Hybrid GA Approach Research Articles

Location-Aware and Budget-Constrained Service Deployment for Composite Applications in Multi-Cloud Environment

Enterprise application providers are increasingly moving their workloads to the cloud for technical and economic benefits. Multi-cloud environment makes it possible to orchestrate multiple cloud resources. With the increasing number of available cloud resources provided by multiple cloud providers at different locations with different prices, application providers face the challenge to select proper cloud resources to deploy their applications in the form of a workflow of component service units. Existing studies usually consider minimizing execution time or/and deployment cost. From the perspective of application providers, however, they also pay huge attention to application response time, including particularly network latency between deployed services and users. Meanwhile, application deployment is often subject to stringent budgetary control to ensure financial viability. This article studies a new type of composite application deployment problem that jointly considers both the performance optimization and budget control in multi-cloud at the global scale. To find solutions with minimal response time without running into the risk of over-spending, we propose a hybrid GA-based approach, featuring new design of domain-tailored service clustering, repair algorithm, solution representation, population initialization, and genetic operators. Extensive experiments using the real-world dataset demonstrate that our proposed hybrid GA approach outperforms some recently proposed approaches.

Modified diffusion model with multiple products using a hybrid GA approach

As technology advances, the speed in which new products are developed also increases. Due to such increases, product forecasting has become much more vital for a company. The Bass diffusion model is a demand-forecast model that explores the phases of a product’s life cycle that have been successful in the diffusion of forecasting innovation in new products. Recognizing the need for an efficient parameter estimation method for multi-product forecasting, we have conducted research using the hybrid genetic algorithm (HGA). The research conducted will provide an alternate approach to explore the forecasting capability of the diffusion models without having as many limitations as the original method. We used both published data and LCD-monitor global sales data to test and verify our method. Results show that the proposed model using a hybrid GA approach can improve the forecasting efficiency.

Multi-objective transmission network planning by a hybrid GA approach with fuzzy decision analysis Systems, 2003, 25, (3), 187–192

Multi-objective transmission network planning by a hybrid GA approach with fuzzy decision analysis Systems, 2003, 25, (3), 187–192

Multi-objective transmission network planning by a hybrid GA approach with fuzzy decision analysis

In this paper, power system transmission network planning is formulated as a multi-objective mathematical optimization problem. In this context, three objectives: investment cost, reliability and environmental impact are considered in the optimization. To overcome the drawback of conventional mathematical optimization method in arriving at local optimum solution, a genetic algorithm approach is developed, from which several possible planning schemes are generated. This is followed by a fuzzy decision analysis method to select the optimum solution scheme. Case study results on a large 220 kV transmission network planning problem are presented to show the methodology's feasibility and efficiency.

A Hybrid GA Approach for OPF with Consideration of FACTS Devices

A Hybrid GA Approach for OPF with Consideration of FACTS Devices

A Hybrid GA Approach for Optimal Control Setting Determination of UPFC

A Hybrid GA Approach for Optimal Control Setting Determination of UPFC