The major problem in solving combinatorial optimization problems is the huge size of the search space. To explore the search space in a reasonable time, using smart search algorithms is inevitable. One of the main dif...
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The major problem in solving combinatorial optimization problems is the huge size of the search space. To explore the search space in a reasonable time, using smart search algorithms is inevitable. One of the main difficulties in implementing search methods is the lack of a uniform, high-level template for all search paradigms. In this paper, we propose a high-level, parametric template suitable for modeling languages which covers both tree search and local search.
Interacted Multiple Ant Colonies optimization (IMACO) is a newly proposed framework. In this framework several colonies of artificial ants are utilized. These colonies are working cooperatively to solve an optimizatio...
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ISBN:
(纸本)9781424459841
Interacted Multiple Ant Colonies optimization (IMACO) is a newly proposed framework. In this framework several colonies of artificial ants are utilized. These colonies are working cooperatively to solve an optimization problem using some interaction technique. Exploration technique is doing an essential job in this framework. This technique is responsible for directing the activity of utilized colonies towards the different parts of the huge search space. This paper describes the newly proposed IMACO framework and proposes an effective exploration technique. Computational tests show that the new exploration technique can furthermore improve the IMACO performance. These tests also show the capability of IMACO to outperform other well known ant algorithms like ant colony system and max-min ant system.
We consider the use of random search for high dimensional optimizationproblems where the objective function to be optimized can only be computed with error. Random search is easy to carry out, but extraction of infor...
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ISBN:
(纸本)9781424498642
We consider the use of random search for high dimensional optimizationproblems where the objective function to be optimized can only be computed with error. Random search is easy to carry out, but extraction of information concerning the objective function is not so straightforward. We propose fitting a statistical model to the objective function values obtained in such a search, and show how the fitted model can be used to estimate the best value obtained when the search effort is limited and how this value compares with the unknown true optimum value. A possible use of this approach is in combinatorial optimization problems. The dimension in such a problem is not usually considered, but if a dimension can be associated with it, then it is likely to be high. We illustrate our method with a numerical example involving a travelling salesman problem.
The Ant Colony optimization (ACO) is a recent meta-heuristic algorithm for solving hard combinatorial optimization problems. The algorithm, however, has the weaknesses of premature convergence and low search speed, wh...
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ISBN:
(纸本)9783642158520
The Ant Colony optimization (ACO) is a recent meta-heuristic algorithm for solving hard combinatorial optimization problems. The algorithm, however, has the weaknesses of premature convergence and low search speed, which greatly hinder its application. In order to improve the performance of the algorithm, a hybrid ant colony optimization (HACO) is presented by adjusting pheromone approach, introducing a disaster operator, and combining the ACO with the saving algorithm and lambda-interchange mechanism. Then, the HACO is applied to solve the vehicle routing problem with time windows. By comparing the computational results with the previous literature, it is concluded that the HACO is an effective way to solve combinatorial optimization problems.
The Ant Colony optimization (ACO) is a recent meta-heuristic algorithm for solving hard combinatorial optimization problems. The algorithm, however, has the weaknesses of premature convergence and low search speed, wh...
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The Ant Colony optimization (ACO) is a recent meta-heuristic algorithm for solving hard combinatorial optimization problems. The algorithm, however, has the weaknesses of premature convergence and low search speed, which greatly hinder its application. In order to improve the performance of the algorithm, a hybrid ant colony optimization (HACO) is presented by adjusting pheromone approach, introducing a disaster operator, and combining the ACO with the saving algorithm and λ-interchange mechanism. Then, the HACO is applied to solve the vehicle routing problem with time windows. By comparing the computational results with the previous literature, it is concluded that the HACO is an effective way to solve combinatorial optimization problems.
Bipartite subgraph problem is an important example of a class of combinatorial optimization problems. It has many important applications in modeling matching problem, modern coding theory, Communication network, and c...
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Bipartite subgraph problem is an important example of a class of combinatorial optimization problems. It has many important applications in modeling matching problem, modern coding theory, Communication network, and computer science. The goal of this NP-complete problem is to find a bipartite subgraph with maximum number of edges of the given graph. In this paper, for efficiently solving the problem, we propose a genetic algorithm-based approach in which the genetic operators are performed based on the condition instead of probability. The proposed algorithm is tested on a large number of instances, and the experimental results show that the proposed algorithm is superior to its competitors. (C) 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Soils, Inc.
Ant colony optimization (ACO) algorithms are a recently developed, population-based approach which has been successfully applied to optimizationproblems. However, in the ACO algorithms it is difficult to adjust the b...
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Ant colony optimization (ACO) algorithms are a recently developed, population-based approach which has been successfully applied to optimizationproblems. However, in the ACO algorithms it is difficult to adjust the balance between intensification and diversification and thus the performance is not always very well. In this work, we propose an improved ACO algorithm in which some of ants can evolve by performing genetic operation, and the balance between intensification and diversification can be adjusted by numbers of ants which perform genetic operation. The proposed algorithm is tested by simulating the Traveling Salesman Problem (TSP). Experimental studies show that the proposed ACO algorithm with genetic operation has superior performance when compared to other existing ACO algorithms.
In this study the very large scale integration (VLSI) floorplanning problem with clustering constraints and the layout area as the minimization criterion is considered. An algorithm, which is based on the primary prin...
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In this study the very large scale integration (VLSI) floorplanning problem with clustering constraints and the layout area as the minimization criterion is considered. An algorithm, which is based on the primary principles of ant colony optimization (ACO), to solve this problem is presented. This ACO-based algorithm employs two different types of pheromone trails as the communication media among artificial ants to effectively guide them to cooperatively construct a high quality floorplan. On the basis of the characteristics of ACO, moreover, an encoding scheme, which is referred to as dynamic junction list (DJL), is proposed to represent the geometric relationships between circuit modules for a floorplan. Experimental results using the Microelectronics Center of North Carolina (MCNC) benchmarks demonstrate the effectiveness of the proposed algorithm.
The greedy approach is widely used for combinatorial optimization problems, but its implementation varies from problem to problem. In this paper we propose a mechanical approach for implementing greedy algorithmic pro...
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ISBN:
(纸本)9783642022692
The greedy approach is widely used for combinatorial optimization problems, but its implementation varies from problem to problem. In this paper we propose a mechanical approach for implementing greedy algorithmic programs. Using PAR, method, a problem can be continually partitioned into subproblems in smaller size based on the problem singleton and the maximum selector, and the greedy algorithm can be mechanically generated by combining the problem-solving sequences. Our structural model supports logical transformation from specifications to algorithmic programs by deductive inference;and thus significantly promotes the automation and reusability of algorithm design.
The Transient Chaotic Neural Network (TCNN) and the Noisy Chaotic Neural Network(NCNN) have been proved their searching abilities for solving combinatorial optimization problems(COPs). The chaotic dynamics of the TCNN...
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ISBN:
(纸本)9781424427932
The Transient Chaotic Neural Network (TCNN) and the Noisy Chaotic Neural Network(NCNN) have been proved their searching abilities for solving combinatorial optimization problems(COPs). The chaotic dynamics of the TCNN and the NCNN are believed to be important for their searching abilities. However, in this paper, we propose a strategy which cuts off the rich dynamics such as periodic and chaotic attractors in the TCNN and just utilizes the nonchaotic converge dynamics of the TCNN to save the time needed for computation. The strategy is named as nonchaotic simulated annealing (NCSA). Experiments on the traveling salesman problems exibit the effectiveness of NCSA. The NCSA saves over half of the time needed for the computation while maintaining the searching ability of the TCNN.
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