Journal Article10.1007/S00500-006-0096-0
Multi-Objective Optimization using Grid Computing
Antonio J. Nebro,Enrique Alba,Francisco Luna +2 more
- 17 Jan 2007
- Vol. 11, Iss: 6, pp 531-540
TL;DR: The conclusion is that this kind of systems can provide to the community with a large and precise set of Pareto fronts that would be otherwise unknown.
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Abstract: This paper analyzes some technical and practical issues concerning the use of parallel systems to solve multi-objective optimization problems using enumerative search. This technique constitutes a conceptually simple search strategy, and it is based on evaluating each possible solution from a given finite search space. The results obtained by enumeration are impractical for most computer platforms and researchers, but they exhibit a great interest because they can be used to be compared against the values obtained by stochastic techniques. We analyze here the use of a grid computing system to cope with the limits of enumerative search. After evaluating the performance of the sequential algorithm, we present, first, a parallel algorithm targeted to multiprocessor systems. Then, we design a distributed version prepared to be executed on a federation of geographically distributed computers known as a computational grid. Our conclusion is that this kind of systems can provide to the community with a large and precise set of Pareto fronts that would be otherwise unknown.
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Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization
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A new multi-objective particle swarm optimizer using empirical movement and diversified search strategies
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