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

18 Citations (Scopus)

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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