Multi-objective quantum-behaved particle swarm optimization algorithm with double-potential well and share-learning

For improving the convergence accuracy and diversity of multi-objective optimization algorithm a multi-objective quantum-behaved particle swarm optimization algorithm with double-potential well and share-learning is proposed, which overcomes the deficiency of particles readily gathering in identical solutions. The two local attractors, inside and outside, are introduced to construct the particle locations updating model, using the quantum tunneling and transition effects in double-potential well model. In this way, the particle moves to the solution sparseness region in later evolution stage, so as to avoid gathering in the single local attractor and escape from local optimum. Therefore the optimization accuracy of the algorithm is improved. The share-learning strategy is adopted to extend the search range of particles and increase the diversity of solutions. The problem of easily converging to boundary solutions in quantum-behaved particle swarm optimization algorithm could be avoided. Simulation results show that the proposed algorithm makes excellent performance in optimization accuracy, convergence, diversity, and distribution, compared with three existing algorithms. Moreover, the proposed algorithm can hold on better convergence and distribution performance when handling high-dimensional multi-objective problems.