In this paper, we prove some convergence properties for a class of ant colony optimization algorithms. In particular, we prove that for any small constant epsilon greater than or equal to 0 and for a sufficiently larg...
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In this paper, we prove some convergence properties for a class of ant colony optimization algorithms. In particular, we prove that for any small constant epsilon greater than or equal to 0 and for a sufficiently large number of algorithm iterations t, the probability of finding an optimal solution at least once is P* (t) greater than or equal to 1 - epsilon and that this probability tends to 1 for t--> infinity. We also prove that, after an optimal solution has been found, it takes a finite number of iterations for the pheromone trails associated to the found optimal solution to grow higher than any other pheromone trail and that, for t -->infinity, any fixed ant will produce the optimal solution during the tth iteration with probability P greater than or equal to1 - is an element of((T)min, (T)max), where (T)min and(T)max are the minimum and maximum values that can be taken by pheromone trails..
Most local optimization algorithms are hard to search the global minimum. In this paper, we implemented and tested an AVO inversion scheme based on aco algorithms. The ant colony optimization (aco) algorithms are insp...
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Most local optimization algorithms are hard to search the global minimum. In this paper, we implemented and tested an AVO inversion scheme based on aco algorithms. The ant colony optimization (aco) algorithms are inspired by the behavior of ants to find solutions to combinatorial optimization problem. Inversion results of synthetic data and real model demonstrate that aco algorithms applied in nonlinear AVO inversion should be considered well not only in terms of accuracy but also in terms of computation effort. Meanwhile it can provide a new approach to solve the nonlinear problems of network traffic allocation optimization. (C) 2010 Published by Elsevier Ltd.
The dynamics of Ant Colony Optimization (aco) algorithms is studied using a deterministic model that assumes an average expected behavior of the algorithms. The aco optimization metaheuristic is an iterative approach,...
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The dynamics of Ant Colony Optimization (aco) algorithms is studied using a deterministic model that assumes an average expected behavior of the algorithms. The aco optimization metaheuristic is an iterative approach, where in every iteration, artificial ants construct solutions randomly but guided by pheromone information stemming from former ants that found good solutions. The behavior of aco algorithms and the aco model are analyzed for certain types of permutation problems. It is shown analytically that the decisions of an ant are influenced in an intriguing way by the use of the pheromone information and the properties of the pheromone matrix. This explains why aco algorithms can show a complex dynamic behavior even when there is only one ant per iteration and no competition occurs. The aco model is used to describe the algorithm behavior as a combination of situations with different degrees of competition between the ants. This helps to better understand the dynamics of the algorithm when there are several ants per iteration as is always the case when using aco algorithms for optimization. Simulations are done to compare the behavior of the aco model with the aco algorithm. Results show that the deterministic model describes essential features of the dynamics of aco algorithms quite accurately, while other aspects of the algorithms behavior cannot be found in the model.
This paper proves the convergence for the Ant Colony Optimization algorithms (aco). Starting from a simplified Ant System, the process of this special Ant System is described mathematically as a Markov chain and its a...
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ISBN:
(纸本)1932415688
This paper proves the convergence for the Ant Colony Optimization algorithms (aco). Starting from a simplified Ant System, the process of this special Ant System is described mathematically as a Markov chain and its algorithm is proved to have a probability 1 to reach a global optimal solution. Furthermore, it is showed that the convergence holds for the aco algorithms with some kind of pheromone limitation and elite strategy.
Ant Colony Optimization has proven to be an important optimization technique. It has provided a solid base for solving classical computational problems, networks routing problems and many others. Nonetheless, algorith...
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ISBN:
(纸本)9781479986965
Ant Colony Optimization has proven to be an important optimization technique. It has provided a solid base for solving classical computational problems, networks routing problems and many others. Nonetheless, algorithms within the Ant Colony metaheuristic have been shown to struggle to reach the global optimum of the search space, with only a few select ones guaranteed to reach it at all. On the other hand, Ant Colony-based hybrid solutions that address this issue suffer from either severely decreased efficiency or low scalability and are usually static and custom-made, with only one particular use. In this paper we present a generic and robust solution to this problem, restricted rigorously to the Ant Colony Optimization paradigm, named Angry Ant Framework. It adds a new dimension - a dynamic, biologically-inspired pheromone stratification, which we hope can become the objective of further state-of-the-art research. We present a series of experiments to enable a discussion on the benefits provided by this new framework. In particular, we show that Angry Ant Framework increases the efficiency, while at the same time improving the flexibility, the adaptability and the scalability with a very low computational investment.
EDF (Earliest Deadline First) has been proved to be optimal scheduling algorithm for single processor real-time operating systems when the systems are preemptive and underloaded. The limitation of this algorithm is, i...
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ISBN:
(纸本)9781424418220
EDF (Earliest Deadline First) has been proved to be optimal scheduling algorithm for single processor real-time operating systems when the systems are preemptive and underloaded. The limitation of this algorithm is, its performance decreases exponentially when system becomes slightly overloaded. Authors have already proved ability of aco (Ant Colony Optimization) based scheduling algorithm for real-time operating system which is optimal during underloaded condition and it gives outstanding results in overloaded condition. The limitation of this algorithm is, it takes more time for execution compared to EDF algorithm. In this paper, an adaptive scheduling algorithm is proposed which is combination of both of these algorithms. Basically the new algorithm uses EDF algorithm but when the system becomes overloaded, it will switch to aco based scheduling algorithm. Again, when the overload disappears, the system will switch to EDF algorithm. Therefore, the proposed algorithm takes the advantages of both algorithms and overcomes the limitations of each other. The proposed algorithm along with EDF algorithm and aco based scheduling algorithm, is simulated for real-time system and the results are obtained. The performance is measured in terms of Success Ratio and Effective CPU Utilization. Execution Time taken by each scheduling algorithm is also measured. From analysis and experiments it reveals that the proposed algorithm is fast as well as very efficient in both underloaded and overloaded conditions.
This paper describes a novel bio-inspired metaheuristic named ASClass for data clustering problem. The particular principles used for the design of this strategy are inspired by the foraging behavior observed in ant c...
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ISBN:
(纸本)9781467387095
This paper describes a novel bio-inspired metaheuristic named ASClass for data clustering problem. The particular principles used for the design of this strategy are inspired by the foraging behavior observed in ant colony. In this technique, an ant colony optimization algorithm is used to search a closed tour of minimal length connecting n objects in database. The process of building path-object is inspired by the collective weaving observed in social spiders.
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