Book ChapterDOI
JABAT middleware as a tool for solving optimization problems
Dariusz Barbucha,Ireneusz Czarnowski,Piotr Jędrzejowicz,Ewa Ratajczak-Ropel,Izabela Wierzbowska +4 more
- Vol. 2, pp 181-195
TLDR
Several applications of JABAT as a tool for solving difficult optimization problems are presented and list of implementations and extensions of the system shows how useful and flexible the system can be.Abstract:
JABAT supports designing and implementing A-Team architectures for solving difficult optimization problems. This paper presents several applications of JABAT as a tool for solving such problems. List of implementations and extensions of JABAT shows how useful and flexible the system can be. The paper summarises experiences of authors gained while developing various A-Teams. Some conclusions concerning such details of the A-Team model like the composition of the team of agents, the choice of rules determining how the agents interact with the population of solutions, or how synchronisation or cooperation of agents influence the quality of results are offered.read more
Citations
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Journal ArticleDOI
A reinforcement learning-based multi-agent framework applied for solving routing and scheduling problems
Maria Amelia Lopes Silva,Sérgio Ricardo de Souza,Marcone Jamilson Freitas Souza,Ana L. C. Bazzan +3 more
TL;DR: The framework presented here is a step forward in relation to the other frameworks of the literature regarding to the adaptation to the particular aspects of the problems and strengthens the issue of the scalability of the framework.
Journal ArticleDOI
Hybrid metaheuristics and multi-agent systems for solving optimization problems: A review of frameworks and a comparative analysis
Maria Amelia Lopes Silva,Sérgio Ricardo de Souza,Marcone Jamilson Freitas Souza,Moacir Felizardo de França Filho +3 more
TL;DR: It can be said that there are important gaps to be filled in the development of Frameworks for Optimization using metaheuristics, which open important possibilities for future works, particularly by implementing the approach of multi-agent systems.
Book ChapterDOI
Distributed learning with data reduction
TL;DR: The central part of the dissertation proposes an agent-based distributed learning framework to carry-out data reduction in parallel in separate locations, employing specialized software agents.
Journal ArticleDOI
A cooperative population learning algorithm for vehicle routing problem with time windows
TL;DR: This paper proposes an Agent-Based Cooperative Population Learning Algorithm for the Vehicle Routing Problem with Time Windows, which uses a set of various heuristics which run under the cooperation scheme defined separately for each stage.
Journal ArticleDOI
Search modes for the cooperative multi-agent system solving the vehicle routing problem
TL;DR: The main goal of the paper is to evaluate to what extent a mode of cooperation 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.
References
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Book ChapterDOI
Agent theories, architectures, and languages: a survey
TL;DR: A survey of what the authors perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents is presented.
Journal ArticleDOI
Reduction Techniques for Instance-BasedLearning Algorithms
D. Randall Wilson,Tony Martinez +1 more
TL;DR: Of those algorithms that provide substantial storage reduction, the DROP algorithms have the highest average generalization accuracy in these experiments, especially in the presence of uniform class noise.
Journal ArticleDOI
Numerical methods for fuzzy clustering
TL;DR: In this paper, the authors considered the problem of decomposition of the probability density function of the original set into the weighted sum of the component fuzzy set densities, which is done by optimization of some functional defined over all possible fuzzy classifications.
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