Research Area: Metaheuristic Computing
This paper proposes a novel optimization algorithm, called Emperor Penguin Optimizer (EPO), which mimics the huddling behavior of emperor penguins (Aptenodytes forsteri). The main steps of EPO are to generate the huddle boundary, compute temperature around the huddle, calculate the distance, and find the effective mover. These steps are mathematically modeled and implemented on 44 well-known benchmark test functions. It is compared with eight state-of-the-art optimization algorithms. The paper also considers for solving six real-life constrained and one unconstrained engineering design problems. The convergence and computational complexity are also analyzed to ensure the applicability of proposed algorithm. The experimental results show that the proposed algorithm is able to provide better results as compared to the other well-known metaheuristic algorithms.
Keywords:
optimization algorithm
Emperor Penguin Optimizer
huddle boundary
compute temperature
engineering design problem
computational complexity
Author(s) Name: Gaurav Dhiman, Vijay Kumar
Journal name: Knowledge-Based Systems
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.knosys.2018.06.001
Volume Information: Volume 159
