A Many-Objective Evolutionary Algorithm with Angle-Based Selection and Shift-Based Density Estimation
This work addresses scalability issues in evolutionary computation for many-objective optimization, offering a simple and parameter-efficient method that is incremental in nature.
The paper tackles the challenge of evolutionary many-objective optimization by proposing the AnD algorithm, which uses angle-based selection and shift-based density estimation to delete poor individuals, achieving highly competitive performance compared to seven state-of-the-art algorithms on benchmark problems with up to 15 objectives.
Evolutionary many-objective optimization has been gaining increasing attention from the evolutionary computation research community. Much effort has been devoted to addressing this issue by improving the scalability of multiobjective evolutionary algorithms, such as Pareto-based, decomposition-based, and indicator-based approaches. Different from current work, we propose a novel algorithm in this paper called AnD, which consists of an angle-based selection strategy and a shift-based density estimation strategy. These two strategies are employed in the environmental selection to delete the poor individuals one by one. Specifically, the former is devised to find a pair of individuals with the minimum vector angle, which means that these two individuals share the most similar search direction. The latter, which takes both the diversity and convergence into account, is adopted to compare these two individuals and to delete the worse one. AnD has a simple structure, few parameters, and no complicated operators. The performance of AnD is compared with that of seven state-of-the-art many-objective evolutionary algorithms on a variety of benchmark test problems with up to 15 objectives. The experimental results suggest that AnD can achieve highly competitive performance. In addition, we also verify that AnD can be readily extended to solve constrained many-objective optimization problems.