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An experimental investigation of the synergetic effect of multiple agents working together in the A-Team

Barbucha D., Jędrzejowicz P.

Systems Science

2008

Vol. 34, no 2

55-62

The paper aims at evaluating experimentally the existence and strength of the synergetic effect produced by a joint effort of a number of optimization agents solving instances of one of the classic combinatorial optimization problems - the vehicle routing problem. The computational experiment carried out involved JABAT environment, which is a middleware supporting the construction of the dedicated A-Team architectures. Several types of optimization agents have been constructed and tested. The main factors in the reported experiment are structure and composition of the set of agents working in parallel on solving instances of the problem at hand.

Routing of hazardous materials using a population learning algorithm

Barbucha D.

Archives of Transport

2005

Vol. 17, iss.3-4

11-24

The hazardous materials transportation covers a large part of economic activities in the industrial countries. Amount of hazardous materials transported over the world grows systematically. Although hazardous materials accidents are rare events, their impact can be severe for the human and natural environment. This feature of hazardous materials transportation adds a special concern in the decision-making process of hazardous materials transportation. The problem of optimal routing of hazardous materials is described and formally defined in the paper as an extension of classical vehicle routing problem by taking into account minimisation of the risk. A new heuristic algorithm for solving the routing of hazardous materials problem based on evolution of population is presented in the paper. It is also evaluated using benchmark data sets and then solutions generated by the algorithm are compared with optimal ones.

W pracy został opisany i formalnie zdefiniowany problem optymalnego wyznaczenia tras przewozu materiałów niebezpiecznych, jako rozszerzenie klasycznego problemu wyznaczenia tras pojazdów przez uwzględnienie ryzyka związanego z transportem. Dla omawianego problemu zaproponowano nowy algorytm heurystyczny oparty na ewolucji populacji. Został on poddany eksperymentom obliczeniowym z użyciem danych benchmarkowych, a następnie na podstawie wyników otrzymanych za pomocą tego algorytmu dokonano jego oceny.