Finding Representative Solutions in Multimodal Optimization for Enhanced Decision-Making

Many real-world optimization problems are multimodal by nature, and there may exist a large number of optimal solutions. Despite having the same or similar objective values, solutions can still differ in terms of technical feasibility or the preferred range of their decision variable values. Therefore, it is more desirable to employ optimization methods capable of offering several optimal solutions to the Decision Maker (DM). Existing niching methods aim to find all possible solutions in a single optimization run, resulting in possibly too many options to choose from. Due to limited resources available for evaluating solutions in practice, the DM, however, might only be interested in finding a few sufficiently different solutions quickly. This work aims to facilitate this decision-making process by providing only a number of representative solutions to the DM. This way, the DM is not overloaded with superfluous information, resulting in faster and better decision-making. This paper proposes a novel niching method, Suppression Radius-based Niching (SRN), based on the principle of suppression radius to determine representative niching areas. The proposed method is especially appealing for real-world scenarios where reducing the number of function evaluations is crucial due to the high computational costs of evaluations.