1. Introduction

2. Deep Learning

3. Multi-Objective Optimization (MOO) Methods  
3.1. Pareto Method  
3.2. Scalarization Method  

4. Some Recent Works of Deep Learning Methods with MOO Based Parameter Optimization  
4.1. Single Models Based Architectures  
4.2. Ensemble Models Based Architectures  
4.3. Surrogate Models Based Architectures  

5. Applications  
5.1. Text Recognition  
5.2. Traffic Management  
5.3. Wind Speed Forecasting  
5.4. Mobile and Embedded Systems  
5.5. Machinery  
5.6. Environment Control  
5.7. Healthcare  
5.8. Image Processing  

6. Discussion and Conclusion