A Hybrid Optimization Framework with Dynamic Transition Scheme for Large-Scale Portfolio Management

Abstract

Meta-heuristic algorithms have successfully solved many real-world problems in recent years. Inspired by different natural phenomena, the algorithms with special search mechanisms can be good at tackling certain problems. However, they may fail to solve other problems. Among the various approaches, hybridizing meta-heuristic algorithms may possibly help to enrich their search behaviors while promoting the search adaptability. Accordingly, an efficient hybrid population-based optimization framework, namely the HYPO, is proposed in this study in which two meta-heuristic algorithms with different search ideas are connected by a dynamic contribution-based state transition scheme. Specifically, the dynamic transition scheme determines the directions of information transitions after considering the current contribution and system state at each iteration so that useful information can be shared and learnt between the concerned meta-heuristic algorithms throughout the search process. To carefully examine the effectiveness of the dynamic transition scheme, the proposed HYPO framework is compared against various well-known meta-heuristic algorithms on a set of large-scale benchmark functions and portfolio management problems of different scales in which the HYPO attains outstanding performances on the problems with complex features. Last but not least, the hybrid framework sheds lights on many possible directions for further improvements and investigations.