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Proceedings of GO 2005, pp. 247 – 252.A Hybrid Multi-Agent Collaborative SearchApplied to the Solution of Space Mission Design ProblemsMassimiliano VasileDepartment of Aerospace Engineering, Politecnico di Milano, via La Masa 34, 20156, Milan, Italy, vasile@aero.polimi.itAbstractKeywords:In this paper a novel algorithm is proposed for the solution of single and multiple objective problemsin space mission analysis and design. The underlying idea is to look for the set collecting all thesolutions that satisfy a given optimality criterion or property. A population of agents is associatedto a set of general solutions and behavioural rules are devised for each agent and for the entirepopulation in order to drive the agents toward the optimal set. This mechanism is hybridised witha particular adaptive domain decomposition technique, which is used either to extend the search orto favour convergence. Some example cases will show the effectiveness of the proposed approach.global optimisation, multiobjective optimisation, hybrid methods, space mission design.1. IntroductionIn many space mission design problems there is the need to extensively search for optimalsolutions in highly complex solution domains. Typical problems of trajectory design, optimalcontrol, multidisciplinary design and concurrent engineering can be translated into multiobjectiveoptimisation problems characterised by multimodal functions and heterogeneousparameters (mixed integer-real parameters) [1,2]. Moreover it is often required to procure reliablesets of solutions, where reliability is measured in terms of repeatability and predictabilityof the result. This would suggest a purely deterministic approach to many problems occurringin mission analysis. In turn the request for exhaustiveness of the search leads often tolong computational time (few days up to a week). Although this is generally acceptable comparedto standard development time of a space mission, it might become not acceptable inthe early or preliminary phase of the design or in case of extremely costly evaluations (as inmultidisciplinary design). An alternative to deterministic search could be to use stochasticbased approaches, especially when the solution space is deceiving. In this case predictabilityof the algorithm has to be dropped. In particular in most stochastic algorithms the width ofthe intervals for each parameter is not always easy to define although it can change the probabilityto find the global optimum. Moreover the need for sets of optimal solutions, ratherthan a single global optimum, requires a reformulation of single objective problems in orderto allow the convergence to a set as for multiobjective problems. In this paper a unifying formulationof both multiobjective and single objective problems is proposed and an algorithmthat combines a deterministic domain decomposition technique and a stochastic-based multiagentsearch is presented. This algorithm is intended to improve performances and reliabilityof optimisation in some space mission design problems by allowing a partial or completereconstruction of the set of optimal solutions. The domain decomposition procedure generatesbranches along which the multiagent collaborative search is performed. Non promisingbranches are then pruned while promising ones are further explored until termination. Thebranching scheme is initially inferred and then updated on the basis of the outcome of the