Research Area: Metaheuristic Computing
This paper is an influential attempt to identify and alleviate some of the issues with the recently proposed optimization technique called the Marine Predator Algorithm (MPA). With a visual investigation of its exploratory and exploitative behavior, it is observed that the transition of search from being global to local can be further improved. As an extremely cost-effective method, a set of nonlinear functions is used to change the search patterns of the MPA algorithm. The proposed algorithm, called Nonlinear Marin Predator Algorithm (NMPA), is tested on a set of benchmark functions. A comprehensive comparative study shows the superiority of the proposed method compared to the original MPA and even other recent meta-heuristics. The paper also considers solving a real-world case study around power allocation in non-orthogonal multiple access (NOMA) and visible light communications (VLC) for Beyond 5G (B5G) networks to showcase the applicability of the NMPA algorithm. NMPA algorithm also shows its superiority in solving a wide range of benchmark functions as well as obtaining fair power allocation for multiple users in NOMA-VLC-B5G systems compared with the state-of-the-art algorithms.
Keywords:
Meta-heuristic
Optimization
Beyond-5G networks
Marin predator algorithm
Nonlinear theory
Algorithm
Benchmark
Visible light communications
Author(s) Name: Ali Safaa Sadiq, Amin Abdollahi Dehkordi, Seyedali Mirjalili, Quoc-Viet Pham
Journal name: Expert Systems with Applications
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.eswa.2022.117395
Volume Information: Volume 203, 1 October 2022, 117395
