Quantum particles-enhanced multiple Harris Hawks swarms for dynamic optimization problems

Abstract Dynamic optimization problems (DOPs) have been a subject of considerable research interest mainly due to their widespread application potential. In the literature, various mechanisms have been reported to cope with the challenges of DOPs. The proposed mechanisms have usually been adopted by well-known population-based optimization algorithms, such as genetic algorithms or particle swarm optimization. Although new generation swarm-intelligence algorithms are continuously being developed and have much to offer in DOPs, their performance is usually tested on stationary optimization problems. In this study, a recently introduced optimization algorithm, Harris Hawk Optimizer, is redesigned as a multi-population based algorithm to deal with possible multiple optima. Thus, the proposed modification is allowed to search diverse parts of the search space more efficiently, particularly in multimodal environments. Next, it is further enhanced by using quantum particles to tackle with diversification and intensification challenges in DOPs. As shown in the present work, this mechanism can maintain population diversity and intensification depending on a user-supplied parameter. Finally, based on different algorithmic components, four different variants of HHO are proposed. The performances of the developed algorithms are tested on both stationary and dynamic test problems. Dynamic test functions introduced in the IEEE Congress on Evolutionary Computation 2009 (CEC 2009) are used and further extended to test the proposed algorithms' performances. Finally, appropriate statistical analysis is conducted to demonstrate significant improvements over the existing algorithms.