Solving Large-Scale Function Optimization Problem by Using a New Metaheuristic Algorithm Based on Quantum Dolphin Swarm Algorithm

Meta-heuristic algorithm has been a research hotspot in solving the optimal solution of large-scale functions. However, meta-heuristic algorithms are prone to fall into local optimum problems, such as the recently proposed dolphin swarm algorithm (DSA). To solve this problem, in this study, the quantum search algorithm is introduced into DSA. In addition, to test the performance of the proposed quantum dolphin swarm algorithm (QDSA), six commonly used large-scale functions (e.g. Rotated hyper-ellipsoid function) are taken as examples. Furthermore, some advanced algorithms (e.g. whale optimization algorithm (WOA)) are used for comparison. The results show that the ability of QDSA to obtain global optimal solution is obviously improved compared with DSA, and the performance of QDSA is superior to other algorithms considered for comparison. Finally, it can be concluded that such a novel meta-heuristic algorithm may help to improve the problem of solving the optimal solution of large-scale functions.