Nature-Inspired Metaheuristic Optimization for Network Design and

Communication Systems: Trends, Applications, and Future Directions

Hussein Ibrahim Hussein1,2,*, Lateef Abd Zaid Qudr1, Weal Hasan Ali Almohammed3

1 Department of Computer Engineering Techniques, Alsafwa University College, Karbala, Iraq

2 Department of Information Security, College of Information Technology, University of Babylon, Hillah, Iraq

3 Department of Computer Science, College of Computer Science and IT, University of Kerbala, Iraq

Abstract

Communication network design (CND) and wireless sensor networks (WSNs) presents significant challenges, particularly in

optimizing network reliability, energy efficiency, and cost-effectiveness. This literature review discusses using metaheuristic

algorithms to solve the mentioned NP-hard problems and provide accurate results for network reliability, resource management

assignment, and energy utilization in data transmission networks. Recent advancements in hybrid metaheuristic approaches,

such as combining Genetic Algorithms with Branch and Bound (GA-BB) or Particle Swarm Optimization with Simulated

Annealing (PSO-SA), demonstrate their effectiveness in optimizing network performance in emerging domains like vehicular

ad-hoc networks (VANETs) and Internet of Things (IoT)- enabled WSNs. The review also discusses the application of

optimization approaches about distinct issues such as cluster head selection in WSNs, routing protocols in dynamic networks,

and supply chain network design. These developments are essential in evolving technologies such as 6G networks and the

Internet of Everything (IoE), where complex systems demand innovative optimization strategies. By highlighting these concerns

in the current study, this review calls for the increased use of metaheuristic techniques towards furthering the application of

future networks in smart cities, healthcare, and secure network architectures.

Keywords: Metaheuristic algorithms; Wireless Sensor Networks; Communication Network Design; Energy efficiency; Network

reliability

1. Introduction