Mechanical Design Using Quantum-Behaved Particle Swarm Optimizer With Exponential Mutation Operator

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Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviours of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and quantum mechanics theories, this work presents new Quantum-behaved PSO (QPSO) approaches using mutation operator with exponential probability distribution. The simulation results demonstrate good performance of the QPSO in solving a well-studied continuous optimization problem of mechanical engineering design.