Nonlinear marine predator algorithm: A cost-effective optimizer for fair power allocation in NOMA-VLC-B5G networks

Ali Safaa Sadiq, Amin Abdollahi Dehkordi, Seyedali Mirjalili, Quoc Viet Pham

Research output: Contribution to journalArticlepeer-review

Abstract

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.1

Original languageEnglish
Article number117395
JournalExpert Systems with Applications
Volume203
DOIs
Publication statusPublished - 1 Oct 2022

Keywords

  • Algorithm
  • Benchmark
  • Beyond-5G networks
  • Marin predator algorithm
  • Meta-heuristic
  • Nonlinear theory
  • Optimization
  • Visible light communications

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