Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm

By Seyed Mohammad Mirjalili. Knowledge-Based Systems, 2015

4,197 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/31a177752c50f9a635d06fe1da03eff481e481e3

How researchers see this paper

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

In Artificial Intelligence

1. Yaochu Jin
2. Xing-yi Zhang
3. Ye Tian
4. Ran Cheng
5. Tansel Dökeroğlu
6. Ali Asghar Heidari
7. Huiling Chen
8. Gaurav Dhiman

In Computational Theory and Mathematics

1. Yaochu Jin
2. Xing-yi Zhang
3. Ye Tian
4. Ran Cheng
5. Tansel Dökeroğlu
6. Ali Asghar Heidari
7. Gaurav Dhiman
8. Vijay Kumar

In Ocean Engineering

1. Hassan A. AlSattar
2. Ayça Deniz
3. Bo-Seok Yang
4. Mohammed Azmi Al-Betar
5. Atulya Kumar Nagar
6. Marcin Wozniak
7. A. A. Zaidan
8. Vijay Kumar

Citations in the corpus

• RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method (2021, methodology): “The mathematical formulation (i.e., objective function and constraints) of the problem is detailed in Appendix A. (Gandomi, et al., 2013) HGSO (Hashim, et al., 2019) GWO (Hashim, et al., 2019) WOA (Hashim, et al The results of RUN were compared with those of MVO (Mirjalili, et al., 2016), grasshoppe”
• The Arithmetic Optimization Algorithm (2021, neutral): “…(BBO) [34] • Flower Pollination Algorithm (FPA) [35] • Grey Wolf Optimizer (GWO) [36] • Bat Algorithm (BAT) [37] • Firefly Algorithm (FA) [38] • Cuckoo Search Algorithm (CS) [39] • Moth-Flame Optimization (MFO) [40] • Gravitational Search Algorithm (GSA) [25] • Differential Evolution (DE) [16].”
• INFO: An efficient optimization algorithm based on weighted mean of vectors (2022, methodology): “= 1 √2𝐴2+𝐴1 𝑃 − 𝜎 ≤ 0, 0 ≤ 𝐴1, 𝐴2 ≤ 1, 𝑙 = 100 𝑐𝑚, 𝑃 = 2 𝑘𝑁 𝑐𝑚2 , 𝜎 = 2 𝑘𝑁 𝑐𝑚2 The problem was optimized using several meta-heuristic algorithms, such as mothflame optimization (MFO) [79], ant lion optimizer (ALO) [77], cuckoo search (CS) [76], differential evolution with dynamic ”
• Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts (2021, methodology): “…et al., 2017), GWO (Mirjalili, et al., 2014), MFO (S. Mirjalili, 2015), WOA (Mirjalili & Lewis, 2016), GOA (Saremi, et al., 2017), DA (Seyedali Mirjalili, 2015b), ALO (Seyedali Mirjalili, 2015a), PSO (Kennedy & Eberhart, 1995), DE (G. Sun, Yang, Yang, & Xu, 2019), BA, and FPA (X.-S. Yang, et…”