Abstract
The idea of decomposition is becoming increasingly successful and popular in evolutionary multi-objective optimization. An efficient cone decomposition approach was further developed in the conical area evolutionary algorithm (CAEA). This approach improves the runtime efficiency and population diversity of decomposition-based algorithms effectively for bi-objective optimization in practice. In this paper, it is proved that the optimum of any conical subproblem in cone decomposition possesses the global Pareto optimality, apart from previously known the local Pareto optimality, in the presence of continuous frontier segment within the associated subregion. Experimental results on 5 bi-objective benchmark problems indicate that the solutions of conical subproblems obtained by CAEA not only acquire obviously better Pareto optimality in terms of C-metric but also constitute higher qualities of frontiers than the solutions by the other four popular multi-objective algorithms.
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