle Swarm Optimizer with Historical Memory for Function Optimization

Jie Li, Junqi Zhang, Changjun Jiang, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

154 Scopus citations


Particle swarm optimization (PSO) algorithm is a population-based stochastic optimization technique. It is characterized by the collaborative search in which each particle is attracted toward the global best position (gbest) in the swarm and its own best position (pbest). However, all of particles' historical promising pbests in PSO are lost except their current pbests. In order to solve this problem, this paper proposes a novel composite PSO algorithm, called historical memory-based PSO (HMPSO), which uses an estimation of distribution algorithm to estimate and preserve the distribution information of particles' historical promising pbests. Each particle has three candidate positions, which are generated from the historical memory, particles' current pbests, and the swarm's gbest. Then the best candidate position is adopted. Experiments on 28 CEC2013 benchmark functions demonstrate the superiority of HMPSO over other algorithms.

Original languageEnglish (US)
Article number7114277
Pages (from-to)2350-2363
Number of pages14
JournalIEEE Transactions on Cybernetics
Issue number10
StatePublished - Oct 2015

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


  • Estimation of distribution algorithm (EDA)
  • historical memory
  • particle swarm optimization (PSO)


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