An enhanced whale optimization algorithm using the Nelder-Mead algorithm and logistic chaotic map

Farouq Zitouni, Saad Harous

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Metaheuristic algorithms are nature-inspired approaches for global optimization. They provide near-optimal solutions in a reasonable amount of time, unlike gradient-based algorithms, which are computationally intractable and greedy. We introduce a novel hybrid metaheuristic optimizer for global optimization. We combine the Nelder-Mead and the whale optimization algorithms. First, we apply the Nelder-Mead algorithm to restrict the area of the search space, minimize the search effort, and avoid stagnating in local optimums. Then, we use the linear chaotic map to control the decrementation of the values of vector a→ in the basic whale optimization algorithm. We employ the CEC 2021 benchmark test suite to evaluate the performance of the proposed hybrid optimizer. In addition, we compare our work to the original whale optimization algorithm and the chaotic whale optimization algorithm. Finally, we use the Friedman and Wilcoxon statistical tests to analyze the comparative study. The proposed algorithm shows efficient performance by comparing the considered metaheuristic algorithms.

Original languageEnglish
Title of host publicationHandbook of Whale Optimization Algorithm
Subtitle of host publicationVariants, Hybrids, Improvements, and Applications
PublisherElsevier
Pages129-137
Number of pages9
ISBN (Electronic)9780323953658
ISBN (Print)9780323953641
DOIs
Publication statusPublished - Jan 1 2023

Keywords

  • Global optimization
  • Logistic chaotic map
  • Nelder-Mead algorithm
  • Whale optimization algorithm

ASJC Scopus subject areas

  • General Computer Science

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