Author
Dariusz Barbucha
Bio: Dariusz Barbucha is an academic researcher from California Maritime Academy. The author has contributed to research in topics: Vehicle routing problem & Population. The author has an hindex of 11, co-authored 28 publications receiving 331 citations.
Papers
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01 Jan 2009
TL;DR: The chapter proposes a middleware called JABAT (JADE-Based A-Team), intended to become a first step towards next generation A-Teams which are fully Internet accessible, portable, scalable and in conformity with the FIPA standards.
Abstract: The chapter proposes a middleware called JABAT (JADE-Based A-Team). JABAT allows to design and implement A-Team architectures for solving combinatorial and selected non-linear optimization problems. The JABAT is intended to become a first step towards next generation A-Teams which are fully Internet accessible, portable, scalable and in conformity with the FIPA standards. From the user point of view JABAT is the web-based application, in the paper refereed to as the e-JABAT.
53 citations
16 Oct 2006
TL;DR: The paper proposes a JADE-based A-Team environment as a middleware supporting the implementation and execution of population-based algorithms for combinatorial optimization problems including traveling salesman, resource-constrained project scheduling, vehicle routing and clustering problems.
Abstract: The paper proposes a JADE-based A-Team environment as a middleware supporting the implementation and execution of population-based algorithms. The paper includes an overview of the JADE-based A-Team components and presents examples of the population-based architectures designed to obtain solutions to example combinatorial optimization problems including traveling salesman, resource-constrained project scheduling, vehicle routing and clustering problems. Conclusions focus on advantages of the JADE-based A-Team and on suggestions for further research
33 citations
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.
27 citations
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.
26 citations
01 Jan 2010
TL;DR: The experiment shows that designing effective working strategy can considerably improve the performance of the A-Team system.
Abstract: An A-Team is a system of autonomous agents and the common memory. Each agent possesses some problem-solving skills and the memory contains a population of problem solutions. Cyclically solutions are being sent from the common memory to agents and from agents back to the common memory. Agents cooperate through selecting and modifying these solutions according to the user-defined strategy referred to as the working strategy. The modifications can be constructive or destructive. An attempt to improve a solution can be successful or unsuccessful. Agents can work asynchronously (each at its own speed) and in parallel. The A-Team working strategy includes a set of rules for agent communication, selection of solution to be improved and management of the population of solutions which are kept in the common memory. In this paper influence of different strategies on A-Team performance is investigated. To implement various strategies the A-Team platform called JABAT has been used. Different working strategies with respect to selecting solutions to be improved by the A-Team members and replacing the solutions stored in the common memory by the improved ones are studied. To evaluate typical working strategies the computational experiment has been carried out using several benchmark data sets. The experiment shows that designing effective working strategy can considerably improve the performance of the A-Team system.
25 citations
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TL;DR: The state of the art related to the use and to the application of ABM as optimization technique, given their peculiarity in dealing with the representation and the simulation of complex systems is illustrated.
Abstract: Agent based models (ABM) have been recently applied to solve optimization problems whose domains present several inter-related components in a distributed and heterogeneous environment. In this work we illustrate the state of the art related to the use and to the application of ABM as optimization technique, given their peculiarity in dealing with the representation and the simulation of complex systems. After a description of the approach and a comparison with classical heuristics, an extensive review aimed at evaluating the impact of these methodologies in the Operational Research/Management Science literature is provided.
221 citations
01 Jan 2006
TL;DR: In this article, a probabilistic knowledge about future request arrivals is used to better manage the fleet of vehicles in a real-time setting, where dummy customers (representing forecasted requests) are introduced in vehicle routes to provide a good coverage of the territory.
Abstract: An important, but seldom investigated, issue in the field of dynamic vehicle routing and dispatching is how to exploit information about future events to improve decision making. In this paper, we address this issue in a real-time setting with a strategy based on probabilistic knowledge about future request arrivals to better manage the fleet of vehicles. More precisely, the new strategy introduces dummy customers (representing forecasted requests) in vehicle routes to provide a good coverage of the territory. This strategy is assessed through computational experiments performed in a simulated environment.
202 citations
TL;DR: The analysis of basic concepts and implementation method proves that VEO/VEP is a specialized form of CPS and it can play a vital role in the structure building of Industry 4.0.
132 citations
TL;DR: This paper provides a review on the use of machine learning techniques in the design of different elements of meta-heuristics for different purposes including algorithm selection, fitness evaluation, initialization, evolution, parameter setting, and cooperation.
106 citations
TL;DR: There is an adaptiveness in all parameters of the Particle Swarm Optimization algorithm, which starts with random values of the parameters and based on some conditions all parameters are adapted during the iterations.
89 citations