The economical aspects related to the operation of active distribution networks are of big interest today. One way to improve the efficiency and the reliability indicators of these grids is to apply a network reconfig...
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ISBN:
(纸本)9781665418782
The economical aspects related to the operation of active distribution networks are of big interest today. One way to improve the efficiency and the reliability indicators of these grids is to apply a network reconfiguration in order to minimize the power loss. The purpose of this work is to present a distribution network reconfiguration through the iterative improvement algorithm.
The probabilistic traveling salesman problem is a paradigmatic example of a stochastic combinatorial optimization problem. For this problem, recently an estimation-based local search algorithm using delta evaluation h...
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The probabilistic traveling salesman problem is a paradigmatic example of a stochastic combinatorial optimization problem. For this problem, recently an estimation-based local search algorithm using delta evaluation has been proposed. In this paper, we adopt two well-known variance reduction procedures in the estimation-based local search algorithm: the first is an adaptive sampling procedure that selects the appropriate size of the sample to be used in Monte Carlo evaluation;the second is a procedure that adopts importance sampling to reduce the variance involved in the cost estimation. We investigate several possible strategies for applying these procedures to the given problem and we identify the most effective one. Experimental results show that a particular heuristic customization of the two procedures increases significantly the effectiveness of the estimation-based local search. (C) 2008 Elsevier B.V. All rights reserved.
In this paper, we develop a formalism called a distributed constraint satisfaction problem (distributed CSP) and algorithms for solving distributed CSPs. A distributed CSP is a constraint satisfaction problem in which...
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In this paper, we develop a formalism called a distributed constraint satisfaction problem (distributed CSP) and algorithms for solving distributed CSPs. A distributed CSP is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. Various application problems in Distributed Artificial Intelligence can be formalized as distributed CSPs. We present our newly developed technique called asynchronous backtracking that allows agents to act asynchronously and concurrently without any global control, while guaranteeing the completeness of the algorithm. Furthermore, we describe how the asynchronous backtracking algorithm can be modified into a more efficient algorithm called an asynchronous weak-commitment search, which can revise a bad decision without exhaustive search by changing the priority order of agents dynamically. The experimental results on various example problems show that the asynchronous weak-commitment search algorithm is, by far more, efficient than the asynchronous backtracking algorithm and can solve fairly large-scale problems.
The paper considers the iterative improvement algorithms, the efficiency of which substantially depends on the chosen parameters values. The problem of control of these parameters is formulated and discussed. We desig...
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The paper considers the iterative improvement algorithms, the efficiency of which substantially depends on the chosen parameters values. The problem of control of these parameters is formulated and discussed. We designed the modified algorithms where parameters are automatically adjusted at each iteration. The original and modified algorithms are applied to solve the problem of optimal control for the ecology-economic system.
Attribute reduction is a combinatorial optimization problem in data mining that aims to find minimal reducts from large set of attributes. The problem is exacerbated if the number of instances is large. Therefore, thi...
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ISBN:
(纸本)9783319131535;9783319131528
Attribute reduction is a combinatorial optimization problem in data mining that aims to find minimal reducts from large set of attributes. The problem is exacerbated if the number of instances is large. Therefore, this paper concentrates on a double treatment iterative improvement algorithm with intelligent selection on composite neighbourhood structure to solve the attribute reduction problems and to obtain near optimal reducts. The algorithm works iteratively with only accepting an improved solution. The proposed approach has been tested on a set of 13 benchmark datasets taken from the University of California, Irvine (UCI) machine learning repository in line with the state-of-the-art methods. The 13 datasets have been chosen due to the differences in size and complexity in order to test the stability of the proposed algorithm. The experimental results show that the proposed approach is able to produce competitive results for the tested datasets.
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