Factors governing the behavior of multiple cooperating swarms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A self-adaptive multiagent evolutionary algorithm for electrical machine design
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Team-work based architecture for distributed manufacturing scheduling
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
Agent-based protein structure prediction
Multiagent and Grid Systems - Multi-agent systems for medicine, computational biology, and bioinformatics
A cooperative and self-adaptive metaheuristic for the facility location problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Computers and Operations Research
Multiagent optimization system for solving the traveling salesman problem (TSP)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A multi-agent framework for distributed theorem proving
Expert Systems with Applications: An International Journal
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
A multiagent architecture for solving combinatorial optimization problems through metaheuristics
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Use of MaSE methodology for designing a swarm-based multi-agent system
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Knowledge integration and management in autonomous systems
Autonomous Agents and Multi-Agent Systems
A memetic cooperative optimization schema and its application to the tool switching problem
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Development and analysis of cooperative model-based metaheuristics
Cybernetics and Systems Analysis
A multi-agent organizational framework for coevolutionary optimization
Transactions on Petri nets and other models of concurrency IV
A distributed and probabilistic concurrent constraint programming language
ICLP'05 Proceedings of the 21st international conference on Logic Programming
Knowledge management in different software development approaches
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
A distributed agent-based approach for simulation-based optimization
Advanced Engineering Informatics
Hi-index | 0.00 |
In this work, we introduce a multiagent architecture called the MultiAGent Metaheuristic Architecture (MAGMA) conceived as a conceptual and practical framework for metaheuristic algorithms. Metaheuristics can be seen as the result of the interaction among different kinds of agents: The basic architecture contains three levels, each hosting one or more agents. Level-0 agents build solutions, level-1 agents improve solutions, and level-2 agents provide the high level strategy. In this framework, classical metaheuristic algorithms can be smoothly accommodated and extended. The basic three level architecture can be enhanced with the introduction of a fourth level of agents (level-3 agents) coordinating lower level agents. With this additional level, MAGMA can also describe, in a uniform way, cooperative search and, in general, any combination of metaheuristics. We describe the entire architecture, the structure of agents in each level in terms of tuples, and the structure of their coordination as a labeled transition system. We propose this perspective with the aim to achieve a better and clearer understanding of metaheuristics, obtain hybrid algorithms, suggest guidelines for a software engineering-oriented implementation and for didactic purposes. Some specializations of the general architecture will be provided in order to show that existing metaheuristics [e.g., greedy randomized adaptive procedure (GRASP), ant colony optimization (ACO), iterated local search (ILS), memetic algorithms (MAs)] can be easily described in our framework. We describe cooperative search and large neighborhood search (LNS) in the proposed framework exploiting level-3 agents. We show also that a simple hybrid algorithm, called guided restart ILS, can be easily conceived as a combination of existing components in our framework.