Title: Particle swarm optimisation with stochastic ranking for constrained numerical and engineering benchmark problems

Authors: Layak Ali; Samrat L. Sabat; Siba K. Udgata

Addresses: School of Physics, University of Hyderabad, Hyderabad – 500046, India. ' School of Physics, University of Hyderabad, Hyderabad – 500046, India. ' Department of Computer and Information Science, University of Hyderabad, Hyderabad – 500046, India

Abstract: Most of the real world science and engineering optimisation problems are non-linear and constrained. This paper presents a hybrid algorithm by integrating particle swarm optimisation with stochastic ranking for solving standard constrained numerical and engineering benchmark problems. Stochastic ranking technique that uses bubble sort mechanism for ranking the solutions and maintains a balance between the objective and the penalty function. The faster convergence of particle swarm optimisation and the ranking technique are the major motivations for hybridising these two concepts and to propose the stochastic ranking particle swarm optimisation (SRPSO) technique. In this paper, SRPSO is used to optimise 15 continuous constrained single objective benchmark functions and five well-studied engineering design problems. The performance of the proposed algorithm is evaluated based on the statistical parameters such mean, median, best, worst values and standard deviations. The SRPSO algorithm is compared with six recent algorithms for function optimisation. The simulation results indicate that the SRPSO algorithm performs much better while solving all the five standard engineering design problems where as it gives a competitive result for constrained numerical benchmark functions.

Keywords: stochastic ranking; particle swarm optimisation; PSO; constrained optimisation; numerical benchmark problems; engineering benchmark problems; engineering design.

DOI: 10.1504/IJBIC.2012.047238

International Journal of Bio-Inspired Computation, 2012 Vol.4 No.3, pp.155 - 166

Received: 03 Jun 2011
Accepted: 28 Nov 2011
Published online: 22 Sep 2014 *

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