Title: Particle swarm optimisation: a triggered approach
Authors: Mohamed H. Gadallah; Mohamed B. Ali; Ahmed M. Emam
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 This, in turn, avoids making position and velocity changes based on outdated information.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.
Int. J. of Industrial and Systems Engineering, 2014 Vol.16, No.1, pp.1 - 29
Available online: 28 Oct 2013