In cognitive radio networks (CRNs), the resource allocation is viewed as a multi-objectiveoptimisation problem in terms of limitation of quality of service, the capacity of a channel and the transmitted power. To ove...
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In cognitive radio networks (CRNs), the resource allocation is viewed as a multi-objectiveoptimisation problem in terms of limitation of quality of service, the capacity of a channel and the transmitted power. To overcome these individual issues many researchers have been undertaken, but it does not solve the multi-objective problems. In this study, the authors propose a multi-objective random walk grey wolf optimisation (MORWGWO) algorithm to enhance the resource allocation in the CRNs. Here, they combined the spectrum sensing process with the resource allocation process. Initially, an enhanced fuzzy C-means algorithm based cluster formation is proposed for spectrum sensing and then they model the resource allocation process and propose a MORWGWO algorithm for CRNs which generates the Pareto front inbetween of different objectives in a time-efficient manner. The simulation result shows that the proposed method significantly enhances the network performance in terms of delay, delivery ratio, throughput, network lifetime, energy consumption, and fairness index. Result shows that the proposed method has a better throughput of 23.22, 15.09, 6.13% for varying nodes and 27.25, 10.62, 7.09% for varying data transfer rates while comparing with the multiple objective particle swarm optimisation, non-dominated sorting genetic algorithm, and multi-objective evolutionary algorithm.
In recent years, the proportion of wind power capacity is increased dramatically for carbon emissions reduction in several countries. However, wind power generation cannot be dispatched as conventional thermal power g...
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In recent years, the proportion of wind power capacity is increased dramatically for carbon emissions reduction in several countries. However, wind power generation cannot be dispatched as conventional thermal power generation because of its randomness and intermittency. To deal with the increased uncertainty, a probabilistic model is established to analyse the uncertainty of wind power and load. Conditional value at risk index is applied to assess risk including the loss of load and spilling' wind energy associated with unpredictable imbalances between generation and load. The cost-risk model is proposed by minimising the operation cost and risk in optimal economic dispatch problem. The study uses multiple objective particle swarm optimisation to solve the model and obtain the Pareto-optimal solutions, which can reflect the relationship between risk and cost. The optimal solution can be determined in Pareto-optimal set by risk management method based on analysis of the risk marginal cost. The model and methods are tested on the IEEE 30-bus power system. The results demonstrate the proposed method can control the cost and risk in effective way.
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