19
个编辑
更改
无编辑摘要
上面每个函数中的参数<math>\lambda</math>用来调整函数各自的形状。
上面每个函数中的参数<math>\lambda</math>用来调整函数各自的形状。
Step 2 Construction process:<br />The ant's movement is based on 4-connected pixels or 8-connected pixels. The probability with which the ant moves is given by the probability equation <math>P_{x,y}</math><br />Step 3 and Step 5 Update process:<br />The pheromone matrix is updated twice. in step 3 the trail of the ant (given by <math>\tau_{(x,y)}</math> ) is updated where as in step 5 the evaporation rate of the trail is updated which is given by the below equation.<br /><math>
Step 2 Construction process:<br />The ant's movement is based on 4-connected pixels or 8-connected pixels. The probability with which the ant moves is given by the probability equation <math>P_{x,y}</math><br/>Step 3 and Step 5 Update process:<br />The pheromone matrix is updated twice. in step 3 the trail of the ant (given by <math>\tau_{(x,y)}</math> ) is updated where as in step 5 the evaporation rate of the trail is updated which is given by the below equation.<br /><math>
步骤2构建过程: <br/>
蚂蚁的移动是在4个连接的像素或8个连接的像素进行的。根据概率方程<math>P_{x,y}</math>给出蚂蚁移动的概率<br/>
<br /><math>
步骤3与步骤5更新过程<br/>信息素矩阵更新两次。在步骤3中，蚂蚁(由<math>\tau_{(x,y)}</math>给出)的踪迹被更新，就像在步骤5中，蚂蚁的踪迹蒸发率由下面的方程进行更新。
<br/><math>
\tau_{new} \leftarrow
\tau_{new} \leftarrow
(1-\psi)\tau_{old} + \psi \tau_{0}
(1-\psi)\tau_{old} + \psi \tau_{0}
Image Edge detected using ACO:<br />The images below are generated using different functions given by the equation (1) to (4).<ref>{{cite web|title=File Exchange {{ndash}} Ant Colony Optimization (ACO)|website=[[MATLAB]] Central|url=http://www.mathworks.com/matlabcentral/fileexchange/32009-ant-colony-optimization--aco-}}</ref>
Image Edge detected using ACO:<br />The images below are generated using different functions given by the equation (1) to (4).<ref>{{cite web|title=File Exchange {{ndash}} Ant Colony Optimization (ACO)|website=[[MATLAB]] Central|url=http://www.mathworks.com/matlabcentral/fileexchange/32009-ant-colony-optimization--aco-}}</ref>
使用 ACO 检测图像边缘: < br/> 下面的图像是使用方程(1)至(4)给出的不同函数生成的。<ref>{{cite web|title=File Exchange {{ndash}} Ant Colony Optimization (ACO)|website=[[MATLAB]] Central|url=http://www.mathworks.com/matlabcentral/fileexchange/32009-ant-colony-optimization--aco-}}</ref>
使用 ACO 检测图像边缘: < br/> 下面的图像是使用方程(1)至(4)给出的不同函数生成的。<ref>{{cite web|title=File Exchange {{ndash}} Ant Colony Optimization (ACO)|website=[[MATLAB]] Central|url=http://www.mathworks.com/matlabcentral/fileexchange/32009-ant-colony-optimization--aco-}}</ref>
[[File:(a)Original Image (b)Image Generated using equation(1) (c)Image generated using equation(2) (d) Image generated using equation(3) (e)Image generated using equation(4).jpg|none|thumb]]
[[File:(a)Original Image (b)Image Generated using equation(1) (c)Image generated using equation(2) (d) Image generated using equation(3) (e)Image generated using equation(4).jpg|none|thumb]]
* An [[estimation of distribution algorithm]] (EDA) is an [[evolutionary algorithm]] that substitutes traditional reproduction operators by model-guided operators. Such models are learned from the population by employing machine learning techniques and represented as probabilistic graphical models, from which new solutions can be sampled<ref>{{cite book|last1=Pelikan|first1=Martin|last2=Goldberg|first2=David E.|last3=Cantú-Paz|first3=Erick|title=BOA: The Bayesian Optimization Algorithm|journal=Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation - Volume 1|date=1 January 1999|pages=525–532|url=http://dl.acm.org/citation.cfm?id=2933973|isbn=9781558606111|series=Gecco'99}}</ref><ref>{{cite book|last1=Pelikan|first1=Martin|title=Hierarchical Bayesian optimization algorithm : toward a new generation of evolutionary algorithms|date=2005|publisher=Springer|location=Berlin [u.a.]|isbn=978-3-540-23774-7|edition=1st}}</ref> or generated from guided-crossover.<ref>{{cite book|last1=Thierens|first1=Dirk|s2cid=28648829|chapter=The Linkage Tree Genetic Algorithm|journal=Parallel Problem Solving from Nature, PPSN XI|date=11 September 2010|pages=264–273|doi=10.1007/978-3-642-15844-5_27|language=en|isbn=978-3-642-15843-8}}</ref><ref>{{cite journal|last1=Martins|first1=Jean P.|last2=Fonseca|first2=Carlos M.|last3=Delbem|first3=Alexandre C. B.|title=On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem|journal=Neurocomputing|date=25 December 2014|volume=146|pages=17–29|doi=10.1016/j.neucom.2014.04.069}}</ref>
* An [[estimation of distribution algorithm]] (EDA) is an [[evolutionary algorithm]] that substitutes traditional reproduction operators by model-guided operators. Such models are learned from the population by employing machine learning techniques and represented as probabilistic graphical models, from which new solutions can be sampled<ref>{{cite book|last1=Pelikan|first1=Martin|last2=Goldberg|first2=David E.|last3=Cantú-Paz|first3=Erick|title=BOA: The Bayesian Optimization Algorithm|journal=Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation - Volume 1|date=1 January 1999|pages=525–532|url=http://dl.acm.org/citation.cfm?id=2933973|isbn=9781558606111|series=Gecco'99}}</ref><ref>{{cite book|last1=Pelikan|first1=Martin|title=Hierarchical Bayesian optimization algorithm : toward a new generation of evolutionary algorithms|date=2005|publisher=Springer|location=Berlin [u.a.]|isbn=978-3-540-23774-7|edition=1st}}</ref> or generated from guided-crossover.<ref>{{cite book|last1=Thierens|first1=Dirk|s2cid=28648829|chapter=The Linkage Tree Genetic Algorithm|journal=Parallel Problem Solving from Nature, PPSN XI|date=11 September 2010|pages=264–273|doi=10.1007/978-3-642-15844-5_27|language=en|isbn=978-3-642-15843-8}}</ref><ref>{{cite journal|last1=Martins|first1=Jean P.|last2=Fonseca|first2=Carlos M.|last3=Delbem|first3=Alexandre C. B.|title=On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem|journal=Neurocomputing|date=25 December 2014|volume=146|pages=17–29|doi=10.1016/j.neucom.2014.04.069}}</ref>
分布估计算法（EDA）是一种进化算法，它用模型引导的算子代替传统的复制算子。这类模型通过机器学习技术从群体中学习，并以概率图形模型表示，从中可以通过抽样<ref>{{cite book|last1=Pelikan|first1=Martin|last2=Goldberg|first2=David E.|last3=Cantú-Paz|first3=Erick|title=BOA: The Bayesian Optimization Algorithm|journal=Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation - Volume 1|date=1 January 1999|pages=525–532|url=http://dl.acm.org/citation.cfm?id=2933973|isbn=9781558606111|series=Gecco'99}}</ref><ref>{{cite book|last1=Pelikan|first1=Martin|title=Hierarchical Bayesian optimization algorithm : toward a new generation of evolutionary algorithms|date=2005|publisher=Springer|location=Berlin [u.a.]|isbn=978-3-540-23774-7|edition=1st}}</ref>或引导交叉<ref>{{cite book|last1=Thierens|first1=Dirk|s2cid=28648829|chapter=The Linkage Tree Genetic Algorithm|journal=Parallel Problem Solving from Nature, PPSN XI|date=11 September 2010|pages=264–273|doi=10.1007/978-3-642-15844-5_27|language=en|isbn=978-3-642-15843-8}}</ref><ref>{{cite journal|last1=Martins|first1=Jean P.|last2=Fonseca|first2=Carlos M.|last3=Delbem|first3=Alexandre C. B.|title=On the performance of linkage-tree genetic algorithms for the multidimensional knapsack problem|journal=Neurocomputing|date=25 December 2014|volume=146|pages=17–29|doi=10.1016/j.neucom.2014.04.069}}</ref>生成新的解。
*[[Intelligent Water Drops|Intelligent water drops]] (IWD), a swarm-based optimization algorithm based on natural water drops flowing in rivers
*[[Intelligent Water Drops|Intelligent water drops]] (IWD), a swarm-based optimization algorithm based on natural water drops flowing in rivers
智能水滴（IWD），一种基于自然水滴在河流中流动的群优化算法
智能水滴（IWD），一种基于自然水滴在河流中流动的群优化算法
* [[Stochastic diffusion search]] (SDS), an agent-based probabilistic global search and optimization technique best suited to problems where the objective function can be decomposed into multiple independent partial-functions
* [[Stochastic diffusion search]] (SDS), an agent-based probabilistic global search and optimization technique best suited to problems where the objective function can be decomposed into multiple independent partial-functions