We present a self-adaptive and distributed metaheuristic called CoalitionBased Metaheuristic (CBM). This method is based on the Agent Metaheuristic Framework (AMF) and hyper-heuristic approach. In CBM, several agents, grouped in a coalition, concurrently explore the search space of a given problem instance. Each agent modifies a solution with a set of operators. The selection of these operators is determined by heuristic rules dynamically adapted by individual and collective learning mechanisms. The intention of this study is to exploit AMF and hyper-heuristic approaches to conceive an efficient, flexible and modular metaheuristic. AMF provides a generic model of metaheuristic that encourages modularity, and hyper-heuristic approach gives some guidelines to design flexible search methods. The performance of CBM is assessed by computational experiments on the vehicle routing problem.
Abstract.Recently, the quality and the diversity of transport services are more and more required. Moreover, in case of a great deal of services and selling goods, a significant part of price is transport cost. Thus, the design of models and applications which make possible efficient transport planning and scheduling becomes important. A great deal of real transport problems may be modelled by using Pickup and Delivery Problem with Time Windows (PDPTW) and capacity constraints, which is based on the realization of a set of transport requests by a fleet of vehicles with given capacities. Each request is described by pickup and delivery locations, time periods when pickup and delivery operations should be performed and needed load. Application of evolutionary approach has brought good results in case of another, simpler transport problem -the Vehicle Routing Problem with Time Windows (VRPTW). This paper is aimed at proposing a straightforward extension of VRPTW based heuristics for the PDPTW.
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