Quantum-inspired Optimization Approach for Engineering Design

Optimization problems are widely encountered in various fields of mechanical engineering. Sometimes such problems can be very complex due to the actual and practical nature of the objective function or the model constraints. During the history of science of computational intelligence, many evolutionary algorithms and swarm intelligence approaches were proposed having more or less success in solving various mechanical engineering optimization problems. In this context, the Particle Swarm Optimization (PSO) is a bio-inspired optimization mechanism based on the metaphor of social behavior of birds flocking and fish schooling in search for food. In PSO each member is seen as a particle, and each particle is a potential solution to the problem under analysis. In this context, each particle which moves through the space of the problem has a randomized velocity associated to it. Inspired by the classical PSO method and quantum mechanics theories, this work presents a new Quantum-behaved PSO (QPSO) approach using Gaussian probability distribution function. The simulation results demonstrate good performance of the QPSO approaches in solving a benchmark problem of tension/compression spring design.