The Experts below are selected from a list of 12 Experts worldwide ranked by ideXlab platform
Huynh Thi Thanh Binh - One of the best experts on this subject based on the ideXlab platform.
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evolutionary algorithm and multifactorial evolutionary algorithm on clustered shortest path tree problem
Information Sciences, 2021Co-Authors: Phan Thi Hong Hanh, Pham Dinh Thanh, Huynh Thi Thanh BinhAbstract:Abstract In literature, Clustered Shortest-Path Tree Problem (CluSPT) is an NP-hard problem. Previous studies focus on approximation algorithms which Search for an optimal solution in relatively large space. Thus, these algorithms consume a large amount of computational resources while the quality of obtained results is lower than expected. In order to enhance the performance of the Search process, this paper proposes two different approaches which are inspired by two perspectives of analyzing the CluSPT. The first approach intuition is to narrow down the Search space by reducing the original graph into a multi-graph with fewer nodes while maintaining the ability to find the optimal solution. The problem is then solved by a proposed evolutionary algorithm. This approach performs well on those datasets having small number of edges between clusters. However, the increase in the size of the datasets would cause the excessive redundant edges in multi-graph that pressurize Searching for potential solutions. The second approach overcomes this limitation by breaking down the multi-graph into a set of simple graphs. Every graph in this set is corresponding to a mutually exclusive Search space. From this point of view, the problem could be modeled into a bi-level optimization problem in which the Search space includes two nested Search spaces. Accordingly, the Nested Local Search Evolutionary Algorithm (N-LSEA) is introduced to Search for the optimal solution of glscluspt, the upper level uses a simple Local Search algorithm while the lower level uses the Genetic Algorithm. Due to the neighboring characteristics of the Local Search Step in the upper level, the lower level reduced graphs share the common traits among each others. Thus, the Multi-tasking Local Search Evolutionary Algorithm (MLSEA) is proposed to take advantages of these underlying commonalities by exploiting the implicit transfer across similar tasks of multi-tasking schemes. The improvement in experimental results over N-LSEA via this multi-tasking scheme inspires the future works to apply M-LSEA in graph-based problems, especially for those could be modeled into bi-level optimization.
Phan Thi Hong Hanh - One of the best experts on this subject based on the ideXlab platform.
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evolutionary algorithm and multifactorial evolutionary algorithm on clustered shortest path tree problem
Information Sciences, 2021Co-Authors: Phan Thi Hong Hanh, Pham Dinh Thanh, Huynh Thi Thanh BinhAbstract:Abstract In literature, Clustered Shortest-Path Tree Problem (CluSPT) is an NP-hard problem. Previous studies focus on approximation algorithms which Search for an optimal solution in relatively large space. Thus, these algorithms consume a large amount of computational resources while the quality of obtained results is lower than expected. In order to enhance the performance of the Search process, this paper proposes two different approaches which are inspired by two perspectives of analyzing the CluSPT. The first approach intuition is to narrow down the Search space by reducing the original graph into a multi-graph with fewer nodes while maintaining the ability to find the optimal solution. The problem is then solved by a proposed evolutionary algorithm. This approach performs well on those datasets having small number of edges between clusters. However, the increase in the size of the datasets would cause the excessive redundant edges in multi-graph that pressurize Searching for potential solutions. The second approach overcomes this limitation by breaking down the multi-graph into a set of simple graphs. Every graph in this set is corresponding to a mutually exclusive Search space. From this point of view, the problem could be modeled into a bi-level optimization problem in which the Search space includes two nested Search spaces. Accordingly, the Nested Local Search Evolutionary Algorithm (N-LSEA) is introduced to Search for the optimal solution of glscluspt, the upper level uses a simple Local Search algorithm while the lower level uses the Genetic Algorithm. Due to the neighboring characteristics of the Local Search Step in the upper level, the lower level reduced graphs share the common traits among each others. Thus, the Multi-tasking Local Search Evolutionary Algorithm (MLSEA) is proposed to take advantages of these underlying commonalities by exploiting the implicit transfer across similar tasks of multi-tasking schemes. The improvement in experimental results over N-LSEA via this multi-tasking scheme inspires the future works to apply M-LSEA in graph-based problems, especially for those could be modeled into bi-level optimization.
Pham Dinh Thanh - One of the best experts on this subject based on the ideXlab platform.
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evolutionary algorithm and multifactorial evolutionary algorithm on clustered shortest path tree problem
Information Sciences, 2021Co-Authors: Phan Thi Hong Hanh, Pham Dinh Thanh, Huynh Thi Thanh BinhAbstract:Abstract In literature, Clustered Shortest-Path Tree Problem (CluSPT) is an NP-hard problem. Previous studies focus on approximation algorithms which Search for an optimal solution in relatively large space. Thus, these algorithms consume a large amount of computational resources while the quality of obtained results is lower than expected. In order to enhance the performance of the Search process, this paper proposes two different approaches which are inspired by two perspectives of analyzing the CluSPT. The first approach intuition is to narrow down the Search space by reducing the original graph into a multi-graph with fewer nodes while maintaining the ability to find the optimal solution. The problem is then solved by a proposed evolutionary algorithm. This approach performs well on those datasets having small number of edges between clusters. However, the increase in the size of the datasets would cause the excessive redundant edges in multi-graph that pressurize Searching for potential solutions. The second approach overcomes this limitation by breaking down the multi-graph into a set of simple graphs. Every graph in this set is corresponding to a mutually exclusive Search space. From this point of view, the problem could be modeled into a bi-level optimization problem in which the Search space includes two nested Search spaces. Accordingly, the Nested Local Search Evolutionary Algorithm (N-LSEA) is introduced to Search for the optimal solution of glscluspt, the upper level uses a simple Local Search algorithm while the lower level uses the Genetic Algorithm. Due to the neighboring characteristics of the Local Search Step in the upper level, the lower level reduced graphs share the common traits among each others. Thus, the Multi-tasking Local Search Evolutionary Algorithm (MLSEA) is proposed to take advantages of these underlying commonalities by exploiting the implicit transfer across similar tasks of multi-tasking schemes. The improvement in experimental results over N-LSEA via this multi-tasking scheme inspires the future works to apply M-LSEA in graph-based problems, especially for those could be modeled into bi-level optimization.
Bryant A Julstrom - One of the best experts on this subject based on the ideXlab platform.
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an effective genetic algorithm for the minimum label spanning tree problem
Genetic and Evolutionary Computation Conference, 2006Co-Authors: Jeremiah Nummela, Bryant A JulstromAbstract:Given a connected, undirected graph G with labeled edges, the minimum label spanning tree problem seeks a spanning tree on G to whose edges are attached the smallest possible number of labels. A greedy heuristic for this NP-hard problem greedily chooses labels so as to reduce the number of components in the subgraphs they induce as quickly as possible. A genetic algorithm for the problem encodes candidate solutions as per mutations of the labels; an initial segment of such a chromosome lists the labels that appear on the edges in the chromosome's tree. Three versions of the GA apply generic or heuristic crossover and mutation operators and a Local Search Step. In tests on 27 randomly-generated instances of the minimum-label spanning tree problem, versions of the GA that apply generic operators, with and without the Local Search Step, perform less well than the greedy heuristic, but a version that applies the Local Search Step and operators tailored to the problem returns solutions that require on average 10 fewer labels than the heuristic's.
Jeremiah Nummela - One of the best experts on this subject based on the ideXlab platform.
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an effective genetic algorithm for the minimum label spanning tree problem
Genetic and Evolutionary Computation Conference, 2006Co-Authors: Jeremiah Nummela, Bryant A JulstromAbstract:Given a connected, undirected graph G with labeled edges, the minimum label spanning tree problem seeks a spanning tree on G to whose edges are attached the smallest possible number of labels. A greedy heuristic for this NP-hard problem greedily chooses labels so as to reduce the number of components in the subgraphs they induce as quickly as possible. A genetic algorithm for the problem encodes candidate solutions as per mutations of the labels; an initial segment of such a chromosome lists the labels that appear on the edges in the chromosome's tree. Three versions of the GA apply generic or heuristic crossover and mutation operators and a Local Search Step. In tests on 27 randomly-generated instances of the minimum-label spanning tree problem, versions of the GA that apply generic operators, with and without the Local Search Step, perform less well than the greedy heuristic, but a version that applies the Local Search Step and operators tailored to the problem returns solutions that require on average 10 fewer labels than the heuristic's.
