Affiliations 

  • 1 School of Management, Shanghai University, Shanghai, China, 200444
  • 2 Department of Systems Engineering & Operations Research, George Mason University, Fairfax, Virginia 22030, USA
  • 3 Department of Industrial and Systems Engineering, National University of Singapore, Singapore, 119260
  • 4 School of Management, Universiti Sains Malaysia, Penang, Malaysia
IEEE Trans Evol Comput, 2017 Apr;21(2):206-219.
PMID: 29170617 DOI: 10.1109/TEVC.2016.2592185

Abstract

Particle Swarm Optimization (PSO) is a popular metaheuristic for deterministic optimization. Originated in the interpretations of the movement of individuals in a bird flock or fish school, PSO introduces the concept of personal best and global best to simulate the pattern of searching for food by flocking and successfully translate the natural phenomena to the optimization of complex functions. Many real-life applications of PSO cope with stochastic problems. To solve a stochastic problem using PSO, a straightforward approach is to equally allocate computational effort among all particles and obtain the same number of samples of fitness values. This is not an efficient use of computational budget and leaves considerable room for improvement. This paper proposes a seamless integration of the concept of optimal computing budget allocation (OCBA) into PSO to improve the computational efficiency of PSO for stochastic optimization problems. We derive an asymptotically optimal allocation rule to intelligently determine the number of samples for all particles such that the PSO algorithm can efficiently select the personal best and global best when there is stochastic estimation noise in fitness values. We also propose an easy-to-implement sequential procedure. Numerical tests show that our new approach can obtain much better results using the same amount of computational effort.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.