Cloud Genetic Algorithm Research Articles

Mathematical Modeling Method of the Improved Genetic Algorithm for Random Power Fluctuation

In order to solve the problem that the traditional genetic algorithm has a slow search speed and is easy to fall into the local optimal solution, a mathematical modeling method of an improved genetic algorithm in random power fluctuation is proposed. Drawing on the idea of a genetic algorithm (GA) and using the randomness and stability trend of cloud droplets of the normal cloud model, the author proposes a new genetic algorithm, cloud genetic algorithm (CGA). CGA is implemented by the Y-condition cloud generator of the normal cloud model to realize the cross operation, and the basic cloud generator realizes the mutation operation. Finally, the power function optimization experiment and the IIR digital filter optimization design are carried out, and the standard GA, NQGA, CAGA, and LARES algorithms are carried out compared. The experimental results show that the IIR digital filter designed by CGA has the smallest maximum ripple (Ap) in the passband, which is 0.342, and the largest minimum attenuation (As) in the stopband, which is 34.27. It can be observed that the overall performance is better. The validity of the algorithm is proved, and it has a certain reference and application value.

Port Supply-Chain Benefit Distribution Model and Algorithm in a Cloud Environment

Huo, H. and Wang, Z., 2019. Port supply-chain benefit distribution model and algorithm in a cloud environment. In: Li, L.; Wan, X., and Huang, X. (eds.), Recent Developments in Practices and Research on Coastal Regions: Transportation, Environment and Economy. Journal of Coastal Research, Special Issue No. 98, pp. 58–61. Coconut Creek (Florida), ISSN 0749-0208.On the basis of the cloud environment, through mathematical analysis, the port supply-chain interest coordination model is established. According to the construction goals and principles of the port supply-chain benefit distribution based on the cloud environment, the technical model and member structure constructed under the cloud environment are described. The overall process of operation describes the port supply; at the same time, we focus on the pre-evaluation and evaluation of the process, and the mathematical model is established and solved by cloud genetic algorithm. Finally, the increased profit of the port supply chain under the cloud environment is rationally distributed to promote the stability of the port supply chain. At the same time, the risk assessment model of port supply chain based on support vector machine is established, and compared with back propagation neural network and radial basis neural network evaluation models. At the same time, considering the error of data acquisition, the fault-tolerant value is introduced into the collected data to form a fault-tolerant interval. The data of the port supply-chain risk assessment model are analyzed by the rough neural network method. The error-tolerant values are evaluated. The model provides a reference for the establishment and development of the port supply-chain risk assessment model.

Cost Optimization Control of Logistics Service Supply Chain Based on Cloud Genetic Algorithm

As a kind of service supply chain, logistics service supply chain is an important trend in the development of logistics industry. Compared with developed countries in foreign countries, the level of logistics development in China is lagging behind. Facing the unprecedented competition pressure from foreign logistics enterprises, how to realize the construction and operation with high quality and low cost in logistics service supply chain is one of the key problems at present. Quality cost is the cross field of quality management and cost control, and its function has been widely verified at home and abroad. Therefore, through the research and analysis of the quality cost, the construction and cost optimization method of logistics service supply chain based on cloud genetic algorithm is proposed. The quality cost theory is applied to the related research of logistics service supply chain to realize the integration between integrated logistics service providers and functional logistics service providers with high quality and low cost. At the same time, it can provide the basis and means for quality management and cost control in the long-term operation of logistics service supply chain. Finally, the effectiveness of quality cost in the construction and operation optimization of logistics service supply chain is verified through the case of S Company.

Research on Traffic Flow Prediction in the Big Data Environment Based on the Improved RBF Neural Network

This paper proposes an optimized prediction algorithm of radial basis function neural network based on an improved artificial bee colony (ABC) algorithm in the big data environment. The algorithm first uses crossover and mutation operators of the differential evolution algorithm to replace the search strategy of employed bees in the ABC algorithm, then improves the search strategy of onlookers in the ABC algorithm to produce an optimal candidate food source near the population. The algorithm can better balance local and global searching capability. To verify the efficiency of this algorithm in the big data environment, apply it to Lozi and Tent chaotic time series and measured traffic flow time series, and then compare it with K-nearest neighbor model, radial basis function (RBF) neural network model, improved back propagation neural network model, and RBF neural network based on a cloud genetic algorithm model. The experimental results indicate that the accuracy of prediction for Lozi and Tent chaotic time series and the measured traffic flow improves greatly in the big data environment using the proposed algorithm, which proves the effectiveness of the proposed algorithm in predicting traffic flow time series.

Routing of Military Communication Networks Based on Cloud Genetic Algorithm

Routing of Military Communication Networks Based on Cloud Genetic Algorithm

A novel approach based on the Gauss-vSVR with a new hybrid evolutionary algorithm and input vector decision method for port throughput forecasting

The prediction of port throughput is very complicate, and its accuracy is affected by many socio-economic factors, particularly affected by their embedded distributed randomness of these factors and mixed noises produced in the processes of data collection, transformation, and calculation. Firstly, in view of the v-support vector regression hybridized with Gauss function (briefed as Gauss-vSVR model), to well solve the nonlinear and mixed noises, this paper uses this model to simulate the nonlinear evolving system of port throughput series. Then, to look for more suitable parameter combination of this model and take into account that GA still suffers from the problems of trapped into local optima and time-consuming, this study integrates the global chaotic perturbation algorithm by using Cat mapping function and local acceleration search algorithm by employing cloud theory, i.e., abbreviated as chaotic cloud genetic algorithm (CCGA), to well determine the parameter values for an Gauss-vSVR model. Additionally, based on the principal component analysis and correlation analysis method, an input vector decision method (namely IVD) is proposed to identify the final input variables for Gauss-vSVR model. Finally, hybridization of IVD and CCGA with Gauss-vSVR model, namely IGvSVR-CCGA, is proposed for port throughput forecasting. Subsequently, the port throughput data and its associate socio-economic factors of two largest Chinese ports, Shanghai Port and Tianjin Port, are employed as practical examples to test forecast performance. The numerical results indicate that the proposed hybrid forecasting model receives more satisfied forecasting performance than other classical prediction models; in the meanwhile, the CCGA algorithm also obtains higher optimal efficiency than other alternative algorithms.

The stabilization and 3D visual simulation of the triple inverted pendulum based on CGA-PIDNN

Aiming at the triple inverted pendulum which is a strong coupling, multivariable, high-order and unsteady system, a design method of the controller based on PID neural network (PIDNN) optimized by cloud genetic algorithm (CGA) is proposed, this method is called CGA-PIDNN. CGA can be applied to learn and train the PIDNN connection weights. CGA can overcome the defect of the slow convergence rate and premature convergence for genetic algorithm (GA). PIDNN is a simple and normative network which is easy to be realized and has a good dynamic performance. The CGA-PIDNN control system of triple inverted pendulum is verified with MATLAB simulation test. The comparison results with the control effect of PIDNN control system optimized by standard GA (GA-PIDNN) are presented first. Then in LabVIEW environment, by using the combination of virtual reality technology and MATLAB, the three-dimensional (3D) animation simulation model of the triple inverted pendulum CGA-PIDNN control system is built. The simulation results indicate that CGA-PIDNN control method is effective, whose control effects are superior to those by GA-PIDNN control, it is believed that CGA-PIDNN is effective and will become a promising candidate of control methods.

Multi-objective optimization model for multi-project scheduling on critical chain

In this paper, a multi-project scheduling in critical chain problem is addressed. This problem considers the influence of uncertainty factors and different objectives to achieve completion rate on time of the whole projects. This paper introduces a multi-objective optimization model for multi-project scheduling on critical chain, which takes into consideration multi-objective, such as overall duration, financing costs and whole robustness. The proposed model can be used to generate alternative schedules based on the relative magnitude and importance of different objectives. To respond to this need, a cloud genetic algorithm is proposed. This algorithm using randomness and stability of Normal Cloud Model, cloud genetic algorithm was designed to generate priority of multi-project scheduling activities and obtain plan of multi-project scheduling on critical chain. The performance comparison shows that the cloud genetic algorithm significantly outperforms the previous multi-objective algorithm.

Resource Continuity Constraints in Repetitive Project Scheduling

In projects with repeating activities and in which the duration of activity unit has been influenced by resource-constrained, Repetitive Scheduling Method (RSM) can be combined to ensure uninterrupted usage of resources between similar activities in different units. The reason for this approach is that in practice the application of RSM may be hindered by several considerations, in this paper we present a cloud genetic algorithm for repetitive project scheduling, in which considering Resource continuity constraints in Repetitive Project scheduling to solve the problem. An example application is presented to illustrate implementation and features of the proposed model. Finally, the relationship of duration and limited amount of resources have been analyzed.

Research of order allocation model based on cloud and hybrid genetic algorithm under ecommerce environment

For massive order allocation problem of the third party logistics (TPL) in ecommerce, this paper proposes a general order allocation model based on cloud architecture and hybrid genetic algorithm (GA), implementing cloud deployable MapReduce (MR) code to parallelize allocation process, using heuristic rule to fix illegal chromosome during encoding process and adopting mixed integer programming (MIP) as fitness function to guarantee rationality of chromosome fitness. The simulation experiment shows that in mass processing of orders, the model performance in a multi-server cluster environment is remarkable superior to that in stand-alone environment. This model can be directly applied to cloud based logistics information platform (LIP) in near future, implementing fast auto-allocation for massive concurrent orders, with great application value.

Distribution Network Reconfiguration with Distributed Generation Based on Cloud Genetic Algorithm

According to the traditional distribution network reconfiguration, the fault feeder with distributed generation (DG) will separate from the distribution network immediately while the network goes wrong. In order to improve the system power supply reliability and the utilization rate of DG, the new standard allows the distribution network change into island operation. This paper establishes the mathematics model of the distribution network reconfiguration with DG, the objective function and constraint conditions. The traditional genetic algorithm (GA) has the shortcomings of premature convergence and slow convergence speed to solve this nonlinear optimization problem. This paper applies the cloud genetic algorithm (CGA) to solve the network reconfiguration problem. The Case study on IEEE33 test system shows that the algorithm is reasonable and effective.