Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
A comprehensive analysis of hyper-heuristics
Intelligent Data Analysis
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Is the meta-EA a viable optimization method?
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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In this paper, we propose an agent-based multi-level search framework for the asynchronous cooperation of hyper-heuristics. This framework contains a population of different hyper-heuristic agents and a coordinator agent. Each hyper-heuristic agent operates on the same set of low level heuristics, while the coordinator agent operates on top of all the hyper-heuristic agents. Starting from the same initial solution, each hyper-heuristic agent performs a search over the space generated by the low level heuristics. The hyper-heuristic agents cooperate asynchronously through the coordinator agent by exchanging their elite solutions. The coordinator agent maintains a pool of elite solutions and manages the communication between the hyper-heuristics agents. Preliminary computational experiments have been carried out on a set of permutation flow shop benchmark instances. The results illustrated the superior performance of the multi-level framework for asynchronous cooperation of hyper-heuristics.