Quantum-Inspired Distributed Memetic Algorithm

This paper proposed a novel distributed memetic evolutionary model, where four modules exploration, intensified exploitation, knowledge transfer, and restart are coevolved to maximize their strengths achieve superior global optimality. Distributed exploration evolves three independent populations by heterogenous operators. Intensified exploitation an external elite archive in parallel with balance local searches. Knowledge transfer is based on point-ring communication topology share successful experiences among distinct search agents. Evolutionary adopts adaptive perturbation strategy control diversity reasonably. Quantum computation newly emerging technique, which has powerful computing power parallelized ability. Therefore, this further fuses quantum mechanisms into the model build new algorithm, referred as quantum-inspired algorithm (QDMA). In QDMA, individuals represented characteristics evolved optimizers hyperspace. The QDMA integrates superiorities of distributed, memetic, evolution. Computational experiments carried out evaluate performance QDMA. results demonstrate effectiveness special designs show that greater superiority compared state-of-the-art algorithms Wilcoxon's rank-sum test. attributed not only good cooperative coevolution but also each component.