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Memetic algorithms (MAs), which have materialized as a fusion of population based global search and individual lifetime learning (i.e., local search) in the literature, have been widely used in real world applications to solve complex optimization problems. The balance of global and local search in MA plays a key role in defining the performance of MA in problem solving. The probabilistic memetic...
A three-dimensional vehicle routing problem for urban last-mile logistics (3-DVRP for ULML) is formulated, toinclude the vertical dimension, aim to account for verticaldistances in a high-rise urban environment. The crux of theproposed objective function lies in addressing a research gap to account for the vertical distance and minimizing the total node-to-node (N2N) travel and service time which...
A major drawback of evolutionary optimization approaches in the literature is the apparent lack of automated knowledge transfers and reuse across problems. Particularly, evolutionary optimization methods generally start a search from scratch or ground zero state, independent of how similar the given new problem of interest is to those optimized previously. In this paper, we present a study on the...
Capacitated arc routing problem (CARP) has attracted much attention due to its generality to many real world problems. Memetic algorithm (MA), among other meta-heuristic search methods, has been shown to achieve competitive performances in solving CARP ranging from small to medium size. In this paper we propose a formal probabilistic memetic algorithm for CARP that is equipped with an adaptation mechanism...
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