Search

This paper presents a novel variant of quantum mechanics inspired Particle Swarm Optimisation (PSO) algorithm named constrained/unconstrained Quantum Particle Swarm Optimisation (CQPSO). The proposed algorithm has the properties of quantum mechanics embedded in the structure of the PSO along with the presence of a quadratic interpolation recombination operator. The performance of CQPSO is validated on three standard non linear, unconstrained functions, eight constrained benchmark problems and two constrained, real life, electrical design problems. The experimental results show that the presence of quadratic interpolation recombination operator enhances the performance of quantum mechanics inspired PSO.

Stochastic search algorithms that take their inspiration from nature are gaining a great attention of many researchers to solve high dimension and non – linear complex optimization problems for which traditional methods fails. Shuffled frog – leaping algorithm (SFLA) is recent addition to the family of stochastic search algorithms that take its inspiration from the foraging process of frogs. SFLA has proved its efficacy in solving discrete as well as continuous optimization problems. The present study introduces a modified version of SFLA that uses geometric centroid mutation to enhance the convergence rate. The variant is named as Centroid Mutated – SFLA (CM-SFLA). The proposal is implemented on five benchmark and car side impact problem. Simulated results illustrate the efficacy of the proposal in terms of convergence speed and mean value. © 2014 The Authors. Published by Elsevier B.V. Peer-review under responsibility of organizing committee of the International Conference on Information and Communication Technologies (ICICT 2014).