Currently,the industry is experiencing an exponential increase in dealing with binary-based combinatorial problems.In this sense,metaheuristics have been a common trend in the field in order to design approaches to so...Currently,the industry is experiencing an exponential increase in dealing with binary-based combinatorial problems.In this sense,metaheuristics have been a common trend in the field in order to design approaches to solve them successfully.Thus,a well-known strategy consists in the use of algorithms based on discrete swarms transformed to perform in binary environments.Following the No Free Lunch theorem,we are interested in testing the performance of the Fruit Fly Algorithm,this is a bio-inspired metaheuristic for deducing global optimization in continuous spaces,based on the foraging behavior of the fruit fly,which usually has much better sensory perception of smell and vision than any other species.On the other hand,the Set Coverage Problem is a well-known NP-hard problem with many practical applications,including production line balancing,utility installation,and crew scheduling in railroad and mass transit companies.In this paper,we propose different binarization methods for the Fruit Fly Algorithm,using Sshaped and V-shaped transfer functions and various discretization methods to make the algorithm work in a binary search space.We are motivated with this approach,because in this way we can deliver to future researchers interested in this area,a way to be able to work with continuous metaheuristics in binary domains.This new approach was tested on benchmark instances of the Set Coverage Problem and the computational results show that the proposed algorithm is robust enough to produce good results with low computational cost.展开更多
We introduce a hybrid algorithm for the 01 multidimensional multi-objective knapsack problem. This algorithm, called GTS MOKP, combines a genetic procedure and a tabu search operator. The algorithm is evaluated on 9 ...We introduce a hybrid algorithm for the 01 multidimensional multi-objective knapsack problem. This algorithm, called GTS MOKP, combines a genetic procedure and a tabu search operator. The algorithm is evaluated on 9 well-known benchmark instances and shows highly competitive results compared with two state-of-the-art algorithms.展开更多
Gene subset selection is essential for classification and analysis of microarray data. However, gene selection is known to be a very difficult task since gene expression data not only have high dimensionalities, but a...Gene subset selection is essential for classification and analysis of microarray data. However, gene selection is known to be a very difficult task since gene expression data not only have high dimensionalities, but also contain redundant information and noises. To cope with these difficulties, this paper introduces a fuzzy logic based pre-processing approach composed of two main steps. First, we use fuzzy inference rules to transform the gene expression levels of a given dataset into fuzzy values. Then we apply a similarity relation to these fuzzy values to define fuzzy equiva- lence groups, each group containing strongly similar genes. Dimension reduction is achieved by considering for each group of similar genes a single representative based on mutual information. To assess the usefulness of this approach, exten- sive experimentations were carried out on three well-known public datasets with a combined classification model using three statistic filters and three classifiers.展开更多
In wireless artificial intelligent computing systems,the construction of backbone network,which determines the optimum network for a set of given terminal nodes like users,switches,and concentrators,can be naturally f...In wireless artificial intelligent computing systems,the construction of backbone network,which determines the optimum network for a set of given terminal nodes like users,switches,and concentrators,can be naturally formed as the Steiner tree problem.The Steiner tree problem asks for a minimum edge-weighted tree spanning a given set of terminal vertices from a given graph.As a well-known graph problem,many algorithms have been developed for solving this computationally challenging problem in the past decades.However,existing algorithms typically encounter difficulties for solving large instances,i.e.,graphs with a high number of vertices and terminals.In this paper,we present a novel partition-and-merge algorithm for effectively handle large-scale graphs.The algorithm breaks the input network into small subgraphs and then merges the subgraphs in a bottom-up manner.In the merging procedure,partial Steiner trees in the subgraphs are also created and optimized by an efficient local optimization.When the merging procedure ends,the algorithm terminates and reports the final solution for the input graph.We evaluated the algorithm on a wide range of benchmark instances,showing that the algorithm outperforms the best-known algorithms on large instances and competes favorably with them on small or middle-sized instances.展开更多
文摘Currently,the industry is experiencing an exponential increase in dealing with binary-based combinatorial problems.In this sense,metaheuristics have been a common trend in the field in order to design approaches to solve them successfully.Thus,a well-known strategy consists in the use of algorithms based on discrete swarms transformed to perform in binary environments.Following the No Free Lunch theorem,we are interested in testing the performance of the Fruit Fly Algorithm,this is a bio-inspired metaheuristic for deducing global optimization in continuous spaces,based on the foraging behavior of the fruit fly,which usually has much better sensory perception of smell and vision than any other species.On the other hand,the Set Coverage Problem is a well-known NP-hard problem with many practical applications,including production line balancing,utility installation,and crew scheduling in railroad and mass transit companies.In this paper,we propose different binarization methods for the Fruit Fly Algorithm,using Sshaped and V-shaped transfer functions and various discretization methods to make the algorithm work in a binary search space.We are motivated with this approach,because in this way we can deliver to future researchers interested in this area,a way to be able to work with continuous metaheuristics in binary domains.This new approach was tested on benchmark instances of the Set Coverage Problem and the computational results show that the proposed algorithm is robust enough to produce good results with low computational cost.
文摘We introduce a hybrid algorithm for the 01 multidimensional multi-objective knapsack problem. This algorithm, called GTS MOKP, combines a genetic procedure and a tabu search operator. The algorithm is evaluated on 9 well-known benchmark instances and shows highly competitive results compared with two state-of-the-art algorithms.
基金the French Ouest Genopole Program and the "Bioinformatique Lig'erienne" project of the "Pays de la Loire" Region.Huerta EB is supported by a CoSNET research schol-arship
文摘Gene subset selection is essential for classification and analysis of microarray data. However, gene selection is known to be a very difficult task since gene expression data not only have high dimensionalities, but also contain redundant information and noises. To cope with these difficulties, this paper introduces a fuzzy logic based pre-processing approach composed of two main steps. First, we use fuzzy inference rules to transform the gene expression levels of a given dataset into fuzzy values. Then we apply a similarity relation to these fuzzy values to define fuzzy equiva- lence groups, each group containing strongly similar genes. Dimension reduction is achieved by considering for each group of similar genes a single representative based on mutual information. To assess the usefulness of this approach, exten- sive experimentations were carried out on three well-known public datasets with a combined classification model using three statistic filters and three classifiers.
基金funded by the Sichuan Province Science and Technology Program(No.2024NSFTD0033)the National Natural Science Foundation of China(Nos.62372093 and 62376055)+1 种基金the Natural Science Foundation of Sichuan Province of China(No.2023NSFSC1415)the Shenzhen Agency of Innovation(No.KJZD20240903095712016).
文摘In wireless artificial intelligent computing systems,the construction of backbone network,which determines the optimum network for a set of given terminal nodes like users,switches,and concentrators,can be naturally formed as the Steiner tree problem.The Steiner tree problem asks for a minimum edge-weighted tree spanning a given set of terminal vertices from a given graph.As a well-known graph problem,many algorithms have been developed for solving this computationally challenging problem in the past decades.However,existing algorithms typically encounter difficulties for solving large instances,i.e.,graphs with a high number of vertices and terminals.In this paper,we present a novel partition-and-merge algorithm for effectively handle large-scale graphs.The algorithm breaks the input network into small subgraphs and then merges the subgraphs in a bottom-up manner.In the merging procedure,partial Steiner trees in the subgraphs are also created and optimized by an efficient local optimization.When the merging procedure ends,the algorithm terminates and reports the final solution for the input graph.We evaluated the algorithm on a wide range of benchmark instances,showing that the algorithm outperforms the best-known algorithms on large instances and competes favorably with them on small or middle-sized instances.
