Emperor penguin optimizer: A bio-inspired algorithm for engineering problems
By Gaurav Dhiman, Vijay Kumar. Knowledge-Based Systems, 2018
646 citations recorded by the source corpus.
Abstract 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.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/c3d063d5b0d5c8948a78ee22ae4e7e816c43752b
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In Artificial Intelligence
In Computational Theory and Mathematics
Citations in the corpus
- Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems (2019, methodology): “Emperor Penguin Optimizer [41] EPO Collective Neurodynamic Optimization [1,42–44] CNO Artificial Chemical Reaction Optimization Algorithm [45] ACROA Galaxy-based Search Algorithm [46] GbSA Firefly Algorithm [47] FA Exchange Market Algorithm [48] EMA Social-Based Algorithm [49] SBA Harmony Search [50”
- STOA: A bio-inspired based optimization algorithm for industrial engineering problems (2019, background): “…problems (Chandrawat et al., 2017; Singh and Dhiman, 2017; Kaur and Dhiman, 2019; Singh and Dhiman, 2018b; Singh et al., 2018b,a; Kaur et al., 2018; Dhiman et al., 2018, 2019; Dhiman and Kumar, 2018) have high computational cost, nonlinear constraints, non-convex, and huge amount of solution…”
- A novel algorithm for global optimization: Rat Swarm Optimizer (2020, background): “For i world i i i i have various i i i i i , expensive, i i i i i i (A. Kaur, Jain, & Goel, 2017, 2019, n.d.; A. Kaur, 2019; H. Kaur et al., 2019; Dhiman & Kumar, 2017a; Singh & Dhiman, 2018a; Dhiman & Kumar, 2018d; Singh & Dhiman, 2018b).”