In the cloud manufacturing environment, workshop resource scheduling serves as a pivotal component, characterized by increased dynamics and complexities. Nevertheless, existing dynamic scheduling methods are often lim...
详细信息
In the cloud manufacturing environment, workshop resource scheduling serves as a pivotal component, characterized by increased dynamics and complexities. Nevertheless, existing dynamic scheduling methods are often limited to solving specific dynamic events. Thus, considering the actual workshop resource scheduling in a cloud manufacturing environment, this article examines the methods to address unexpected events including randomly arriving tasks, resource breakdown, as well as resource maintenance. Besides, a dynamic scheduling method based on the Game Theory, considering workshop capacity in cloud manufacturing, was developed. In the first place, the priority of workshop tasks was evaluated by Game Theory, and the optimal task processing sequence in the workshop was determined to maximize benefits. Secondly, to verify the dynamic regulation performance of the method, it was combined with the particleswarmoptimization (PSO) algorithm considering multi-objective factors to obtain an ameliorated PSO algorithm addressing the challenge of resource optimization scheduling in a genuinely dynamic workshop environment. Finally, this method was tested through a case study, and the results demonstrate that it can achieve superior dynamic and static performance compared to alternative algorithms.
The algorithm herein adopts density-based method and max-min distance method to define initial clustering center to eliminate the need for defining clustering center in advance in k-means algorithm,and normalize the d...
详细信息
The algorithm herein adopts density-based method and max-min distance method to define initial clustering center to eliminate the need for defining clustering center in advance in k-means algorithm,and normalize the data set to reduce the influence of fluctuation of attribute value for each dimension of sample set on accuracy of clustering ***,it obtains dissimilarity matrix and takes advantage of good global convergence ability of particle swarm optimization algorithm to improve proneness of K-means algorithm to be trapped in local *** effectiveness of the algorithm was verified via ***,although the algorithm herein performs well in part of small low dimensional data set,while how to effectively make cluster analysis on large high dimensional data still needs to be further researched.
In this paper, the problem of both bandwidth and power allocation for two-way multiple relay systems in overlay cognitive radio (CR) set-up is investigated. In the CR overlay mode, primary users (PUs) cooperate with c...
详细信息
ISBN:
(纸本)9781479935130
In this paper, the problem of both bandwidth and power allocation for two-way multiple relay systems in overlay cognitive radio (CR) set-up is investigated. In the CR overlay mode, primary users (PUs) cooperate with cognitive users (CUs) for mutual benefits. In our framework, we propose that the CUs are allowed to allocate a part of the PUs spectrum to perform their cognitive transmission. In return, acting as an amplify-and-forward two-way relays, they are used to support PUs to achieve their target data rates over the remaining bandwidth. More specifically, CUs acts as relays for the PUs and gain some spectrum as long as they respect a specific power budget and primary quality-of-service constraints. In this context, we first derive closed-form expressions for optimal transmit power allocated to PUs and CUs in order to maximize the cognitive objective. Then, we employ a strong optimization tool based on particle swarm optimization algorithm to find the optimal relay amplification gains and optimal cognitive released bandwidths as well. Our numerical results illustrate the performance of our proposed algorithm for different utility metrics and analyze the impact of some system parameters on the achieved performance.
PSO algorithm is a kind of swarm intelligence optimizationalgorithm which has the advantages of simple principle,easy implementation,few parameters needed to adjust and so ***,the search accuracy of the basic PSO alg...
详细信息
PSO algorithm is a kind of swarm intelligence optimizationalgorithm which has the advantages of simple principle,easy implementation,few parameters needed to adjust and so ***,the search accuracy of the basic PSO algorithm still needs to be *** this paper,a modified PSO algorithm using exponent decline inertia weight is put forward and successfully applied to the parameter identification of the furnace pressure *** modified PSO algorithm combines the nonlinear optimization and genetic algorithm to optimize the inertia weight and acceleration constants of the basic PSO algorithm,and is proved to be effective in parameter identification.
According to the time and space, randomness and volatility of traffic flow, a short-term traffic flow forecasting model based on empirical mode decomposition(EMD), genetic particleswarmoptimization(
ISBN:
(纸本)9781467389808
According to the time and space, randomness and volatility of traffic flow, a short-term traffic flow forecasting model based on empirical mode decomposition(EMD), genetic particleswarmoptimization(
In this paper, considering the load, wind power and photo voltaic timing characteristics, stablishes a planning model containing distributed power energy storage device. In the planning model, the minimization of the ...
详细信息
ISBN:
(纸本)9781510813465
In this paper, considering the load, wind power and photo voltaic timing characteristics, stablishes a planning model containing distributed power energy storage device. In the planning model, the minimization of the total cost including the initial investment, fuel costs, net loss costs, environmental damage costs, operation and maintenance costs and power purchase costs. To improve the acceptance ability of the distribution network, and to ensure the economy. Then, using the particle swarm optimization algorithm to solve a typical example, to show that the model and the proposed method is correct and effective.
A modified algorithm is proposed according to the multi-objective constrained optimization *** order to let constraint conditions convert to an optimization objective used a transform strategy,which is a satisfactory ...
详细信息
A modified algorithm is proposed according to the multi-objective constrained optimization *** order to let constraint conditions convert to an optimization objective used a transform strategy,which is a satisfactory summation function of constraint conditions,to accelerate the convergence rate,a new region changed acceleration mechanism is used,and for shake of improving the local search ability,chaos search technology is *** modified algorithm not only improves the diversity of solution set but also makes the nondominated solutions approach the Pareto set as close as *** last,the algorithm is applied to three classical test functions;the optimization performance of modified algorithm is evaluated and numerical experimental results show the effectiveness of the proposed method.
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...
详细信息
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.
Gravity Search algorithm（GSA） is a swarm intelligence optimizationalgorithm based on the gravity *** standard GSA algorithm has strong global search capability,while its convergence speed is *** particleswarm Opti...
详细信息
Gravity Search algorithm（GSA） is a swarm intelligence optimizationalgorithm based on the gravity *** standard GSA algorithm has strong global search capability,while its convergence speed is *** particleswarmoptimization（PSO） algorithm has high convergence speed and search *** on the advantages of the above two algorithms,a hybrid algorithm（PSOGSA） is proposed in this paper,and two adaptive weighted update strategies are introduced into the optimization process to improve the search accuracy of the hybrid *** the same time,we added variable mutation probability to solve the problem that particles are easily be trapped in local *** order to verify the effectiveness of the two improved hybrid algorithms,the two algorithms are applied to the power system economic load dispatch（ELD） *** generation cost optimization performance tests are computed for three groups of power systems with different unit *** simulation results show that the two adaptive weighted hybrid algorithms which are proposed in this paper can effectively reduce the generation cost of the power system.
Quantum particleswarmalgorithm integrated the quantum behavior with particle swarm optimization algorithm,is used to settle the majorization question of calculating available transmission *** by using the software o...
详细信息
Quantum particleswarmalgorithm integrated the quantum behavior with particle swarm optimization algorithm,is used to settle the majorization question of calculating available transmission *** by using the software of Matlab to IEEE-30 bus system as an example of the simulation,after comparing the simulation results with the traditional particle swarm optimization algorithm results,we dissected the optimization performance and convergence speed of the above two algorithms,and verify the effectiveness of quantum particleswarmalgorithm to settle the majorization question of the available transmission capability.
暂无评论