Fusion-based hybrid many-objective optimization algorithm

In the last three decades there have been a number of efficient multi-objective optimization algorithms capable of solving real-world problems. However, due to the complexity of most real-world problems (high-dimensionality of problems, computationally expensive, and unknown function properties) researchers and decision-makers are increasingly facing the challenge of selecting an optimization algorithm capable of solving their hard problems. In this paper, we propose a simple yet efficient hybridization of multi- and many-objective optimization algorithms framework called hybrid many-objective optimization algorithm using fusion of solutions obtained by several many-objective algorithms (fusion) to gain the combined benefits of several algorithms and reducing the challenge of choosing one optimization algorithm to solve complex problems. During the optimization process, the Fusion framework (1) executes all optimization algorithms in parallel, (2) it combines solutions of these algorithms and extracts well-distributed solutions using predefined structured reference points or user-defined reference points, and (3) adaptively selects best-performing algorithm to tackle the problem at different stages of the search process. A case study of the fusion framework by considering GDE3, SMPSO, and SPEA2 as multi-objective optimization algorithms is presented. Experimental results on five unconstrained and four constrained benchmark test problems with three to ten objectives show that the Fusion framework significantly outperforms all algorithms involved in the hybridization process as well as the NSGA-III algorithm in terms of diversity and convergence of obtained solutions. Furthermore, the proposed framework is consistently able to find accurate solutions for all test problems which can be interpreted as its high robustness characteristic.