Combining Fitness Landscape Analysis and Adaptive Operator Selection in Multi and Many-Objective Optimization

Real-world optimisation problems tends to be complex and have many conflicting objectives. Many of these problems have been solved satisfactorily by metaheuristics, but choosing the appropriate method to each problem is still an open problem, as it depends on the characteristics of the problem. Fitness Landscape Analysis (FLA) is a mechanism that can be used to identify the major characteristics of a problem and guide the choice of the algorithm. Recently, the use of Adaptive Operator Selection (AOS) schemes to permit the adaptation of an algorithm to the online characteristics of a problem have been successfully used to solve complex problems. Most AOS mechanisms are only guided by a reward based on the fitness values, which does not bring information about the problem characteristics. This work intends to incorporate FLA information about the problems into an AOS mechanism to improve its performance. The proposed approach is based on the MOEA/D-DRA framework and combines two FLA metrics (Fitness Cloud Index and Dispersion Metric) with the Probability Matching operator selection scheme. Five variants of the proposed approach are investigated in a multi-objective benchmark (CEC 2009) and a many-objective benchmark (CEC 2018). The results indicate that the addition of FLA information improves the behavior of the AOS mechanism, that multimodality information tends to be more important than searchability information, that using both metrics is better than using only the searchability metric and that using only the multimodality information is usually the best choice.