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A Modified Artificial Bee Colony Algorithm Based on search Space Division and Disruptive Selection Strategy

MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-14页

作者： He, Zhen-an Ma, Caiwen Wang, Xianhong Li, Lei Wang, Ying Zhao, Yuan Guo, Huinan Chinese Acad Sci Xian Inst Opt & Precis Mech Xian 710019 Shaanxi Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Peoples Liberat Army Unit 96165 Shangrao 334000 Jiangxi Peoples R China North Optoelect Stock Ltd Xian 710032 Shaanxi Peoples R China Chinese Armed Police Force Engn Univ Xian 710086 Shaanxi Peoples R China

Artificial bee colony (ABC) algorithm has attracted much attention and has been applied to many scientific and engineering applications in recent years. However, there are still some insufficiencies in ABC algorithm such as poor quality of initial solution, slow convergence, premature, and low precision, which hamper the further development and application of ABC. In order to further improve the performance of ABC, we first proposed a novel initialization method called search space division (SSD), which provided high quality of initial solutions. And then, a disruptive selection strategy was used to improve population diversity. Moreover, in order to accelerate convergence rate, we changed the definition of the scout bee phase. In addition, we designed two types of experiments to testify our proposed algorithm. On the one hand, we conducted experiments to make sure how much each modification makes contribution to improving the performance of ABC. On the other hand, comprehensive experiments were performed to prove the superiority of our proposed algorithm. The experimental results indicate that SDABC significantly outperforms other ABCs, contributing to higher solution accuracy, faster convergence speed, and stronger algorithm stability.

关键词： BEES algorithm search algorithms SPACE division multiple access STABILITY theory PERFORMANCE evaluation

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A Discrete Group search Optimizer for Hybrid Flowshop Scheduling Problem with Random Breakdown

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-11页

作者： Cui, Zhe Gu, Xingsheng Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

The scheduling problems have been discussed in the literature extensively under the assumption that the machines are permanently available without any breakdown. However, in the real manufacturing environments, the machines could be unavailable inevitably for many reasons. In this paper, the authors introduce the hybrid flowshop scheduling problem with random breakdown (RBHFS) together with a discrete group search optimizer algorithm (DGSO). In particular, two different working cases, preempt-resume case, and preempt-repeat case are considered under random breakdown. The proposed DGSO algorithm adopts the vector representation and several discrete operators, such as insert, swap, differential evolution, destruction, and construction in the producers, scroungers, and rangers phases. In addition, an orthogonal test is applied to configure the adjustable parameters in the DGSO algorithm. The computational results in both cases indicate that the proposed algorithm significantly improves the performances compared with other high performing algorithms in the literature.

关键词： DISCRETE groups PRODUCTION scheduling search algorithms PARAMETER estimation DIFFERENTIAL evolution (Computer science)

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Quantum search Algorithm Based on Multi-Phase

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Chinese Physics Letters 2014年第5期31卷 1-4页

作者：李坦鲍皖苏林文铅张侯付向群 The PLA Information Engineering University Zhengzhou 450001 Synergetic Innovation Center of Quantum Information and Quantum Physics University of Science and Technology of China Hefei 230026

The success probability of the Grover quantum search algorithm decreases quickly when the fraction of target items exceeds 1/4, where the phase plays a significant role. Therefore, we use multiple phases to complement each other. We obtain three useful properties and an important theorem of the success probability and design a systematic solution of the optimal phases for an arbitrary number of phases. Based on these results, we finally propose a multi-phase quantum search algorithm whose success probability rises with the increase of the number of phases with just a single iteration, and it tends to be 100% when the fraction of target items is over a lower limit.

关键词： search algorithms PHASE rule & equilibrium HADAMARD matrices MATHEMATICAL analysis PROBABILITY theory

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A Multimodel Based Range Query Processing Algorithm for Information Collection in CPS

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2015年第8期11卷 1-13页

作者： Li, Guilin Gao, Xing Guo, Longjiang Lin, JunCong Gao, Ying Liao, Minghong Xiamen Univ Dept Software Xiamen 361005 Peoples R China Heilongjiang Univ Sch Comp Sci & Technol Harbin 150080 Peoples R China

A multimodel based range query processing algorithm is proposed to solve the information collection task for the CPSs, which utilizes multiple probability models to depict the data distribution of a sensor node. The execution of the multimodel based algorithm consists of two phases, which are the preprocessing phase and the query processing phase. During the preprocessing phase, multiple models are constructed for each node according to their historical data. During the query processing phase, a suitable model is selected from the multiple models with the help of a sampling based algorithm, which is used to process the query. As the multimodel based algorithm needs to sample data from the network, it can waste energy more than that of the single model based algorithm in some cases, which does not sample data from the network. The cost of the multimodel based and single model based algorithm is analyzed. A cost model based algorithm is proposed to select a better algorithm to process a query from the two algorithms. Experimental results show that the cost model based algorithm can save 13.3% energy consumption more than that of the single model based algorithm.

关键词： search algorithms COMPUTER algorithms CYBER-physical systems ACQUISITION of data WIRELESS sensor nodes

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A Privacy-Preserving Continuous Location Monitoring System for Location-Based Services

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2015年第8期11卷 1-10页

作者： Song, Doohee Sim, Jongwon Park, Kwangjin Song, Moonbae Wonkwang Univ Informat Commun Engn Iksan Shi 570749 South Korea Samsung Elect Suwon South Korea

To protect users' private locations in location-based services, various location anonymization techniques have been proposed. The most commonly used technique is spatial cloaking, which organizes users' exact locations into cloaked regions (CRs). This satisfies the K-anonymity requirement;that is, the querier is not distinguishable among K users within the CR. However, the practicality of cloaking techniques is limited due to the lack of privacy-preserving query processing capacity, for example, providing answers to the user's spatial queries based on knowledge of the user's cloaked location rather than the exact location. This paper proposes a cloaking system model called anonymity of motion vectors (AMV) that provides anonymity for spatial queries. The proposed AMV minimizes the CR of a mobile user using motion vectors. In addition, the AMV creates a ranged search area that includes the nearest neighbor (NN) objects to the querier who issued a CR-based query. The effectiveness of the proposed AMV is demonstrated in simulated experiments.

关键词： COMPUTER security CONTINUOUS functions LOCATION-based services COMPUTER users search algorithms QUERY (Information retrieval system)

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CSA: A Credibility search Algorithm Based on Different Query in Unstructured Peer-to-Peer Networks

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-20页

作者： Mei, Hongyan Zhang, Yujie Meng, Xiangwu Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

Efficient searching for resources has become a challenging task with less network bandwidth consumption in unstructured peer-to-peer (P2P) networks. Heuristic search mechanism is an effective method which depends on the previous searches to guide future ones. In the proposed methods, searching for high-repetition resources is more effective. However, the performances of the searches for nonrepetition or low-repetition or rare resources need to be improved. As for this problem, considering the similarity between social networks and unstructured P2P networks, we present a credibility search algorithm based on different queries according to the trust production principle in sociology and psychology. In this method, queries are divided into familiar queries and unfamiliar queries. For different queries, we adopt different ways to get the credibility of node to its each neighbor. And then queries should be forwarded by the neighbor nodes with higher credibility. Experimental results show that our method can improve query hit rate and reduce search delay with low bandwidth consumption in three different network topologies under static and dynamic network environments.

关键词： PEER-to-peer architecture (Computer networks) HEURISTIC algorithms search algorithms BANDWIDTHS COMPUTER networks ELECTRIC network topology

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Gradient-Based Cuckoo search for Global Optimization

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-12页

作者： Fateen, Seif-Eddeen K. Bonilla-Petriciolet, Adrian Cairo Univ Dept Chem Engn Giza 12316 Egypt Aguascalientes Inst Technol Dept Chem Engn Aguascalientes 20256 Ags Mexico

One of the major advantages of stochastic global optimization methods is the lack of the need of the gradient of the objective function. However, in some cases, this gradient is readily available and can be used to improve the numerical performance of stochastic optimization methods specially the quality and precision of global optimal solution. In this study, we proposed a gradient-based modification to the cuckoo search algorithm, which is a nature-inspired swarm-based stochastic global optimization method. We introduced the gradient-based cuckoo search (GBCS) and evaluated its performance vis-a-vis the original algorithm in solving twenty-four benchmark functions. The use of GBCS improved reliability and effectiveness of the algorithm in all but four of the tested benchmark problems. GBCS proved to be a strong candidate for solving difficult optimization problems, for which the gradient of the objective function is readily available.

关键词： MATHEMATICAL optimization search algorithms OPTIMAL control theory STOCHASTIC analysis algorithms

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A New Approach to Reducing search Space and Increasing Efficiency in Simulation Optimization Problems via the Fuzzy-DEA-BCC

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-15页

作者： Miranda, Rafael de Carvalho Barra Montevechi, Jose Arnaldo da Silva, Aneirson Francisco Silva Marins, Fernando Augusto Fed Univ Itajuba UNIFEI BR-37500903 Itajuba MG Brazil Sao Paulo State Univ UNESP BR-12516410 Guaratingueta SP Brazil

The development of discrete-event simulation software was one of the most successful interfaces in operational research with computation. As a result, research has been focused on the development of new methods and algorithms with the purpose of increasing simulation optimization efficiency and reliability. This study aims to define optimum variation intervals for each decision variable through a proposed approach which combines the data envelopment analysis with the Fuzzy logic (Fuzzy-DEA-BCC), seeking to improve the decision-making units' distinction in the face of uncertainty. In this study, Taguchi's orthogonal arrays were used to generate the necessary quantity of DMUs, and the output variables were generated by the simulation. Two study objects were utilized as examples of mono-andmultiobjective problems. Results confirmed the reliability and applicability of the proposed method, as it enabled a significant reduction in search space and computational demand when compared to conventional simulation optimization techniques.

关键词： search algorithms SIMULATION methods & models MATHEMATICAL optimization PROBLEM solving FUZZY logic DATA envelopment analysis

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A Free search Krill Herd Algorithm for Functions Optimization

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-21页

作者： Li, Liangliang Zhou, Yongquan Xie, Jian Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China

To simulate the freedom and uncertain individual behavior of krill herd, this paper introduces the opposition based learning (OBL) strategy and free search operator into krill herd optimization algorithm (KH) and proposes a novel opposition-based free search krill herd optimization algorithm (FSKH). In FSKH, each krill individual can search according to its own perception and scope of activities. The free search strategy highly encourages the individuals to escape from being trapped in local optimal solution. So the diversity and exploration ability of krill population are improved. And FSKH can achieve a better balance between local search and global search. The experiment results of fourteen benchmark functions indicate that the proposed algorithm can be effective and feasible in both low-dimensional and high-dimensional cases. And the convergence speed and precision of FSKH are higher. Compared to PSO, DE, KH, HS, FS, and BA algorithms, the proposed algorithm shows a better optimization performance and robustness.

关键词： search algorithms MATHEMATICAL optimization STOCHASTIC convergence ROBUST control PRECISION (Information retrieval) MATHEMATICAL analysis

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Hybrid Multiple Soft-Sensor Models of Grinding Granularity Based on Cuckoo searching Algorithm and Hysteresis Switching Strategy

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SCIENTIFIC PROGRAMMING 2015年第1期2015卷 1-11页

作者： Wang, Jie-Sheng Shen, Na-Na Univ Sci & Technol Liaoning Natl Financial Secur & Syst Equipment Engn Res Ct Anshan 114044 Peoples R China Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

According to the characteristics of grinding process and accuracy requirements of technical indicators, a hybrid multiple soft-sensor modeling method of grinding granularity is proposed based on cuckoo searching (CS) algorithm and hysteresis switching (HS) strategy. Firstly, a mechanism soft-sensor model of grinding granularity is deduced based on the technique characteristics and a lot of experimental data of grinding process. Meanwhile, the BP neural network soft-sensor model and wavelet neural network (WNN) soft-sensor model are set up. Then, the hybrid multiple soft-sensor model based on the hysteresis switching strategy is realized. That is to say, the optimum model is selected as the current predictive model according to the switching performance index at each sampling instant. Finally the cuckoo searching algorithm is adopted to optimize the performance parameters of hysteresis switching strategy. Simulation results show that the proposed model has better generalization results and prediction precision, which can satisfy the real-time control requirements of grinding classification process.

关键词： search algorithms HYBRID systems PREDICTION models HYSTERESIS GENERALIZATION CLASSIFICATION

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