A Competitive and Cooperative Evolutionary Framework for Ensemble of Constraint Handling Techniques

Ensemble of constraint handling techniques (CHTs) is an effective way for solving constrained optimization problems (COPs). However, the use of multiple CHTs might cause more resource overhead. How to make reasonable resource allocation and cooperation among different CHTs is the issue that requires much more attention to improve the effectiveness of the ensemble of CHTs. Based on this consideration, we tried to handle the issue with adaptive resource allocation and population co-evolution for competition and cooperation among different CHTs. First, the competitive and cooperative evolutionary framework (CCEF) was proposed to construct multiple populations for CHTs. It assigns separate populations to each CHT and performs resource allocation based on success history records calculated by the performance of each CHT population. In addition, it promotes population co-evolution through parent recombination and offspring diffusion. Second, four CHTs with different types were chosen to compose a technique pool for the implementation of CCEF. Third, to obtain stronger search ability, we designed a heuristic and historical-based differential evolutionary operator (EO) embedded into CCEF. Experimental results on five COP benchmark suites (116 COPs in total) and 33 real-world COPs demonstrated that the proposed CCEF outperforms other advanced algorithms. The results revealed that CCEF can absorb the advantages of different CHTs and appropriately allocate resources to the more suitable CHTs.