This paper presents an algorithm based on OBB bounding box and particleswarm hybrid collision detection algorithm. algorithm use hierarchical bounding boxes OBB Rapid exclude some objects do not intersect, only the n...
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ISBN:
(纸本)9783642145322
This paper presents an algorithm based on OBB bounding box and particleswarm hybrid collision detection algorithm. algorithm use hierarchical bounding boxes OBB Rapid exclude some objects do not intersect, only the nodes in the collision of the use of particleswarmoptimization in the searching. To play a level bounding box algorithm and improved particleswarm-based collision detection algorithm Random their own advantages. Finally, Experimental results demonstrate the effectiveness of hybrid collision detection algorithm.
In this paper we present partitioning for simultaneous cut size and circuit delay minimization. Due to the random search, of simulated annealing algorithms, the solution of a circuit partitioning problem is global opt...
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In this paper we present partitioning for simultaneous cut size and circuit delay minimization. Due to the random search, of simulated annealing algorithms, the solution of a circuit partitioning problem is global optimum. [1] The circuit is divided into partitions and number of interconnections between them is minimized. [2] PSO employs a collaborative population-based search, which is inspired by the social behavior of bird flocking. It combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. SA employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule. Experimental result shows that the developed hybrid PSO and SA algorithm can consistently produce the better result than the other algorithms.
To improve the scheduling optimization of mass customization collaborative logistics, a scheduling solution developed based on the novel particle swarm optimization algorithm was proposed. A mathematic model based on ...
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To improve the scheduling optimization of mass customization collaborative logistics, a scheduling solution developed based on the novel particle swarm optimization algorithm was proposed. A mathematic model based on scheduling strategy of mass customization logistics was designed. The novel dynamic particle swarm optimization algorithm framework was given. And simulation experiments were done to validate algorithm. Experiment results show that the proposed algorithm effectively improves the scheduling optimization of mass customization collaborative logistics, which has direct applications for Logistics scheduling
particleswarmoptimization is a new heuristic global optimizationalgorithm based on swarm intelligence. The algorithm is simple, easy to implement and has good performance of optimization. Now it has been applied in...
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ISBN:
(纸本)9787811240559
particleswarmoptimization is a new heuristic global optimizationalgorithm based on swarm intelligence. The algorithm is simple, easy to implement and has good performance of optimization. Now it has been applied in many fields. However, when optimizing multidimensional and multimodal functions, the basic particleswarmoptimization is apt to be trapped in local optima, which is called premature. This paper proposes a modified optimization method (MPSO), which considers for convergence speed and search capacity. This modified algorithm has stronger exploitation ability, so it can prevent premature well. Simulation results show that this modified algorithm performs better performance. It is used in segmentation of infrared image. The experimental results show that the modified PSO not only realizes the image segmentation well;but also improves the speed greatly.
In this paper the numerical computation theory of internal trajectory was researched. Based on this, a multi-parameter fitting calculation model was established in order to get higher precise computation results. Thro...
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In this paper the numerical computation theory of internal trajectory was researched. Based on this, a multi-parameter fitting calculation model was established in order to get higher precise computation results. Through analysing the model, this paper selected the burning rate exponent n and the specific heat ratio γ as the sensitive internal trajectory parameters, meanwhile selected the in-bore maximum pressure m p and the muzzle velocity g v correspondingly as the fitting parameters to improve the fitting efficiency. To do the fitting work, an improved particleswarm optimisation (PSO) algorithm was presented in this paper. The calculation results indicated that the fitting process using improved PSO was obviously better than those using standard PSO and genetic algorithm (GA) in global searching ability, convergence rate and fitting precision. In this case, the method in this paper is more suitable to the multi-parameter fitting calculation of internal trajectory considering requirements of reliable design and on site testing.
In power market environment with energy conservation and emission reduction, clean energy power has an increasingly important position because of its low cost and environmental pollution. This paper researches on powe...
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In power market environment with energy conservation and emission reduction, clean energy power has an increasingly important position because of its low cost and environmental pollution. This paper researches on power system dynamic economy dispatch including wind system. The model of environment economic dispatch including wind power is established with the lowest generating cost of total power system as the objective function. The various constraint conditions are considered with conventional thermal power units and wind power. According to actual load data in a certain area, simulation test is completed using particle swarm optimization algorithm which has advantage of strength searching capability and fast optimizing. Simulation results show that the mathematical model is correct and the optimizationalgorithm is effective. Meanwhile, the application of the relative entropy balance theory is on evaluation and selection to the decision results.
Threshold extraction is the fundamental step in multi-threshold image *** paper has introduced particle swarm optimization algorithm (PSO) for threshold *** when dealing with the peaky high dimension function of maxim...
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Threshold extraction is the fundamental step in multi-threshold image *** paper has introduced particle swarm optimization algorithm (PSO) for threshold *** when dealing with the peaky high dimension function of maximum entropy for multi-threshold image segmentation, the conventional PSO is apt to be trapped in local optima called *** can cause image segmentation *** paper proposes a modified particleswarmoptimization method (MPSO), which improves convergence speed and search capacity and avoid the premature phenomena when used in threshold *** results show that the MPSO has better performance and quicker *** experimental results also show that with the modified PSO as a threshold extraction method, the image is segmented fairly well and the segmentation speed improves greatly.
Vertical particle swarm optimization algorithm (VPSO) is proposed in this *** new algorithm assumes that the particles tend to fly towards two *** is flying toward the global best *** other is flying toward the vertic...
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Vertical particle swarm optimization algorithm (VPSO) is proposed in this *** new algorithm assumes that the particles tend to fly towards two *** is flying toward the global best *** other is flying toward the vertical *** there is a random value produced in every iteration step to measure the probability of flying into two *** VPSO and particle swarm optimization algorithm (PSO) are used to train neural network (NN) and applied in soft-sensor of acrylonitrile ***, simulation results show that the method proposed by this paper is feasible and effective in soft-sensor of acrylonitrile yield.
Based on particle swarm optimization algorithm, this paper presents a grid scheduling optimizationalgorithm combing the advantages of Ant Colony optimizationalgorithm. The algorithm processes task scheduling through...
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ISBN:
(纸本)9780878492695
Based on particle swarm optimization algorithm, this paper presents a grid scheduling optimizationalgorithm combing the advantages of Ant Colony optimizationalgorithm. The algorithm processes task scheduling through particle swarm optimization algorithm to get a group of relatively optimal solutions, and then conducts small-area local search with Ant Colony optimizationalgorithm. Theoretical analysis and results of the simulation experiments show that this scheduling algorithm effectively achieves load balancing of resources with comprehensive advantages in time efficiency and solution accuracy compared to the traditional Ant Colony optimizationalgorithm and particleswarmoptimizationalgorithm, and can be applied to task scheduling in grid computing.
In order to exert the advantage of ant colony algorithm and particle swarm optimization algorithm respectively,a method combined the two algorithms was designed for solving multi-objective flexible job shop scheduling...
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In order to exert the advantage of ant colony algorithm and particle swarm optimization algorithm respectively,a method combined the two algorithms was designed for solving multi-objective flexible job shop scheduling problem in this *** proposed algorithm was composed by two *** first phase made use of the fast convergence of PSO to search the particles optimum position and made the position as the start point of *** the second phase,the traditional ant colony algorithm was improved and was used to search the global optimum scheduling according to its characters of positive feedback and structure of solution *** combined algorithm was validated by practical *** results obtained have shown the proposed approach is feasible and effective for the multi-objective flexible job shop scheduling problem.
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