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Development of Enhanced Chimp Optimization Algorithm (OFCOA) in Cognitive Radio Networks for Energy Management and Resource Allocation

Development of Enhanced Chimp Optimization Algorithm (OFCOA) in Cognitive Radio Networks for Energy Management and Resource Allocation
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Author(s): Dilip Kumar Jang Bahadur Saini (Pimpri Chindward University, India), Anupama Mishra (Swami Rama Himalayan University, India, India), Dhirendra Siddharth (G.L. Bajaj Institute of Technology and Management, India), Pooja Joshi (Swami Rama Himalayan University, India), Ritika Bansal (Insights2Techinfo, India, & Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India, & Department of Electrical and Computer Engineering, Lebanese American University, Beirut, Lebanon), Shavi Bansal (Insights2Techinfo, India)and Kwok Tai Chui (Hong Kong Metropolitan University, Hong Kong)
Copyright: 2023
Volume: 15
Issue: 1
Pages: 20
Source title: International Journal of Software Science and Computational Intelligence (IJSSCI)
Editor(s)-in-Chief: Brij Gupta (Asia University, Taichung City, Taiwan)and Andrew W.H. Ip (University of Saskatchewan, Canada)
DOI: 10.4018/IJSSCI.335898

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Abstract

Transmit time and power optimisation increase secondary network energy efficiency (EE). The optimum resource allocation strategy in cognitive radio networks is the enhanced chimp optimisation algorithm (OFCOA) since the EE maximising problem is a nonlinear fractional programming problem. To control resources and energy, this research offers an energy-efficient CRN opposition function-based chimpanzee optimisation algorithm (OFCOA) solution. Combining the opposition function (OF) with the chimpanzee optimisation technique is recommended. OF in COAs improves decision-making. Spectrum measurements in energy management provide energy-efficient CRN operation. The suggested technique was evaluated using channel occupancy, CRN data, and four major and secondary user scenarios. CPU power, network life, transmission rate, latency, flush, power consumption, and overhead are utilized to evaluate the proposed approach in MATLAB. The proposed method is compared to existing approaches like Particle Swarm Optimisation (PSO), Chimpanzee Optimisation Algorithm (COA), and Whale Optimisation Algorithm.

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