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7th International Joint Conference on Computational Sciences and optimization (CSO)

作者： Ye, Kaibin Shi, Xiaolun Li, Huijian Shi, Ning South China Agr Univ Coll Sci Guangzhou Guangdong Peoples R China Sun Yat Sen Univ Sch Business Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781479953721

Due to the increased complexity of port operations, port project portfolio management has become more and more essential. However, in port project portfolio management, the demand for resources is usually uncertain. In order to reduce the cost and the difficulty of resource allocation, we pay more attention to balancing resource allocation than minimizing the maximum value of the consumption of resources. Thus we consider a resource leveling problem in port project portfolio management. In this paper, we introduce a combination of entropy weight and particle swarm optimization algorithm. This proposed method is applied to a case study. And the results show that this method can achieve satisfactory performance in practice.

关键词： port project portfolio management resource leveling particle swarm optimization algorithm entropy weight

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Computing Single Step Operators of Logic Programming in Radial Basis Function Neural Networks

Computing Single Step Operators of Logic Programming in Radi...

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21st National Symposium on Mathematical Sciences (SKSM)

作者： Hamadneh, Nawaf Sathasivam, Saratha Choon, Ong Hong Univ Sains Malaysia Sch Math Sci Usm 11800 Penang Malaysia

ISBN: (纸本)9780735412415

Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T-p : I -> I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.

关键词： Single step operator particle swarm optimization algorithm recurrent radial basis function neural networks

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A Fuzzy Multi-Objective optimization Method Solving the Output of Energy Storage System 6

A Fuzzy Multi-Objective Optimization Method Solving the Outp...

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6th International Symposium on Parallel Architectures, algorithms, and Programming (PAAP)

作者： Wang, Yiyi Huang, Mei Zhang, Caiping Beijing Jiaotong Univ Natl Act Distribut Network Technol Res Ctr NANTEC Beijing Peoples R China

ISBN: (纸本)9781479938445

The electricity consumption of a charging station is obviously fluctuant. An energy storage system is an effective device to improve the load variation of the charging station. In order to give full play to energy storage system, this paper proposes a charging and discharging control strategy of the storage system considering multi-objective including stabilizing load and economizing electric consumption, changing a bi-objective problem into a single objective problem with weights after fuzzy processing. This paper solves the problem based on particle swarm optimization algorithm, and considers constraints including the demand of uninterruptible power supply and SOC at given time, which has greater practical significance. In this paper, the data of a practical integrated smart grid construction project is taken into considered. Finally, we verify the effectiveness and feasibility of the optimization strategy after simulation analysis.

关键词： energy storage system charging scheduling electric vehicles particle swarm optimization algorithm multi-objective optimization

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Identifying New Prognostic Features for Remaining Useful Life Prediction 16

Identifying New Prognostic Features for Remaining Useful Lif...

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16th International Power Electronics and Motion Control Conference and Exposition (PEMC)

作者： Boukra, Tahar Lebaroud, Abdesselam Univ 20 Aout 1955 Skikda Dept Genie Elect Skikda Algeria

ISBN: (纸本)9781479920600

An accurate prediction of the remaining useful life (RUL) from a prognosis system relies on a good selection of prognosis features. The latter should well capture the trend of the fault progression. In situation where the existence of the fault to failure data is rare and the development of degradation based model is difficult, we must be addressed to the identification of new features having the qualities mentioned above. This paper present an new selection method based upon the particle swarm optimization (PSO) algorithm to identify the advanced prognosis feature and the Hidden Semi Markov Model (HSMM) for the prediction of the remaining useful life. This method was validated on a set of experimental data collected from bearing failures.

关键词： Prognostics Remaining Useful Life particle swarm optimization algorithm Feature selection Hidden Semi Markov Model

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Bio Inspired Approach for Load Balancing to Reduce Energy Consumption in Cloud Data Center

Bio Inspired Approach for Load Balancing to Reduce Energy Co...

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International Conference on Communication, Control and Intelligent Systems

作者： Akhil Goyal Navdeep S. Chahal Software Technology Division C-DAC

ISBN: (纸本)9781467375429

Cloud computing has made it feasible to access various IT resources through a high speed network from anywhere in the world. Constant increasing demand of cloud computing is equally popular in consumers as well as providers. But along with advancement every technology is also associated with some ill effects. On same path, cloud computing also accompanies a serious issue with it and that issue is energy consumption. In this paper Firefly algorithm has been selected as a proposed bio-inspired approach to perform load balancing to reduce energy consumption in cloud data center. Further, the results are compared with particle swarm optimization algorithm (PSO). The energy consumed in case of Firefly algorithm is less than energy consumed in PSO algorithm.

关键词： Cloud Computing Firefly algorithm particle swarm optimization algorithm Energy Consumption Load Balancing

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STUDY ON CLOUD RESOURCE ALLOCATION STRATEGY BASED ON particle swarm ANT COLONY optimization algorithm

STUDY ON CLOUD RESOURCE ALLOCATION STRATEGY BASED ON PARTICL...

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2nd IEEE International Conference on Cloud Computing and Intelligent Systems (CCIS)

作者： Yang, Zhengqiu Liu, Meiling Xiu, Jiapeng Liu, Chen Beijing Univ Posts & Telecommun Sch Software Engn Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

ISBN: (纸本)9781467318556;9781467318570

Cloud computing environment for the efficient resources allocation is an important issue in the field of cloud computing. The resources in Cloud computing application platform are distributed widely and with great diversity. User demands of real-time dynamic change are very difficult to predict accurately. The heuristic ant colony algorithm could be used to solve this kind of problems, but the algorithm has slow convergence speed and parameter selection problems. Aiming at this problem, this paper proposes an optimized ant colony algorithm based on particle swarm to solve cloud computing environment resources allocation problem.

关键词： particle swarm optimization algorithm Ant colony optimization algorithm Cloud resource allocation

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Nonlinear Inertia Weigh particle swarm optimization combines Simulated Annealing algorithm and Application in Function and SVM optimization

Nonlinear Inertia Weigh Particle Swarm Optimization combines...

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3rd International Conference on Mechanical and Electronics Engineering (ICMEE 2011)

作者： Jiao Bin Xu Zhixiang Shanghai DianJi Univ Sch Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9783037852866

This paper proposes an improved particle swarm optimization algorithm (PSO) for the global and local equilibrium problem of searching ability. It improves the iterative way of inertia weight in PSO, using non-linear decreasing algorithm to balance, then PSO combines with simulated annealing(SA). Finally, the optimization test experiments are carried out for the typical functions with the algorithm (ULWPSO-SA), and compare with the basic PSO algorithm. Simulation experiments show that local search ability of algorithm, convergence speed, stability and accuracy have been significantly improved. In addition, the novel algorithm is used in the parameter optimization of support vector machines (ULWPSOSA-SVM), and the experimental results indicate that it gets a better classification performance compared with SVM and PSO-SVM.

关键词： particle swarm optimization algorithm inertia weight simulated annealing algorithm function optimization parameter optimization

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Cultural algorithm Based on particle swarm optimization for Partner Selection of Virtual Enterprise

Cultural Algorithm Based on Particle Swarm Optimization for ...

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31st Chinese Control Conference

作者： Zhou Wei Bu Yan-ping East China Univ Sci & Technol Sch Business Shanghai 200237 Peoples R China Shanghai Jiao Tong Univ Sch Technol Shanghai 201101 Peoples R China

ISBN: (纸本)9789881563811

A novel cultural algorithm based on particle swarm optimization (PSO) algorithm was proposed in this paper. After analyzing the partner selection problems of virtual enterprise, the CPSO algorithm was presented to solve enterprise alliance problem within reasonable time and cost. There are certain number partners of each sub-task in virtual enterprise environment. The objective is, by selecting the optimal combination of partners, to minimize project's completion time and project's total cost. We tested the CPSO algorithm against the PSO method. Simulation results demonstrate that it can be superior to the regular PSO. We also tested the CPSO algorithm with the exhaustion method to show the algorithm's efficiency.

关键词： cultural algorithm particle swarm optimization algorithm virtual enterprise partner selection

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Drilling path optimization by the particle swarm optimization algorithm with global convergence characteristics

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2008年第8期46卷 2299-2311页

作者： Zhu, Guang-Yu Zhang, Wei-Bo Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350002 Peoples R China

Drilling path optimization is one of the key problems in holes-machining. This paper presents a new approach to solve the drilling path optimization problem belonging to discrete space, based on the particle swarm optimization (PSO) algorithm. Since the standard PSO algorithm is not guaranteed to be global convergent or local convergent, based on the mathematical model, the algorithm is improved by adopting the method to generate the stop evolution particle once again to obtain the ability of convergence on the global optimization solution. Also, the operators are proposed by establishing the Order Exchange Unit (OEU) and the Order Exchange List (OEL) to satisfy the need of integer coding in drilling path optimization. The experimentations indicate that the improved algorithm has the characteristics of easy realization, fast convergence speed, and better global convergence capability. Hence the new PSO can play a role in solving the problem of drilling path optimization.

关键词： particle swarm optimization algorithm drilling path optimization global convergence PSO

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PSO-TPS: An Optimal Trust Path Selection algorithm Based on particle swarm optimization in Small World Network

PSO-TPS: An Optimal Trust Path Selection Algorithm Based on ...

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2nd International Conference on Cloud and Green Computing / 2nd International Conference on Social Computing and its Applications (CGC/SCA)

作者： Xu, Guangquan Xu, Chao Tian, Xiuming Zhang, Luxia Li, Xiaohong Li, Weisheng Tianjin Univ Sch Comp Sci & Technol Tianjin 300072 Peoples R China Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing Peoples R China

ISBN: (纸本)9780769548647;9781467330275

Small world network is a type of mathematical graph, in which most nodes are not neighbors of one another, but can be reached from every other by a small number of hops. P2P network is a typical small world network, it has a large scale and high risks, so trust model building and Trust Path Selecting (TPS) become big challenges. To solve TPS problem, we first reduce the scale of the complex P2P network by identifying and deleting the equivalent nodes. Then we provide a Trust Path Selection algorithm based on particle swarm optimization (PSO). In the algorithm, after initializing the particle swarm, each particle can update the speed and location according to its information, and then produce a new particle with better value. Repeating that process continually to implement the global search of the space, we can get the better trust path in the networks. The experimental results show that this algorithm is effective and efficient in finding the suboptimal solution of trust path, hence it can be applied in trust path searching in such small-world networks as P2P network.

关键词： Small World Network P2P Network Super Node particle swarm optimization algorithm

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