Book Title: Innovative Approaches in Multidisciplinary Research and Development (IAMRD)
Chief Editors: Dr. Anil Kashinath Salunke and Dr. Rituraj Pant
Associate Editors: Prof. (Dr.) Sourav Madhur Dey and Dr. Amrutha Satheesan
Co-Editors: Dr. Souvik Sur and Dr. Phakir Singh
ISBN: 978-93-7183-004-1
Chapter: 25
DOI: https://doi.org/10.59646/708/25
Author: Dr. Santosh Govindrao Bodkhe
Abstract
With the growth of large-scale interconnected networks like communication networks, transportation systems, cloud computing infrastructures and smart grids, optimization methods for allocating resources in complex networks have gained more and more importance. The advanced optimization methods play a crucial role in enhancing the efficiency, stability, and performance of complex network environments. The comparative trends of the strategies for distribution of resources are studied such as centralized, decentralized and hybrid optimization approaches. A hybrid methodology is adopted, where the quantitative performance aspects like throughput, latency and cost effectiveness is analyzed as well as qualitative aspects based on the existing algorithmic frameworks and system design study. The results suggest that user resource allocation in complex networks is very restrictive due to the scale of the network, as well as its dynamic nature, computational limitations and competing demands from users. Traditional optimization techniques, such as linear programming and convex optimization, work well in structured environments, but are not suitable in highly dynamic and/or non-linear network settings. New methods like heuristic algorithms, metaheuristic optimization methods (genetic algorithms, particle swarm optimization), reinforcement learning, and distributed optimization frameworks, however, offer greater adaptability and scalability. These methods, which optimise the performance of a network, dynamically allocate resources based on real-time changes. Moreover, the study focuses on the fact that optimization methods can be different depending on the application areas. Load balancing and cost minimisation are important in cloud computing networks while spectrum allocation and energy efficiency are very important in wireless communication networks. Likewise, in the transportation system, it’s all about congestion alleviation and route optimisation. Despite these developments, there are still some challenges, such as computational requirement, convergence and optimality vs scalability. This study shows that the optimization techniques are very important to improve the efficiency and reliability of complex networks, so that they can use the resources more efficiently while maintaining their performance under uncertainty and dynamic environment.
Keywords: Complex networks, resource allocation, optimization techniques, metaheuristic algorithms, reinforcement learning, distributed optimization, network efficiency, load balancing, cloud computing, network performance