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A Novel Framework Based on ACO and PSO for RNA Secondary Structure Prediction

MATHEMATICAL PROBLEMS IN ENGINEERING 2013年第1期2013卷 1-8页

作者： Wang, Gang Zhang, Wen-yi Ning, Qiao Chen, Hui-ling Jilin 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 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.

关键词： PARTICLE swarm optimization ant algorithms RNA GENETIC disorders SEQUENCES (Mathematics) PERFORMANCE evaluation

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Cooperative Task Allocation Method of MCAV/UCAV Formation

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MATHEMATICAL PROBLEMS IN ENGINEERING 2016年第1期2016卷 1-9页

作者： Zhong, Yun Yao, Peiyang Sun, Yu Yang, Juan Air Force Engn Univ Informat & Nav Coll Xian 710077 Peoples R China Chinese Peoples Liberat Army Unit 93010 Shenyang 110015 Peoples R China

Unmanned Combat Aerial Vehicle (UCAV) cooperative task allocation under Manned Combat Aerial Vehicle's (MCAV) limited control is one of the important problems in UCAV research field. Hereto, we analyze the key technical and tactical indices influence task allocation problem and build an appropriate model to maximize the objective function values as well as reflecting various constraints. A novel improved multigroup ant colony algorithm (IMGACA) is proposed to solve the model;the algorithm mainly includes random sequence-based UCAV selection strategy, constraint-based candidate task generation strategy, objective function value-based state transition strategy, and crossover operator-based local search strategy. Simulation results show that the built-model is reasonable and the proposed algorithm performs well in feasibility, timeliness, and stability.

关键词： DRONE aircraft COMPUTER simulation ant algorithms PROBLEM solving STABILITY theory

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Research and Application on a Novel Clustering Algorithm of Quantum Optimization in Server Load Balancing

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-7页

作者： Dong Yumin Xiao Shufen Jia Fanghua Li Jinhai Qingdao Technol Univ Network Ctr Qingdao Peoples R China Qingdao Technol Univ Adm Off Natl Assets Qingdao Peoples R China Qingdao Technol Univ Qingdao Peoples R China

A quantum optimization scheme in network cluster server task scheduling is proposed. We explore and research the distribution theory of energy field in quantum mechanics;specially, we apply it to data clustering. We compare the quantum optimization method with genetic algorithm (GA), ant colony optimization (ACO), simulated annealing algorithm (SAA). At the same time, we prove its validity and rationality by analog simulation and experiment.

关键词： CLIENT/SERVER computing LOAD balancing (Computer networks) CLUSTER analysis (Statistics) QUantUM theory PRODUCTION scheduling SIMULATED annealing (Mathematics) ant algorithms

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Artificial Bee Colony Algorithm Merged with Pheromone Communication Mechanism for the 0-1 Multidimensional Knapsack Problem

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MATHEMATICAL PROBLEMS IN ENGINEERING 2013年第1期2013卷 1-13页

作者： Ji, Junzhong Wei, Hongkai Liu, Chunnian Yin, Baocai Beijing Univ Technol Coll Comp Sci & Technol Beijing Municipal Key Lab Multimedia & Intelligen Beijing 100124 Peoples R China

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.

关键词： ant algorithms PHEROMONES KNAPSACK problem (Mathematics) PROFIT maximization STOCHASTIC processes CONSTRAINT satisfaction (Artificial intelligence)

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A general model for meta-heuristic algorithms using the concept of fields of forces

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ACTA MECHANICA 2011年第1-2期221卷 99-118页

作者： Kaveh, A. Talatahari, S. Iran Univ Sci & Technol Ctr Excellence Fundamental Studies Struct Engn Tehran 16 Iran Univ Tabriz Dept Civil Engn Tabriz Iran

A general model is presented to unify the explanation of different meta-heuristic algorithms. This model is based on the concept of fields of forces from physics and covers many meta-heuristic algorithms consisting of Genetic algorithms, ant Colony Optimization, Particle Swarm Optimization, Big Bang-Big Crunch algorithm and Harmony Search. The properties of these algorithms can be explained using the presented general model that is called the fields of forces (FOF) model. This extension provides efficient means to improve, expand, modify and hybridize the meta-heuristic algorithms. An improved and hybridized algorithm is then developed using the FOF model.

关键词： HEURISTIC algorithms FIELD theory (Physics) FORCE & energy MATHEMATICAL models PHYSICS PARTICLE swarm optimization ant algorithms

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SINGLE FIXED CRANE OPTIMISATION WITHIN A DISTRIBUTION CENTRE

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SOUTH AFRICAN JOURNAL OF INDUSTRIAL ENGINEERING 2011年第2期22卷 107-120页

作者： Matthews, J. Visagie, S. E. Univ Stellenbosch Dept Logist ZA-7600 Stellenbosch South Africa

This paper considersthe optimisation of the movement of a fixed crane operating in a single aisle of a distribution centre. The crane must move pallets in inventory between docking bays, storage locations, and picking lines. Both a static and a dynamic approach to the problem are presented. The optimisation is performed by means of tabu search, ant colony metaheuristics, and hybrids of these two methods. All these solution approaches were tested on real life data obtained from an operational distribution centre. Results indicate that the hybrid methods outperform the other approaches.

关键词： MATHEMATICAL optimization WAREHOUSES ant algorithms HEURISTIC algorithms CRANE manufacturing (Cranes, derricks, etc.) CARGO handling equipment

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An Accurate and Efficient Method to Predict Y-NO Bond Homolysis Bond Dissociation Energies

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MATHEMATICAL PROBLEMS IN ENGINEERING 2013年第1期2013卷 1-10页

作者： Li, Hong Zhi Li, Lin Zhong, Zi Yan Han, Yi Hu, LiHong Lu, YingHua NE Normal Univ Sch Comp Sci & Informat Technol Changchun 130017 Peoples R China NE Normal Univ Sch Life Sci Changchun 130024 Peoples R China NE Normal Univ Fac Chem Inst Funct Mat Chem Changchun 130024 Peoples R China

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.

关键词： ant algorithms PREDICTION (Logic) HOMOLYSIS NITROGEN oxides CHEMICAL bonds DENSITY functional theory

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ACO-Based Routing Algorithm for Cognitive Radio Networks

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MATHEMATICAL PROBLEMS IN ENGINEERING 2013年第1期2013卷 1-8页

作者： Huang, Peng Lin, Feng Zhou, Jiliu Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China Xichang Coll Sch Automot & Elect Engn Xichang 615000 Peoples R China Sichuan Univ Sch Comp Sci Chengdu 610065 Peoples R China

Cognitive Radio Networks (CRNs) are an outstanding solution to improve efficiency of spectrum usage. Secondary users in cognitive networks may select from a set of available channels to use provided that the occupancy does not affect the prioritized licensed users. However, CRNs produce unique routing challenges due to the high fluctuation in the available spectrum as well as diverse quality-of-service (QoS) requirements. In CRNs, distributed multihop architecture and time varying spectrum availability are some of the key factors in design of routing algorithms. In this paper, we develop an ant-colony-optimization-(ACO-) based on-demand cognitive routing algorithm (ACO-OCR), jointly consider path and spectrum scheduling, and take advantage of the availability of multiple channels, to improve the delivery latency and packet loss rate. Then, an analytical framework based on M/G/1 queuing theory is introduced to illustrate the relay node queuing model. The performances of ACO-OCR have been evaluated by means of numerical simulations, and the experimental results confirm its effectiveness. Simulation results show that ACO-OCR outperforms other routing approaches in end-to-end path latency and package loss rate.

关键词： COGNITIVE radio (Wireless communication systems) ROUTING algorithms RADIO networks QUALITY of service DISTRIBUTED algorithms ant algorithms

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Decision optimization analysis on supply chain resource integration in fourth party logistics

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JOURNAL OF MANUFACTURING SYSTEMS 2010年第4期29卷 121-129页

作者： Yao, Jianming Renmin Univ China Sch Business Beijing 100872 Peoples R China

In recent years, the successful operation of the fourth party logistics (4PL) in practice has gradually demonstrated that it is an effective mode to integrate the complicated resources of a supply chain reasonably, efficiently and flexibly. However, there are no effective quantitative methods to guide the resource integration practices of enterprises and this situation will inevitably limit the practical application of 4PL and will become a major bottleneck of showing its superiorities. To solve this operational bottleneck in 4PL, this paper analyzes thoroughly the characteristics of the supply chain resource integration in 4PL mode from a quantitative view, set up an operational framework by case studies of surveyed enterprises combined with the empirical analyses of the supply chain resource integration. On this basis, this paper puts forward a decision optimization method of supply chain resource integration in 4PL based on the discovery, analyses and judgment about the dominant factors in the integration operations, then, sets up a mathematics optimization model for integration decision and an improved ant colony optimization (ACO) algorithm to solve the decision problem. Finally, the paper uses a case simulation to illustrate that the optimization method and algorithm are feasible and valid. (C) 2010 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： Supply chain management Business logistics Decision making Quantitative research Business enterprises Industrial surveys Mathematical optimization ant algorithms

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A Hybrid Algorithm Based on ACO and PSO for Capacitated Vehicle Routing Problems

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MATHEMATICAL PROBLEMS IN ENGINEERING 2012年第1期2012卷 1-17页

作者： Kao, Yucheng Chen, Ming-Hsien Huang, Yi-Ting Tatung Univ Dept Informat Management Taipei 104 Taiwan

The vehicle routing problem (VRP) is a well-known combinatorial optimization problem. It has been studied for several decades because finding effective vehicle routes is an important issue of logistic management. This paper proposes a new hybrid algorithm based on two main swarm intelligence (SI) approaches, ant colony optimization (ACO) and particle swarm optimization (PSO), for solving capacitated vehicle routing problems (CVRPs). In the proposed algorithm, each artificial ant, like a particle in PSO, is allowed to memorize the best solution ever found. After solution construction, only elite ants can update pheromone according to their own best-so-far solutions. Moreover, a pheromone disturbance method is embedded into the ACO framework to overcome the problem of pheromone stagnation. Two sets of benchmark problems were selected to test the performance of the proposed algorithm. The computational results show that the proposed algorithm performs well in comparison with existing swarm intelligence approaches.

关键词： ant algorithms VEHICLE routing problem COMBINATORIAL optimization MATHEMATICAL models of engineering MATHEMATICS

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