Reverse Design of Photonic Devices Based on a Hybrid Particle Swarm Algorithm

Fig. 3. Comparison curves of fitness function on srPSO, PSO, and genetic algorithm. (a) Ackley; (b) Griewank; (c) Rastrigin; (d) Rosenbrock; (e) Schaffer

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Fig. 4. Hybrid algorithm and PSO algorithm iteration graph

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Fig. 5. Splitter insertion loss

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Fig. 6. Electric field distribution and structure model of Y-branch

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Table 1. Fitness functions

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Table 1. Fitness functions

Function	Formula	Optimal	Range
Ackley	$f_{1} = - 20 e x p (- 0.2 \sqrt[]{\frac{1}{n} \sum_{i = 1}^{n} x_{i}^{2}}) - e x p [\frac{1}{n} \sum_{i = 1}^{n} c o s (2 π x_{i}^{2})] + 20 + e$	0	［-30，30］
Griewank	$f_{2} = \frac{1}{4000} \sum_{i = 1}^{n} x_{i}^{2} - \overset{n}{\prod_{i = 1}} c o s (\frac{x_{i}}{\sqrt[]{i}}) + 1$	0	［-600，600］
Rastrigin	$f_{3} = \sum_{i = 1}^{n} [x_{i}^{2} - 10 c o s (2 π x_{i}) + 10]$	0	［-100，100］
Rosenbrock	$f_{4} = \sum_{i = 1}^{n - 1} [100 {(x_{i + 1} - x^{2})}^{2} + {(x_{i} - 1)}^{2}]$	0	［-100，100］
Schaffer	$f_{5} = 0.5 - \frac{(s i n \sqrt[]{x^{2} + y^{2}}) - 0.5}{{[1 + 0.001 (x^{2} + y^{2})]}^{2}}$	1	［-5，5］

Table 2. Algorithm parameters

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Table 2. Algorithm parameters

Name of algorithm	Inertia weight ω	Self-cognition factor C₁	Social-cognition factor C₂	Disturbance threshold T₁	Disturbance threshold T₂
PSO algorithm	1	2	2	-	-
Hybrid algorithm	［1.2，0.6］	［0.6，1.2］	［1，0.4］	3	3

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Yinghan Li, Lü Jie, Lin Jiang, Linghao Cheng. Reverse Design of Photonic Devices Based on a Hybrid Particle Swarm Algorithm[J]. Laser & Optoelectronics Progress, 2022, 59(11): 1113001

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Paper Information

Category: Integrated Optics

Received: Jun. 22, 2021

Accepted: Jul. 13, 2021

Published Online: Jun. 9, 2022

The Author Email: Yinghan Li (axx_lxx@qq.com), Linghao Cheng (chenglh@ieee.org)

DOI:10.3788/LOP202259.1113001

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology