Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems

By Essam H. Houssein, Mohammed R. Saad, Fatma A. Hashim, Hassan Shaban, M. Hassaballah. Engineering applications of artificial intelligence, 2020

427 citations recorded by the source corpus.

Abstract In this paper, we propose a new metaheuristic algorithm based on Levy flight called Levy flight distribution (LFD) for solving real optimization problems. The LFD algorithm is inspired from the Levy flight random walk for exploring unknown large search spaces (e.g., wireless sensor networks (WSNs). To assess the performance of the LFD algorithm, various optimization test bed problems are considered, namely the congress on evolutionary computation (CEC) 2017 suite and three engineering optimization problems: tension/compression spring, the welded beam, and pressure vessel. The statistical simulation results revealed that the LFD algorithm provides better results with superior performance in most tests compared to several well-known metaheuristic algorithms such as simulated annealing (SA), differential evolution (DE), particle swarm optimization (PSO), elephant herding optimization (EHO), the genetic algorithm (GA), moth-flame optimization algorithm (MFO), whale optimization algorithm (WOA), grasshopper optimization algorithm (GOA), and Harris Hawks Optimization (HHO) algorithm. Furthermore, the performance of the LFD algorithm is tested on other different optimization problems of unknown large search spaces such as the area coverage problem in WSNs. The LFD algorithm shows high performance in providing a good deployment schema than energy-efficient connected dominating set (EECDS), A3, and CDS-Rule K topology construction algorithms for solving the area coverage problem in WSNs. Eventually, the LFD algorithm performs successfully achieving a high coverage rate up to 43.16 %, while the A3, EECDS, and CDS-Rule K algorithms achieve low coverage rates up to 40 % based on network sizes used in the simulation experiments. Also, the LFD algorithm succeeded in providing a better deployment schema than A3, EECDS, and CDS-Rule K algorithms and enhancing the detection capability of WSNs by minimizing the overlap between sensor nodes and maximizing the coverage rate. The source code is currently available for public from: https://www.mathworks.com/matlabcentral/fileexchange/76103-lfd .

Subfields: Artificial Intelligence, Computer Networks and Communications, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a6c8c8b80f3bb23b51c789e6b3a7933770c9d174

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Essam H. Houssein
  2. Fatma A. Hashim
  3. Mai S. Mabrouk
  4. Walid Al-Atabany
  5. Kashif Hussain
  6. Pavel Trojovský
  7. Mohammad Dehghani

In Computer Networks and Communications

  1. Essam H. Houssein
  2. Fatma A. Hashim
  3. Mai S. Mabrouk
  4. Kashif Hussain
  5. Walid Al-Atabany

In Molecular Biology

  1. Essam H. Houssein
  2. Kashif Hussain

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