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Design of global optimisation approaches inspired by swarm intelligence is an emergent research area with population and evolution characteristics similar to those of evolutionary algorithms. However, the swarm intelligence concept differs in that it emphasises co-operative behaviour among group members. Particle swarm optimisation (PSO) is a population-based swarm intelligence algorithm driven by the simulation of a social psychological metaphor instead of survival of the fittest individual. Inspired by the classical PSO method and quantum mechanics theories, this work presents a novel quantum-behaved PSO (QPSO) approach using mutation operator with Gaussian probability distribution, called G-QPSO. The simulation results demonstrate good performance of the QPSO and G-QPSO in solving a significant benchmark problem in electromagnetic area, the shape design of Loney's solenoid benchmark problem.