Improving the Quantum Multi-Swarm Optimization with Adaptive Differential Evolution for Dynamic Environments

In this study, the modification of quantum multi-swarm optimization algorithm is proposed for dynamic problems. The implies using search operators from differential evolution with a certain probability within particle swarm to improve algorithm’s capabilities in dynamically changing environments. For testing, Generalized Moving Peaks Benchmark was used. experiments were performed four benchmark settings, and sensitivity analysis main parameters algorithms performed. It shown that applying mutation operator personal best positions particles allows improving performance.