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Status: Unchecked ×Keyword: CEC2022 benchmark functions ×Clear all

2 claims from 1 paper

1. Computer Science › Metaheuristic Optimization Algorithms Research

   Ameliorated elk herd optimizer for global optimization and engineering problems

   Al‐Betar, Braik, Shambour, Al‐Naymat and Porntaveetus · Artificial Intelligence Review · 2025

   The authors improve the elk herd optimiser by adding particle swarm memory and greedy selection, then test it on benchmark suites, four engineering design problems and one industrial process.

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   1. UncheckedThe improved elk herd optimiser (AEHO) performed best on 84% of CEC2014 and 74% of CEC2022 test functions, ranking first in both suites.“Based on the analysis of the experimental findings, AEHO performed optimally on 84% of the CEC2014 functions and 74% of the CEC2022 functions, ranking first in both suites with an average ranking of 3.11 and 1.62, respectively.”
   2. UncheckedThe paper reports that AEHO's mean computation time is about a third of that of the first-ranked method, suggesting greater search efficiency than newer algorithms.“The mean computation time of AEHO is about one-third of the average computation time for the first-ranked method, indicating that AEHO not only performs very well in global searches but also exhibits greater search efficiency when compared to newer optimization algorithms.”