An Adaptive Multi-Population Optimization Algorithm for Global Continuous Optimization

Nowadays, there are various optimization problems that exact mathematical methods are not applicable. Metaheuristics are considered as efficient approaches for finding the solutions. Yet there are many real-world problems that consist of different properties. For instance, financial portfolio optimization may contain many dimensions for different sets of assets, which suggests the need of a more adaptive metaheuristic method for tackling such problems. However, few existing metaheuristics can achieve robust performance across these variable problems even though they may obtain impressive results in specific benchmark problems. In this paper, a metaheuristic named the Adaptive Multi-Population Optimization (AMPO) is proposed for continuous optimization. The algorithm hybridizes yet modifies several useful operations like mutation and memory retention from evolutionary algorithms and swarm intelligence (SI) techniques in a multi-population manner. Furthermore, the diverse control on multiple populations, solution cloning and reset operation are designed. Compared with other metaheuristics, the AMPO can attain an adaptive balance between the capabilities of exploration and exploitation for various optimization problems. To demonstrate its effectiveness, the AMPO is evaluated on 28 well-known benchmark functions. Also, the parameter sensitivity analysis and search behavior study are conducted. Finally, the AMPO is validated on its applicability through a portfolio optimization problem as a challenging example of real-world applications. The benchmark results show that the AMPO achieves a better performance than those of nine state-of-the-art metaheuristics including the IEEE CEC winning algorithms, recent SI and multi-population/hybrid metaheuristics. Besides, the AMPO can consistently produce a good performance in portfolio optimization.