A cooperative hyper-heuristic search framework
In this paper, we aim to investigate the role of cooperation between low level heuristics within a hyper-heuristic framework. Since different low level heuristics have different strengths and weaknesses, we believe that cooperation can allow the strengths of one low level heuristic to compensate for the weaknesses of another. We propose an agent-based cooperative hyper-heuristic framework composed of a population of heuristic agents and a cooperative hyper-heuristic agent. The heuristic agents perform a local search through the same solution space starting from the same or different initial solution, and using different low level heuristics. The heuristic agents cooperate synchronously or asynchronously through the cooperative hyper-heuristic agent by exchanging the solutions of the low level heuristics. The cooperative hyper-heuristic agent makes use of a pool of the solutions of the low level heuristics for the overall selection of the low level heuristics and the exchange of solutions. Computational experiments carried out on a set of permutation flow shop benchmark instances illustrated the superior performance of the cooperative hyper-heuristic framework over sequential hyper-heuristics. Also, the comparative study of synchronous and asynchronous cooperative hyper-heuristics showed that asynchronous cooperative hyper-heuristics outperformed the synchronous ones.
hyper-heuristics cooperative search distributed problem solving permutation flow shop scheduling