A Niching Indicator-Based Multi-modal Many-objective Optimizer
This addresses a bottleneck in optimization for domains requiring multiple equivalent Pareto optimal solutions, though it appears incremental as it builds on existing evolutionary approaches.
The paper tackles the lack of efficient methods for multi-modal many-objective optimization (with more than three objectives) by proposing a niching indicator-based algorithm, which is shown to handle up to 15 objectives and outperform eight existing multi-objective evolutionary algorithms.
Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. Some evolutionary algorithms for multi-modal multi-objective optimization have been proposed in the literature. However, there is no efficient method for multi-modal many-objective optimization, where the number of objectives is more than three. To address this issue, this paper proposes a niching indicator-based multi-modal multi- and many-objective optimization algorithm. In the proposed method, the fitness calculation is performed among a child and its closest individuals in the solution space to maintain the diversity. The performance of the proposed method is evaluated on multi-modal multi-objective test problems with up to 15 objectives. Results show that the proposed method can handle a large number of objectives and find a good approximation of multiple equivalent Pareto optimal solutions. The results also show that the proposed method performs significantly better than eight multi-objective evolutionary algorithms.