Int. J. Bio-Inspired Computation, Vol. 4, No. 3, 2012 155 Copyright © 2012 Inderscience Enterprises Ltd. Particle swarm optimisation with stochastic ranking for constrained numerical and engineering benchmark problems Layak Ali* and Samrat L. Sabat School of Physics, University of Hyderabad, Hyderabad – 500046, India E-mail: informlayak@gmail.com E-mail: slssp@uohyd.ernet.in *Corresponding author Siba K. Udgata Department of Computer and Information Science, University of Hyderabad, Hyderabad – 500046, India E-mail: udgatacs@uohyd.ernet.in 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; SR; particle swarm optimisation; PSO; constrained optimisation. Reference to this paper should be made as follows: Ali, L., Sabat, S.L. and Udgata, S.K. (2012) ‘Particle swarm optimisation with stochastic ranking for constrained numerical and engineering benchmark problems’, Int. J. Bio-Inspired Computation, Vol. 4, No. 3, pp.155–166. Biographical notes: Layak Ali received his Bachelor’s degree in Electronics and Communication Engineering from Gulbarga University, India in 1999. He is currently a PhD candidate in the stream of Electronics Science of School of Physics, University of Hyderabad, India. His current research interests include swarm intelligence algorithms and its applications. Samrat L. Sabat received his PhD in Electronics Science in 2002. Currently he is a Reader at the School of Physics, University of Hyderabad, India. His current research includes swarm intelligence algorithms and its applications. Siba K. Udgata is an Associate Professor at the Computer and Information Sciences, University of Hyderabad, India. He was a United Nations Fellow and worked in UNU/IIST, Macau. His main research activities focus on mobile computing, wireless sensor network and ad hoc network. He has worked on swarm intelligence algorithms for various sensor network and cognitive radio network applications. He is currently working on research projects for development of WSN applications and resource allocation schemes for cognitive radio network.