Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Cooperative Parallel Tabu Search for Capacitated Network Design
Journal of Heuristics
Hyper-heuristics: Learning To Combine Simple Heuristics In Bin-packing Problems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
A cooperative parallel meta-heuristic for the vehicle routing problem with time windows
Computers and Operations Research
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
Case-based heuristic selection for timetabling problems
Journal of Scheduling
Design and Analysis of Experiments
Design and Analysis of Experiments
An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A comprehensive analysis of hyper-heuristics
Intelligent Data Analysis
Metaheuristic Agent Teams for Job Shop Scheduling Problems
HoloMAS '07 Proceedings of the 3rd international conference on Industrial Applications of Holonic and Multi-Agent Systems: Holonic and Multi-Agent Systems for Manufacturing
Explicit and Emergent Cooperation Schemes for Search Algorithms
Learning and Intelligent Optimization
Solving the really hard problems with cooperative search
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Distributed choice function hyper-heuristics for timetabling and scheduling
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
Synchronous vs. asynchronous cooperative approach to solving the vehicle routing problem
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume PartI
Cooperative search for fair nurse rosters
Expert Systems with Applications: An International Journal
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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.