Multi-objective evolutionaryalgorithms (MOEAs) have been the choice for generating a set of Pareto-optimal (PO) solutions in one run. However, these algorithms sometimes suffer slow and poor convergence toward the PO...
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Multi-objective evolutionaryalgorithms (MOEAs) have been the choice for generating a set of Pareto-optimal (PO) solutions in one run. However, these algorithms sometimes suffer slow and poor convergence toward the PO front. One of the remedies to improve their convergence is to couple global search of MOEAs with local search. However, such coupling brings other implementation challenges, such as what, when, and how many solutions can be chosen for local search with MOEAs? In this paper, these challenges are addressed by developing a local search module that can choose solutions for local search using a set of reference lines. The heuristic strategies are also developed with the module for determining the frequency of executing local search and for terminating MOEA adaptively using a statistical performance indicator. The proposed algorithm, which is referred to as RM(2)OEA, is tested on 2-objective ZDT and 3-objective DTLZ test problems. Results demonstrate faster and improved convergence of RM2 OEA over a benchmark MOEA from the literature.
Train timetabling is a difficult and very tightly constrained combinatorial problem that deals with the construction of train schedules. We focus on the particular problem of local reconstruction of the schedule follo...
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ISBN:
(纸本)0780393635
Train timetabling is a difficult and very tightly constrained combinatorial problem that deals with the construction of train schedules. We focus on the particular problem of local reconstruction of the schedule following a small perturbation, seeking minimisation of the total accumulated delay by adapting times of departure and arrival for each train and allocation of resources (tracks, routing nodes, etc.). We describe a permutation-based evolutionaryalgorithm that relies on a semi-greedy heuristic to gradually reconstruct the schedule by inserting trains one after another following the permutation. This algorithm can be hybridised with ILOG's commercial Mixed Integer Programming (MIP) tool CPLEX in a coarse-grained manner: the evolutionary part is used to quickly obtain a good but suboptimal solution and this intermediate solution is refined using CPLEX. Experimental results are presented on a large real-world case involving more than 1 million variables and 2 million constraints. On this particular problem instance, results are surprisingly good in the early part of the search where the evolutionaryalgorithm reaches excellent, although suboptimal, solutions much faster than CPLEX alone. Over the whole search, although the hybridized version is less efficient on average, it does better and faster in a non negligible minority of cases.
Recent work in computational structural biology focuses on modeling intrinsically dynamic proteins important to human biology and health. The energy landscapes of these proteins are rich in minima that correspond to a...
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ISBN:
(纸本)9781450342063
Recent work in computational structural biology focuses on modeling intrinsically dynamic proteins important to human biology and health. The energy landscapes of these proteins are rich in minima that correspond to alternative structures with which a dynamic protein binds to molecular partners in the cell. On such landscapes, evolutionaryalgorithms that switch their objective from classic optimization to mapping are more informative of protein structure function relationships. While techniques for mapping energy landscapes have been developed in computational chemistry and physics, protein landscapes are more difficult for mapping due to their high dimensionality and multimodality. In this paper, we describe a memetic evolutionary algorithm that is capable of efficiently mapping complex landscapes. In conjunction with a hall of fame mechanism, the algorithm makes use of a novel, lineage- and neighborhood-aware local search procedure for better exploration and mapping of complex landscapes. We evaluate the algorithm on several benchmark problems and demonstrate the superiority of the novel local search mechanism. In addition, we illustrate its effectiveness in mapping the complex multimodal landscape of an intrinsically dynamic protein important to human health.
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