Diversity-Driven Evolutionary Algorithms For Solving Engineering Problems

Chapter 1: Introduction        

1.1          Introduction to optimization 

1.2          Single objective optimization

1.3          Multi-objective optimization

1.4          Constrained handling methods            

1.5          Classification of metaheuristic optimization techniques     

1.1          Importance of exploration and exploitation         

1.6          Application areas for optimization technique      

1.7          Significance of the work       

1.8          Scope of research  

1.9          Organisation of the book      

Chapter 2: Diversity-driven multi-parent evolutionary algorithm       

2.1          Introduction         

2.2          Proposed algorithm

2.3          Result and Discussion          

2.4          Conclusion           

Chapter 3: Diversity-driven multi-parent evolutionary algorithm with different mutation 

3.1          DDMPEA algorithm with a different mutation strategy      

3.2          Numerical benchmark results and analyses         

3.3          Statistical analysis of benchmark functions         

3.4          Conclusion           

Chapter 4: Diversity-driven multi-parent evolutionary algorithm for digital filter design   

4.1          Introduction         

4.2          Case I: Introduction to two-channel quadrature mirror filter bank      

4.3          Background to the two-channel quadrature mirror filter bank            

4.4          DDMPEA-ANUM for Two-Channel QMF Bank Design    

4.5          Simulation results 

4.6          Introduction to IIR filters      

4.7          The problem formulation of the Digital IIR filter 

4.8          Solution framework             

4.9          Results from designing a digital IIR filter           

4.10        Conclusion           

Chapter 5: Diversity-driven multi-parent evolutionary algorithm in fault diagnosis          

5.1          Introduction         

5.2          Case I: An effective health indicator for bearing using corrected conditional entropy through diversity-driven multi-parent evolutionary algorithm           

5.3          Case II: Bearing defect identification via diversity-driven multi-parent evolutionary algorithm with adaptive wavelet mutation strategy            

5.3.1       Theoretical background

5.3.2       Proposed methodology for fault detection          

5.3.3       Result and discussion           

5.3.4       Conclusion for Case II          

Chapter 6: Application of diversity-driven multi-parent evolutionary algorithm in WSN  

6.1          Diversity-driven multi-parent Evolutionary algorithm-based cluster head selection in heterogeneous WSN         

6.2          Diversity-driven multi-parent evolutionary algorithm with adaptive non-uniform mutation

6.3          Problem formulation for cluster-head selection in heterogeneous wireless sensor networks

6.4          Implementation    

6.5          Results and discussion         

6.6          Summarized analysis of the proposed protocol    

6.7          Conclusion           

Chapter 7: Conclusions and Scope for Future Research      

7.1          Development of a new evolutionary-based optimization algorithm    

7.2          Optimum CH selection and Fuzzy-based clustering in HWSN & area coverage optimization using DDMPEA-ANUM 

7.3          Two-channel QMF bank design and digital IIR filter design using DDMPEA-ANUM  

7.4          Automatic fault identification of bearing using DDMPEA. 

7.5          Scope for Future Research  

Biography

Sumika Chauhan is currently Visiting Professor and member of the Digital Mining Center of Wroclaw University of Science and Technology, Wroclaw, Poland.  She received her PhD degree in Electrical and Instrumentation from the Sant Longowal Institute of Engineering and Technology, Longowal, India, in 2023. She has authored over 70 research papers in Science Citation Index (SCI) journals and is also serving as Associate Editor in reputed journals. Her current research includes optimization, filter design, fault diagnosis of mechanical components, vibration and acoustic signal processing, identification/measurement, defect prognosis, machine learning and artificial intelligence.