Multicriterion decision making (1997) [35 citations — 0 self]
Abstract:
Evolutionary Computation Applications Applying evolutionary computation (EC) to multicriterion decision making addresses two difficult problems: (i) searching intractably large and complex spaces and (ii) deciding among multiple objectives. Both of these problems are open areas of research, but relatively little work has been done on the combined problem of searching large spaces to meet multiple objectives. While multicriterion decision analysis usually assumes a small number of alternative solutions to choose from, or an ‘easy ’ (e.g. linear) space to search, research on robust search methods generally assumes some way of aggregating multiple objectives into a single figure of merit. This traditional separation of search and multicriterion decisions allows for two straightforward hybrid strategies: (i) make multicriterion decisions first, to aggregate objectives, then apply EC search to optimize the resulting figure of merit, or (ii) conduct multiple EC searches first using different aggregations of the objectives in order to obtain a range of alternative solutions, then make a multicriterion decision to choose among the
