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Description: 章对蚁群算法在连续空间优化中的研究现状作一综述!希望能对相关研究起到一定的启发作用-Chapter on ant colony algorithm in continuous space optimization study reviewed the status quo! Want to be able to play a relevant research inspired the role of
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Author: xq |
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Description: General simulated annealing algorithm-anneal Minimizes a function with the method of simulated annealing (Kirkpatrick et al., 1983)
ANNEAL takes three input parameters, in this order:
LOSS is a function handle (anonymous function or inline) with a loss function, which may be of any type, and needn t be continuous. It does, however, need to return a single value.
PARENT is a vector with initial guess parameters. You must input an initial guess.
OPTIONS is a structure with settings for the simulated annealing. If no OPTIONS structure is provided, anneal uses a default structure. OPTIONS can contain any or all of the following fields (missing fields are filled with default values):
Verbosity: Controls output to the screen.
0 suppresses all output
1 gives final report only [default]
2 gives temperature changes and final report
Generator: Generates a new solution from an old one. Any function handle that takes a solution as input and gives a valid solution (i.e.
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Size: 4096 |
Author: Ping-Feng Xu |
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Description: 无约束连续函数优化的人工蚁群算法通用MATLAB源码
此源码是对人工蚁群算法的一种实现，用于无约束连续函数的优化求解，对于含有约束的情况，可以先使用罚函数等方法，把问题处理成无约束的模型，再使用本源码进行求解
-Unconstrained optimization of continuous function of artificial ant colony algorithm for general-purpose MATLAB source code。
This source code is an implementation of artificial ant colony algorithm for unconstrained optimization of continuous function to solve for the case containing the constraints, you can first use the penalty function and other methods to deal with the problem into the unconstrained model, then use the origin codes solving
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Author: 孙准 |
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Description: Extending the
approach for coping with continuous attributes presented by
cAnt-Miner (Ant-Miner coping with continuous attributes), in
this paper we propose two new methods for handling continuous
attributes in ACO classification algorithms. The first method
allows a more flexible representation of continuous attributes’
intervals. The second method explores the problem of attribute
interaction, which originates from the way that continuous
attributes are handled in cAnt-Miner, in order to implement
an improved pheromone updating method. Empirical evaluation
on eight publicly available data sets shows that the proposed
methods facilitate the discovery of more accurate classification
models.
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Author: tanja |
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Description: Myra is a cross-platform Ant Colony Optimization framework written in Java. It provides a specialised data mining layer to support the application of ACO to classification problems, including the implementation of Ant-Miner and cAnt-Miner algorithms. The latter is an extension of Ant-Miner, which is able to cope with continuous attributes directly - i.e. cAnt-Miner does not requires a discretization method in a preprocessing step.
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Author: juksa |
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Description: 其主要内容包括蚁群算法的思想起源、研究现状及机制原理；蚁群算法的复杂度分析；蚁群算法的收敛性证明；蚁群算法参数对其性能的影响；蚁群算法的参数选择原则；离散域和连续域蚁群算法的若干改进策略；蚁群算法在多个优化领域的典型应用；蚁群算法的硬件实现技术；蚁群算法与其它仿生优化算法的比较与融合；蚁群算法的研究展望；最后还在附录部分给出了基本蚁群算法的程序源代码和相关网站。.-Its main contents include the origin of ant colony algorithm idea, principle and mechanism of Research the complexity analysis of ant colony algorithm prove the convergence of ant colony algorithm ant colony algorithm parameters on its performance the principle of ant colony algorithm parameters discrete domain and continuous domain of a number of ant colony algorithm improvement strategies ant colony optimization algorithm is typical in many applications ACO hardware implementation technology ant colony optimization algorithm and other bionic comparison and integration ant colony algorithm research prospects final appendix also shows the basic ant colony algorithm source code and related sites. .
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Author: ma |
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Description: ACO for continuous optimization
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Size: 480256 |
Author: Kim Dung |
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Description: Ant Colony Optimization (ACO) is an optimization metaheuristic based on the foraging behavior of ants. This metaheuristic was originally proposed to find good solutions to discrete combinatorial problems. Many extensions of the ACO heuristic for continuous domain have been proposed, but even those that claim close similarity with classical (discrete domain) ACO, like ACOR, do not use the heuristic information called visibility, commonly used in the original ACO algorithm. In this paper, we show the importance of the visibility in ACO, by proposing ACOℝ -V , a variant of ACOR that performs better in a number of benchmark functions.
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Author: Alireza |
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Description: Continuous Ant Colony Optimization in MATLAB
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Author: amin |
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Description: continuous ACO.
In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.
This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Initially proposed by Marco Dorigo in 1992 in his PhD thesis,[1][2] the first algorithm was aiming to search for an optimal path in a graph, based on the behavior of ants seeking a path between their colony and a source of food. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behavior of ants. From a broader perspective, ACO performs a model-based search [3] and share some similarities with Estimation of Distribution
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Size: 7168 |
Author: ibra |
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