Abstract
In scientific research and engineering practice, many optimization problems can be abstracted into many-objective optimizations. The key to solving many-objective optimizations is the way to design an effective algorithm to balance exploration and exploitation. This paper proposes a hybrid many-objective optimization algorithm, which consists of evolutionary membrane algorithm and chemical reaction optimization algorithm. In the proposed algorithm, object represents a candidate solution of many-objective optimization problem. The reaction rule will be applied by the four operators to evolve the objects. In addition, superior objects are selected based on corner solution search for balancing convergence and diversity of the solutions in the high-dimensional objective space. In the simulation, the proposed algorithm compared to some state-of-the-art algorithms, including MaOEA-CS, MOEA/D, MOEA/DD, RVEA and NSGA-III, to all benchmark functions for CEC2018 Competition on Evolutionary Many-Objective Optimization. Experimental results empirically demonstrate that the proposed algorithm has a beneficial adaptation ability in terms of both convergence enhancement and diversity maintenance.
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