Addressing Resource Allocation Challenges in Wireless Networks with Cultural Smell Agent Optimization Algorithms: A Literature Survey

• Aliu S. SALIU

  National Space Research and Development Agency (NASRDA), Abuja, Nigeria

  Author

• Ejike C. ANENE

  Department of Electrical and Electronic Engineering, Abubakar Tafawa Balewa University, Bauchi, Nigeria

  Author

• Buhari M. HASSAN

  Department of Electrical and Electronic Engineering, Abubakar Tafawa Balewa University, Bauchi, Nigeria

  Author

• Sabo M. HASSAN

  Author

• Salisu H. MOHAMMED

  Author

• Nafisa S. USMAN

  Department of Computer Engineering, Ahmadu Bello University, Zaria, Kaduna, Nigeria

  Author

The increasing difficulty and evolution of new wireless networks, specifically with the arrival of 5G technologies, require enhanced resource allocation strategies, that can adapt well to changing conditions. Established optimization methods often lack the capacity to address the scalability, flexibility, and real-time needs of such environments. This paper particularly reviews present optimization algorithms, highlighting evolutionary and swarm intelligence techniques, with specific stress on Cultural Algorithms (CAs) and Smell Agent Optimization (SAO). To take advantage of their varied strengths, it assesses the idea of merging the CA and SAO approaches into one algorithm. Although the Smell Agent Optimization emphasizes evaluating nearby options, premised on swarm behavior; using stored knowledge, Cultural Algorithms provide general guidance. Through examining previous research gaps, limitations, and the challenges faced, in wireless resource allocation, this paper advocates a new culturally motivated smell agent algorithm, aimed at improving adaptability, efficiency, and performance, in wireless networks. The proposed approach will offer strong solutions for evolving large-scale wireless environments, as well as promising to address scalability issues and improve convergence rates This work presents a foundational basis for future research in integrating nature-inspired metaheuristics to optimize resource management in next-generation wireless systems.

• FJET_12_75_68

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