When investigating multi-optima problems, a particle swarm algorithm should not converge on a single optima but ideally should explore many optima by continual searching. The common practice of only evaluating each pa...
详细信息
ISBN:
(纸本)0780393635
When investigating multi-optima problems, a particle swarm algorithm should not converge on a single optima but ideally should explore many optima by continual searching. The common practice of only evaluating each particle's performance at discrete intervals can, at small computational cost, be used to adjust particle behaviour in situations where the swarm is 'settling' so as to encourage the swarm to explore further. An algorithm is proposed that, by making each wave of particles partially independent, is suitable for multi optima problems.
PID control algorithm has been extensively used in process controls. But the PID control parameters tuning is a complicated process in the controller. This paper proposes a solution to do optimal selection of process ...
详细信息
ISBN:
(纸本)9781467395878
PID control algorithm has been extensively used in process controls. But the PID control parameters tuning is a complicated process in the controller. This paper proposes a solution to do optimal selection of process control parameters by particle swarm algorithm. The particle swarm algorithm can be applied to obtain the best control parameters of the fitness function with least-error and least-overshoot optimization criterion. A better control effect is achieved in the process simulation of the second order system's response using these control parameters, which indicates that particle swarm algorithm for process control parameters' optimal selection has a high value in practice.
In this paper, optimal control problems for switched discrete systems are studied and compared to the same optimal problem for a continuous system;in particular we focus on problems in which a prespecified sequence of...
详细信息
ISBN:
(纸本)9781479929542
In this paper, optimal control problems for switched discrete systems are studied and compared to the same optimal problem for a continuous system;in particular we focus on problems in which a prespecified sequence of active subsystems is given and propose a metaheuristic approach to find the optimal switching instants. This approach is based on particle swarm algorithm. The objective is to minimize the performance index, depending on these instants, over a finite time horizon. We assume that a pre-assigned modal sequence is given.
To improve the train line plan quality and meet more transportation requirements, a model is presented to solve the train stops setting problem. We analyze the factors on the train setting problem and define the passe...
详细信息
ISBN:
(纸本)9783662493700;9783662493687
To improve the train line plan quality and meet more transportation requirements, a model is presented to solve the train stops setting problem. We analyze the factors on the train setting problem and define the passenger transport efficiency. Then, an optimization model to improve the transport efficiency is constructed. The quantum particleswarm optimization algorithm is hired to solve the problem. Computing case based on Shanghai-Hangzhou high-speed railway proved the rationality of the model and the high performance of the algorithm. It is a new approach to design train stop plans which also offers constructive support for the managers of the railway bureau.
Allocating the resources efficiently in cloud computing environment is not only an important issue and but also a research focus. A strategy of resource allocation and price adjustment based on particleswarm algorith...
详细信息
ISBN:
(纸本)9780769550169
Allocating the resources efficiently in cloud computing environment is not only an important issue and but also a research focus. A strategy of resource allocation and price adjustment based on particle swarm algorithm is proposed in this paper. According to the workload characteristics, a utility function is designed to evaluate QoS. According to the resource demand from all workloads, the resource prices are dynamically adjusted by the corresponding resource agents in order to obtain maximum profits for each *** results of the simulation experiment show that our strategy has effectiveness and robustness.
Multi-label classification is a generalization of single-label classification, and its samples belong to multiple labels. The K-nearest neighbor algorithm can solve this problem as an optimization problem. It finds th...
详细信息
ISBN:
(纸本)9780769551593
Multi-label classification is a generalization of single-label classification, and its samples belong to multiple labels. The K-nearest neighbor algorithm can solve this problem as an optimization problem. It finds the optimum solution by caculating the distance between each sample in general. But in fact, the distance of K-nearest neighbor algorithm may be miscalculated due to the caused by the redundant or irrelevant characteristic value. In order to solve this problem, in this paper, we propose a novel method that uses the particle swarm algorithm to optimize the feature weights to improve the accuracy of distance calculation. As a result, it can improve classification accuracy further. The experimental results show that applying particle swarm algorithm's optimization technique to improving K-nearest neighbor algorithm for multi-label classification problem, can improve the accuracy of classification effectively.
Hybrid multi-objective particle swarm algorithm is applied to vehicle routing problem and achieved good results, this paper based on the previous work, dynamic inertia weight is added to the particle swarm algorithm w...
详细信息
ISBN:
(纸本)9783037851555
Hybrid multi-objective particle swarm algorithm is applied to vehicle routing problem and achieved good results, this paper based on the previous work, dynamic inertia weight is added to the particle swarm algorithm with intelligence factors, it improved the global search ability and the capacity of local convergence of the particle swarm algorithm;and the idea of immunity is introduced in the algorithm,which makes the hybrid multi-objective particle swarm algorithm can effectively discard the repeated solutions in solving vehicle routing problems, this operation can improve the efficiency of the algorithm, and obtain better results under the same conditions.
The performance of Multiple Input Multiple Output (MIMO) systems is significantly influenced by Antenna Gain Imbalance (AGI). A novel AGI detection method using particleswarm Optimization (PSO) algorithm is present i...
详细信息
ISBN:
(纸本)9781467358293;9781467358309
The performance of Multiple Input Multiple Output (MIMO) systems is significantly influenced by Antenna Gain Imbalance (AGI). A novel AGI detection method using particleswarm Optimization (PSO) algorithm is present in this paper. Based on the establishment of the AGI model, the AGI detection problem is described as an optimization problem considering multi-user channel information. The PSO algorithm is introduced to minimize fitness function expressed by the mean square error of the AGI parameters through adjusting the positions of the particles. Further, the convergence condition is obtained from swarm size and iteration number. The simulation results show that the PSO algorithm is much better than statistic method and can obtain nearly the same performance as the optimal method with low complexity.
particleswarm optimization is a global random search algorithm that is simulated by mimicking the behavior of migration and aggregation of birds. In order to improve the global search ability of the algorithm, this p...
详细信息
ISBN:
(纸本)9781538652145
particleswarm optimization is a global random search algorithm that is simulated by mimicking the behavior of migration and aggregation of birds. In order to improve the global search ability of the algorithm, this paper proposes a new inertia weight. For the constrained optimization problem, this paper controls the number of particles that violate the constraint conditions, and proposes a new particle selection method to improve the ability of the particle swarm algorithm to search for boundaries. Finally, experiments were performed using three benchmark functions, and the results show that the optimization speed of the improved particle swarm algorithm has been greatly improved.
Data mining deals with the problem of discovering novel and interesting knowledge from large amount of data. It is the core problem in building a fuzzy classification system to extract an optimal group of fuzzy classi...
详细信息
ISBN:
(纸本)9783540893752
Data mining deals with the problem of discovering novel and interesting knowledge from large amount of data. It is the core problem in building a fuzzy classification system to extract an optimal group of fuzzy classification rules from fuzzy data set. To efficiently mine the classification rule from databases, a novel classification rule mining algorithm based on particleswarm optimization (PSO) was proposed. The experimental results show that the proposed algorithm achieved higher predictive accuracy and much smaller rule list than other classification algorithm.
暂无评论