Abstract
Branke [30] considered robust optimization as a special case of dynamic optimization, where solutions cannot be adapted fast enough to keep in pace with environmental changes. In such cases, it would be desirable to find solutions that perform reasonably well within some range of change. In fact, many real-world applications involve the simultaneous optimization of several competing objectives and are susceptible to decision or environmental parameter variations, which result in large or unacceptable performance variations. Robust optimization of multi-objective problems is the third and final type of uncertainty considered in this work and it involves the optimization of a set of Pareto optimal solutions that remains satisfactory in face of parametric variations.This chapter addresses the issue of robust multi-objective optimization by presenting a robust continuous multi-objective test suite with features of noise-induced solution space, fitness landscape and decision space variation. In addition, the vehicle routing problem with stochastic demand (VRPSD) is presented a practical example of robust combinatorial multi-objective optimization problems.KeywordsPareto FrontMultiobjective OptimizationRobust OptimizationVehicle Rout Problem With Time WindowRoute FailureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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