To tackle the QoS-based service selection problem, a hybrid artificial bee colony algorithm called h-ABC is proposed, which incorporates the ant colony optimizationmechanism into the artificial bee colony optimization...
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To tackle the QoS-based service selection problem, a hybrid artificial bee colony algorithm called h-ABC is proposed, which incorporates the ant colony optimizationmechanism into the artificial bee colony optimization process. In this algorithm, a skyline query process is used to filter the candidates related to each service class, which can greatly shrink the search space in case of not losing good candidates, and a flexible self-adaptive varying construct graph is designed to model the search space based on a clustering process. Then, based on this construct graph, different foraging strategies are designed for different groups of bees in the swarm. Finally, this approach is evaluated experimentally using different standard real datasets and synthetically generated datasets and compared with some recently proposed related service selection algorithms. It reveals very encouraging results in terms of the quality of solutions.
ant algorithms are optimisation algorithms inspired by the foraging behaviour of real ants in the wild. Introduced in the early 1990s, ant algorithms aim at finding approximate solutions to optimisation problems throu...
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ant algorithms are optimisation algorithms inspired by the foraging behaviour of real ants in the wild. Introduced in the early 1990s, ant algorithms aim at finding approximate solutions to optimisation problems through the use of artificial ants and their indirect communication via synthetic pheromones. The first ant algorithms and their development into the ant Colony Optimisation (ACO) metaheuristic is described herein. An overview of past and present typical applications as well as more specialised and novel applications is given. The use of ant algorithms alongside more traditional machine learning techniques to produce robust, hybrid, optimisation algorithms is addressed, with a look towards future developments in this area of study. (C) 2009 Elsevier Ltd. All rights reserved.
Subset problems (set partitioning, packing, and covering) are formal models for many practical optimization problems. A set partitioning problem determines how the items in one set (S) can be partitioned into smaller ...
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Subset problems (set partitioning, packing, and covering) are formal models for many practical optimization problems. A set partitioning problem determines how the items in one set (S) can be partitioned into smaller subsets. All items in S must be contained in one and only one partition. Related problems are set packing (all items must be contained in zero or one partitions) and set covering (all items must be contained in at least one partition). Here, we present a hybrid solver based on ant colony optimization (ACO) combined with arc consistency for solving this kind of problems. ACO is a swarm intelligence metaheuristic inspired on ants behavior when they search for food. It allows to solve complex combinatorial problems for which traditional mathematical techniques may fail. By other side, in constraint programming, the solving process of Constraint Satisfaction Problems can dramatically reduce the search space by means of arc consistency enforcing constraint consistencies either prior to or during search. Our hybrid approach was tested with set covering and set partitioning dataset benchmarks. It was observed that the performance of ACO had been improved embedding this filtering technique in its constructive phase.
Emergency materials dispatch (EMD) is a typical dynamic vehicle routing problem (DVRP) and it concentrates on process strategy solving, which is different from the traditional static vehicle routing problem. Based on ...
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Emergency materials dispatch (EMD) is a typical dynamic vehicle routing problem (DVRP) and it concentrates on process strategy solving, which is different from the traditional static vehicle routing problem. Based on the characteristics of emergency materials dispatch, DVRP changed the EMD into a series of static problems in time axis. A mathematical multiobjective model is established, and the corresponding improved ant colony optimization algorithm is designed to solve the problem. Finally, a numeric example is provided to demonstrate the validity and feasibility of this proposed model and algorithm.
The article briefly discusses Photogrowth, a software system designed to create images based on an algorithm inspired by the behavior of a virtual ant colony.
The article briefly discusses Photogrowth, a software system designed to create images based on an algorithm inspired by the behavior of a virtual ant colony.
A mechanism for deploying a minimum number of sensors to cover all targets that are randomly placed in a grid environment is discussed. The sensors are deployed at grid points that do not have targets. In the presente...
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A mechanism for deploying a minimum number of sensors to cover all targets that are randomly placed in a grid environment is discussed. The sensors are deployed at grid points that do not have targets. In the presented method, condideration is given to a grid point, called the sink, to start deploying the sensors. From the sink location, movement is in one of four possible directions (up, down, left, right) to find the next grid point for placing the sensor depending on the maximum number of targets covered by the new sensor. In case there is no path to move due to target placement, a random grid point is chosen as the next location. This is done till all targets are covered. This process is carried out for each possible sink location and the sink location corresponding to the minimum number of sensors is noted.
作者：
Wang, GangZhang, Wen-yiNing, QiaoChen, Hui-lingJilin Univ
Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ
Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China Jilin Univ
Coll GeoExplorat Sci & Technol Changchun 130026 Peoples R China NE Normal Univ
Sch Comp Sci & Informat Technol Changchun 130024 Peoples R China Wenzhou Univ
Coll Phys & Elect Informat Chashan Univ Town Wenzhou 325035 Zhejiang Peoples R China
Prediction of RNA structure is a useful process for creating new drugs and understanding genetic diseases. In this paper, we proposed a particle swarm optimization (PSO) and ant colony optimization (ACO) based framewo...
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Prediction of RNA structure is a useful process for creating new drugs and understanding genetic diseases. In this paper, we proposed a particle swarm optimization (PSO) and ant colony optimization (ACO) based framework (PAF) for RNA secondary structure prediction. PAF consists of crucial stem searching (CSS) and global sequence building (GSB). In CSS, a modified ACO (MACO) is used to search the crucial stems, and then a set of stems are generated. In GSB, we used a modified PSO (MPSO) to construct all the stems in one sequence. We evaluated the performance of PAF on ten sequences, which have length from 122 to 1494. We also compared the performance of PAF with the results obtained from six existing well-known methods, SARNA-Predict, RnaPredict, ACRNA, PSOfold, IPSO, and mfold. The comparison results show that PAF could not only predict structures with higher accuracy rate but also find crucial stems.
Given a set of n objects, the objective of the 0-1 multidimensional knapsack problem (MKP_01) is to find a subset of the object set that maximizes the total profit of the objects in the subset while satisfying m knaps...
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Given a set of n objects, the objective of the 0-1 multidimensional knapsack problem (MKP_01) is to find a subset of the object set that maximizes the total profit of the objects in the subset while satisfying m knapsack constraints. In this paper, we have proposed a new artificial bee colony (ABC) algorithm for the MKP_01. The new ABC algorithm introduces a novel communication mechanism among bees, which bases on the updating and diffusion of inductive pheromone produced by bees. In a number of experiments and comparisons, our approach obtains better quality solutions in shorter time than the ABC algorithm without the mechanism. We have also compared the solution performance of our approach against some stochastic approaches recently reported in the literature. Computational results demonstrate the superiority of the new ABC approach over all the other approaches.
The paper suggests a new method that combines the Kennard and Stone algorithm(Kenstone, KS), hierarchical clustering (HC), and ant colony optimization (ACO)-based extreme learning machine (ELM) (KS-HC/ACO-ELM) with th...
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The paper suggests a new method that combines the Kennard and Stone algorithm(Kenstone, KS), hierarchical clustering (HC), and ant colony optimization (ACO)-based extreme learning machine (ELM) (KS-HC/ACO-ELM) with the density functional theory (DFT) B3LYP/6-31G(d) method to improve the accuracy of DFT calculations for the Y-NO homolysis bond dissociation energies (BDE). In this method, Kenstone divides the whole data set into two parts, the training set and the test set;HC and ACO are used to perform the cluster analysis on molecular descriptors;correlation analysis is applied for selecting the most correlated molecular descriptors in the classes, and ELM is the nonlinear model for establishing the relationship between DFT calculations and homolysis BDE experimental values. The results show that the standard deviation of homolysis BDE in the molecular test set is reduced from 4.03 kcal mol(-1) calculated by the DFT B3LYP/6-31G(d) method to 0.30, 0.28, 0.29, and 0.32 kcal mol(-1) by the KS-ELM, KS-HC-ELM, and KS-ACO-ELM methods and the artificial neural network (ANN) combined with KS-HC, respectively. This method predicts accurate values with much higher efficiency when compared to the larger basis set DFT calculation and may also achieve similarly accurate calculation results for larger molecules.
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