TY - CHAP
T1 - Grey Wolf Optimizer, Whale Optimization Algorithm, and Moth Flame Optimization for Optimizing Photonics Crystals
AU - Mirjalili, Seyed Mohammad
AU - Mirjalili, Seyedeh Zahra
AU - Khodadadi, Nima
AU - Snasel, Vaclav
AU - Mirjalili, Seyedali
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - In this chapter, three recent swarm intelligence algorithms are used to solve a challenging optimization problem in the field of photonics, including Grey Wolf Optimizer, Whale Optimization Algorithm, and Moth Flame Optimization Algorithm. The problem is to optimize the radii of several rods in a photonics crystal to minimize light wave loss when there is a bend corner. This problem is first presented and formulated in details. It is discussed that due to the use of complex simulations, analytics equations are ill-defined for this problem thereby justifying the use of black-box optimization algorithms. The above-mentioned algorithms are then employed to estimate the global optimal for this problem by finding the optimal values for its structural parameters. The results show that the GWO algorithm provides the best results. The chapter also considers a convergence analysis of all algorithms that led to interesting insights about the process of solution improved during the course of optimization. It is observed that GWO shows constant improvement while others tend to show steady and slow improvement.
AB - In this chapter, three recent swarm intelligence algorithms are used to solve a challenging optimization problem in the field of photonics, including Grey Wolf Optimizer, Whale Optimization Algorithm, and Moth Flame Optimization Algorithm. The problem is to optimize the radii of several rods in a photonics crystal to minimize light wave loss when there is a bend corner. This problem is first presented and formulated in details. It is discussed that due to the use of complex simulations, analytics equations are ill-defined for this problem thereby justifying the use of black-box optimization algorithms. The above-mentioned algorithms are then employed to estimate the global optimal for this problem by finding the optimal values for its structural parameters. The results show that the GWO algorithm provides the best results. The chapter also considers a convergence analysis of all algorithms that led to interesting insights about the process of solution improved during the course of optimization. It is observed that GWO shows constant improvement while others tend to show steady and slow improvement.
KW - Grey Wolf optimizer
KW - Moth flame optimization algorithm
KW - Optimization
KW - Photonic crystal
KW - Whale optimization algorithm
UR - http://www.scopus.com/inward/record.url?scp=85139399922&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-09835-2_9
DO - 10.1007/978-3-031-09835-2_9
M3 - Chapter
AN - SCOPUS:85139399922
T3 - Studies in Computational Intelligence
SP - 169
EP - 179
BT - Studies in Computational Intelligence
PB - Springer Science and Business Media Deutschland GmbH
ER -