The Experts below are selected from a list of 23820 Experts worldwide ranked by ideXlab platform
Fred Glover - One of the best experts on this subject based on the ideXlab platform.
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advanced tabu search algorithms for bipartite boolean Quadratic programs guided by strategic oscillation and path relinking
2020Co-Authors: Yang Wang, Fred GloverAbstract:The bipartite Boolean Quadratic Programming Problem (BBQP) is a generalization of the well-studied NP-hard Boolean Quadratic Programming Problem and can be regarded as a unified model for many grap...
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adaptive tabu search with strategic oscillation for the bipartite boolean Quadratic Programming Problem with partitioned variables
2018Co-Authors: Yang Wang, Abraham P Punnen, Fred GloverAbstract:Abstract The bipartite boolean Quadratic Programming Problem with partitioned variables (BQP-PV) is an NP-hard combinatorial optimization Problem that accommodates a variety of real-life applications. We propose an adaptive tabu search with strategic oscillation (ATS-SO) approach for BQP-PV, which employs a multi-pass search framework where each pass consists of an initial constructive phase, an adaptive tabu search phase and a frequency-driven strategic oscillation phase. In particular, the adaptive tabu search phase combines different move operators to collectively conduct neighborhood exploration and an adaptive tabu tenure management mechanism that obviates the task of determining a proper tabu tenure. The frequency-driven strategic oscillation phase diversifies the search when the search reaches a critical solution, drawing on a destructive procedure to unassign some variables by reference to frequency memory and a constructive procedure to re-assign these variables utilizing both frequency memory and Problem specific knowledge. Computational experiments on five classes of Problem instances indicate that the proposed ATS-SO algorithm is able to find improved solutions for 14 instances and match the best known solutions for all remaining instances, whereas no previous method has succeeded in finding the previous best solutions for all instances. Statistical tests indicate that ATS-SO significantly outperforms the state-of-the-art algorithms in the literature.
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the unconstrained binary Quadratic Programming Problem a survey
2014Co-Authors: Gary A Kochenberger, Fred Glover, Jinkao Hao, Mark Lewis, Haibo Wang, Yang WangAbstract:In recent years the unconstrained binary Quadratic program (UBQP) has grown in importance in the field of combinatorial optimization due to its application potential and its computational challenge. Research on UBQP has generated a wide range of solution techniques for this basic model that encompasses a rich collection of Problem types. In this paper we survey the literature on this important model, providing an overview of the applications and solution methods.
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integrating tabu search and vlsn search to develop enhanced algorithms a case study using bipartite boolean Quadratic programs
2013Co-Authors: Fred Glover, Abraham P Punnen, Gary A KochenbergerAbstract:The bipartite boolean Quadratic Programming Problem (BBQP) is a generalization of the well studied boolean Quadratic Programming Problem. The model has a variety of real life applications; however, empirical studies of the model are not available in the literature, except in a few isolated instances. In this paper, we develop efficient heuristic algorithms based on tabu search, very large scale neighborhood (VLSN) search, and a hybrid algorithm that integrates the two. The computational study establishes that effective integration of simple tabu search with VLSN search results in superior outcomes, and suggests the value of such an integration in other settings. Complexity analysis and implementation details are provided along with conclusions drawn from experimental analysis. In addition, we obtain solutions better than the best previously known for almost all medium and large size benchmark instances.
Yongsheng Liang - One of the best experts on this subject based on the ideXlab platform.
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decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural networks
2012Co-Authors: Sanfeng Chen, Bo Liu, Yongsheng LiangAbstract:This paper studies the decentralized kinematic control of multiple redundant manipulators for the cooperative task execution Problem. The Problem is formulated as a constrained Quadratic Programming Problem and then a recurrent neural network with independent modules is proposed to solve the Problem in a distributed manner. Each module in the neural network controls a single manipulator in real time without explicit communication with others and all the modules together collectively solve the common task. The global stability of the proposed neural network and the optimality of the neural solution are proven in theory. Application orientated simulations demonstrate the effectiveness of the proposed method.
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decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural networks
2012Co-Authors: Shuai Li, Sanfeng Chen, Yangming Li, Yongsheng LiangAbstract:This paper studies the decentralized kinematic control of multiple redundant manipulators for the cooperative task execution Problem. The Problem is formulated as a constrained Quadratic Programming Problem and then a recurrent neural network with independent modules is proposed to solve the Problem in a distributed manner. Each module in the neural network controls a single manipulator in real time without explicit communication with others and all the modules together collectively solve the common task. The global stability of the proposed neural network and the optimality of the neural solution are proven in theory. Application orientated simulations demonstrate the effectiveness of the proposed method.
Abraham P Punnen - One of the best experts on this subject based on the ideXlab platform.
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adaptive tabu search with strategic oscillation for the bipartite boolean Quadratic Programming Problem with partitioned variables
2018Co-Authors: Yang Wang, Abraham P Punnen, Fred GloverAbstract:Abstract The bipartite boolean Quadratic Programming Problem with partitioned variables (BQP-PV) is an NP-hard combinatorial optimization Problem that accommodates a variety of real-life applications. We propose an adaptive tabu search with strategic oscillation (ATS-SO) approach for BQP-PV, which employs a multi-pass search framework where each pass consists of an initial constructive phase, an adaptive tabu search phase and a frequency-driven strategic oscillation phase. In particular, the adaptive tabu search phase combines different move operators to collectively conduct neighborhood exploration and an adaptive tabu tenure management mechanism that obviates the task of determining a proper tabu tenure. The frequency-driven strategic oscillation phase diversifies the search when the search reaches a critical solution, drawing on a destructive procedure to unassign some variables by reference to frequency memory and a constructive procedure to re-assign these variables utilizing both frequency memory and Problem specific knowledge. Computational experiments on five classes of Problem instances indicate that the proposed ATS-SO algorithm is able to find improved solutions for 14 instances and match the best known solutions for all remaining instances, whereas no previous method has succeeded in finding the previous best solutions for all instances. Statistical tests indicate that ATS-SO significantly outperforms the state-of-the-art algorithms in the literature.
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markov chain methods for the bipartite boolean Quadratic Programming Problem
2016Co-Authors: Daniel Karapetyan, Abraham P Punnen, Andrew J ParkesAbstract:We study the Bipartite Boolean Quadratic Programming Problem (BBQP) which is an extension of the well known Boolean Quadratic Programming Problem (BQP). Applications of the BBQP include mining discrete patterns from binary data, approximating matrices by rank-one binary matrices, computing the cut-norm of a matrix, and solving optimisation Problems such as maximum weight biclique, bipartite maximum weight cut, maximum weight induced sub-graph of a bipartite graph, etc. For the BBQP, we first present several algorithmic components, specifically, hill climbers and mutations, and then show how to combine them in a high-performance metaheuristic. Instead of hand-tuning a standard metaheuristic to test the efficiency of the hybrid of the components, we chose to use an automated generation of a multi-component metaheuristic to save human time, and also improve objectivity in the analysis and comparisons of components. For this we designed a new metaheuristic schema which we call Conditional Markov Chain Search (CMCS). We show that CMCS is flexible enough to model several standard metaheuristics; this flexibility is controlled by multiple numeric parameters, and so is convenient for automated generation. We study the configurations revealed by our approach and show that the best of them outperforms the previous state-of-the-art BBQP algorithm by several orders of magnitude. In our experiments we use benchmark instances introduced in the preliminary version of this paper and described here, which have already become the de facto standard in the BBQP literature.
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integrating tabu search and vlsn search to develop enhanced algorithms a case study using bipartite boolean Quadratic programs
2013Co-Authors: Fred Glover, Abraham P Punnen, Gary A KochenbergerAbstract:The bipartite boolean Quadratic Programming Problem (BBQP) is a generalization of the well studied boolean Quadratic Programming Problem. The model has a variety of real life applications; however, empirical studies of the model are not available in the literature, except in a few isolated instances. In this paper, we develop efficient heuristic algorithms based on tabu search, very large scale neighborhood (VLSN) search, and a hybrid algorithm that integrates the two. The computational study establishes that effective integration of simple tabu search with VLSN search results in superior outcomes, and suggests the value of such an integration in other settings. Complexity analysis and implementation details are provided along with conclusions drawn from experimental analysis. In addition, we obtain solutions better than the best previously known for almost all medium and large size benchmark instances.
Naiyang Deng - One of the best experts on this subject based on the ideXlab platform.
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nonparallel hyperplane support vector machine for binary classification Problems
2014Co-Authors: Yuanhai Shao, Weijie Chen, Naiyang DengAbstract:In this paper, we propose a nonparallel hyperplane support vector machine (NHSVM) for binary classification Problems. Our proposed NHSVM is formulated by clustering the training points according to the similarity between classes. It constructs two nonparallel hyperplanes simultaneously by solving a single Quadratic Programming Problem, and is consistent between its predicting and training processes - an essential difference that distinguishes it from other nonparallel SVMs. This proposed NHSVM has been analyzed theoretically and implemented experimentally. The results of experiments conducted using it on both artificial and publicly available benchmark datasets confirm its feasibility and efficacy, especially for ''Cross Planes'' datasets and datasets with heteroscedastic noise.
Yang Wang - One of the best experts on this subject based on the ideXlab platform.
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advanced tabu search algorithms for bipartite boolean Quadratic programs guided by strategic oscillation and path relinking
2020Co-Authors: Yang Wang, Fred GloverAbstract:The bipartite Boolean Quadratic Programming Problem (BBQP) is a generalization of the well-studied NP-hard Boolean Quadratic Programming Problem and can be regarded as a unified model for many grap...
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adaptive tabu search with strategic oscillation for the bipartite boolean Quadratic Programming Problem with partitioned variables
2018Co-Authors: Yang Wang, Abraham P Punnen, Fred GloverAbstract:Abstract The bipartite boolean Quadratic Programming Problem with partitioned variables (BQP-PV) is an NP-hard combinatorial optimization Problem that accommodates a variety of real-life applications. We propose an adaptive tabu search with strategic oscillation (ATS-SO) approach for BQP-PV, which employs a multi-pass search framework where each pass consists of an initial constructive phase, an adaptive tabu search phase and a frequency-driven strategic oscillation phase. In particular, the adaptive tabu search phase combines different move operators to collectively conduct neighborhood exploration and an adaptive tabu tenure management mechanism that obviates the task of determining a proper tabu tenure. The frequency-driven strategic oscillation phase diversifies the search when the search reaches a critical solution, drawing on a destructive procedure to unassign some variables by reference to frequency memory and a constructive procedure to re-assign these variables utilizing both frequency memory and Problem specific knowledge. Computational experiments on five classes of Problem instances indicate that the proposed ATS-SO algorithm is able to find improved solutions for 14 instances and match the best known solutions for all remaining instances, whereas no previous method has succeeded in finding the previous best solutions for all instances. Statistical tests indicate that ATS-SO significantly outperforms the state-of-the-art algorithms in the literature.
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the unconstrained binary Quadratic Programming Problem a survey
2014Co-Authors: Gary A Kochenberger, Fred Glover, Jinkao Hao, Mark Lewis, Haibo Wang, Yang WangAbstract:In recent years the unconstrained binary Quadratic program (UBQP) has grown in importance in the field of combinatorial optimization due to its application potential and its computational challenge. Research on UBQP has generated a wide range of solution techniques for this basic model that encompasses a rich collection of Problem types. In this paper we survey the literature on this important model, providing an overview of the applications and solution methods.
