Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
The vehicle routing problem
Asynchronous Teams: Cooperation Schemes for Autonomous Agents
Journal of Heuristics
Cooperative Parallel Tabu Search for Capacitated Network Design
Journal of Heuristics
A Roadmap of Agent Research and Development
Autonomous Agents and Multi-Agent Systems
Euro-Par '99 Proceedings of the 5th International Euro-Par Conference on Parallel Processing
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Cooperative Parallel Variable Neighborhood Search for the p-Median
Journal of Heuristics
A Guided Cooperative Search for the Vehicle Routing Problem with Time Windows
IEEE Intelligent Systems
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Soft computing and cooperative strategies for optimization
Applied Soft Computing
Coevolutionary Quantum-Behaved Particle Swarm Optimization with Hybrid Cooperative Search
PACIIA '08 Proceedings of the 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application - Volume 01
Explicit and Emergent Cooperation Schemes for Search Algorithms
Learning and Intelligent Optimization
A-Teams and Their Applications
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Solving the really hard problems with cooperative search
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
A cooperative hyper-heuristic search framework
Journal of Heuristics
Cooperative solution to the vehicle routing problem
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
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
JABAT middleware as a tool for solving optimization problems
Transactions on computational collective intelligence II
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Transactions on Computational Collective Intelligence IX
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Cooperation as a problem-solving strategy is widely used to build methods addressing complex hard optimization problems. It involves a set of highly autonomous programs (agents), each implementing a particular solution method, and a cooperation scheme combining these autonomous programs into a single problem-solving strategy. Possible form of such cooperation may be based, for example, on adaptive memory methods, where partial elements of good solutions are stored and next combined to create new complete solutions. Alternative approach is based on central memory, where complete elite solutions are exchanged among various agents and/or heuristics. Moreover, cooperatively solving a task is often combined with learning mechanism, where agents adapt their behavior to the new states of environment during the process of solving the problem. The main goal of the paper is to evaluate to what extent a mode of cooperation (synchronous or asynchronous) between a number of optimization agents cooperating through sharing a central memory influences the quality of solutions while solving instances of the Vehicle Routing Problem. The investigated search modes are evaluated using a dedicated cooperative multi-agent system allowing for various modes of cooperation and with the reinforcement learning mechanism implemented in it.