Authors: Mohamed H. Gadallah; Mohamed B. Ali; Ahmed M. Emam
Addresses: Industrial Engineering and Operations Research, Department of Mechanical Design and Production, Faculty of Engineering, Cairo University, 12613, Egypt ' Faculty of Information Technology and Design, University of Jazeera (UOJ), P.O. Box 36567, Dubai, UAE ' Department of Operations Research, Institute of Statistical Studies and Research (ISSR), Cairo University, 12613, Egypt
Abstract: This paper presents a modification to the particle swarm optimisation (PSO) to tackle two difficulties observed in many applications: premature convergence of the solution, and the degree of confidence of the decision maker. This approach, known as triggered particle swarm optimisation, treats the problem in a dynamic environment and making each particle reset its record of best positions. This approach treated the PSO by triggering the particle swarm optimiser in a dynamic environment, making each particle reset its record of its best position. This, in turn, avoids making position and velocity changes based on outdated information. Due to random-based nature, a statistical confidence interval estimation approach is developed around the returned optimum at different levels. The proposed algorithm, triggered particle swarm optimisation (T-PSO), performs significantly better than the original PSO and the new particle swarm optimisation (NPSO) discussed in references.
Keywords: triggered PSO; particle swarm optimisation; T-PSO; statistical optimisation; statistical analysis; premature convergence; degree of confidence.
International Journal of Industrial and Systems Engineering, 2014 Vol.16 No.1, pp.1 - 29
Available online: 28 Oct 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article