A survey and classification of Opposition-Based Metaheuristics

- A survey and a classification of Opposition-Based Metaheuristics is presented.
- Fifty-four approaches are reviewed analyzing important characteristics.
- For each approach, we analyzed the objective and role of the OBL component.
- We analyzed the type of OBL and the problem tackled by each approach.
- Two main categories are considered: OBL applied or OBL inspired Metaheuristics.

Opposition-Based Learning (OBL) is a research area that has been widely applied in several algorithms for improving the search process. In this work we present a revision of several applications of OBL in metaheuristics and some metaheuristic approaches that are inspired in OBL. For reviewing each OBL approach we analyze the objective of including OBL, the role performed by the OBL component, the type of OBL and the type of problem tackled. We also propose a classification of these approaches that apply or are inspired in OBL. Our goal is to motivate researchers in metaheuristics to include ideas from OBL and report which strategies were successfully applied.