Simulation Based Multi-Objective Optimization for Dynamic Business Processes

strategies are iterative and based on two steps: a NSGA-II algorithm performed on kriging response surfaces or kriging expected improvements and relevant enrichment methods composed

To solve the robust optimization problem, one way is to introduce a multi-objective optimization formulation where the first objective is the function itself and the second is

Interestingly, features based on average hypervolume measures (hv, hvd, nhv) are closely related to one another, for samples from both random and adaptive walks. This means that

For this variant, the number of colonies #Col is set to m + 1 and the number of pheromone structures is set to m, where m = |F | is the number of objectives to opti- mize: each

In this paper we present a literature review of the PDPTW followed our multi-objective approach, minimizing the compromise between total travel cost and total

Keywords: Classification systems, feature extraction, ensemble of classifiers, multi-objective genetic

ParadisEO architecture: Evolving Objects (EO) for the design of population- based metaheuristics, Moving Objects (MO) for the design of solution-based meta- heuristics and

This book of essays illustrates that gender­based and sexualized violence experienced within communities across the world is being rebuked by girls and young women who