Design and Applications of Nature Inspired Optimization : Contribution of Women Leaders in the Field

This book gives a detailed information of various real-life applications from various fields using nature inspired optimization techniques. These techniques are proven to be efficient and robust in many difficult problems in literature. The authors provide detailed information about real-life problems and how various nature inspired optimizations are applied to solve these problems. The authors discuss techniques such as Biogeography Based Optimization, Glow Swarm Optimization, Elephant herd Optimization Algorithm, Cuckoo Search Algorithm, Ant Colony Optimization, and Grey Wolf Optimization etc. These algorithms are applied to a wide range of problems from the field of engineering, finance, medicinal etc. As an important part of the Women in Science and Engineering book series, the work highlights the contribution of women leaders in nature inspired optimization, inspiring women and men, girls and boys to enter and apply themselves to the field. Provides a detailed overview of nature inspired optimization techniques from prominent women in the field; Presents well-defined variables and objective functions and their constraints of various real-life problems in nature inspired optimization; Includes methodology and criteria for presentation of the solutions obtained Preface Contents Contributors Chapter 1: An Overview of Swarm Intelligence-Based Algorithms 1 Introduction 2 Characteristics of SI-Based Algorithms 3 Particle Swarm Optimization Algorithm Pseudocode of PSO Minimizing Rosenbrock Function Using PSO 4 Artificial Bee Colony Algorithm Minimizing Schwefel ́s Function Using Artificial Bee Colony Algorithm 5 Comparative Analysis Using Fixed Iteration Test 6 Conclusion References Chapter 2: Particle Swarm Optimization and Its Applications in the Manufacturing Industry 1 Introduction to Optimization Local and Global Optimal Solution Nature-Inspired Algorithms 2 Particle Swarm Optimization How PSO Works Understanding PSO Parameters Binary Particle Swarm Optimization Research Developments in PSO 3 Application of PSO Manufacturing Industry 4 Conclusion References Chapter 3: Role of Machine Learning in Bioprocess Engineering: Current Perspectives and Future Directions 1 Introduction 2 Approaches of Machine Learning in Bioprocess Engineering 3 Why Machine Learning Strategies Are Needed in Bioprocess Engineering 4 Applications of Machine Learning in Bioprocess Engineering (Case Studies) Approaches of Machine Learning in Biorefinery: A Case Study Approaches of Machine Learning in Monoclonal Antibody Production: A Case Study Approaches of Machine Learning for Antibiotic Production: A Case Study Machine Learning in Protein Engineering: A Case Study 5 Current Challenges and Future Prospects 6 Conclusion Glossary References Chapter 4: Advanced Selection Operation for Differential Evolution Algorithm 1 Introduction 2 Basic Differential Evolution (DE) 3 Proposed Modification Advance Selection Strategy Proposed DERLaS and MRLDEaS 4 Experimental Settings Test Functions Performance Criteria Parameter Setting 5 Result and Discussion Result on Benchmark Problems Result on Real-Life Application Convergence Graphs 6 Conclusions References Chapter 5: Profit Optimization of Two-Unit Briquetting System Using Grey Wolf Optimization Algorithm 1 Introduction 2 Introduction 3 State Transition Diagram 4 Transition Probabilities and Mean Sojourn Periods 5 System Effectiveness Measures Mean Time to System Failure System Availability Busy Period Expected Visits by Repairmen Profit 6 Grey Wolf Optimizer 7 Graphical Results and Discussion 8 Conclusions References Chapter 6: Solving Portfolio Optimization Using Sine-Cosine Algorithm Embedded Mutation Operations 1 Introduction 2 Markowitz Model Based on Historical Stock Price Data Markowitz Mean-Variance Model Rate of Return Expected Return Variance Portfolio Formulation Statement of the Problem 3 Problem Description Problem 1 Problem 2 4 Sine-Cosine Algorithm Mutation Power Mutation Polynomial Mutation Random Mutation Gaussian Mutation Cauchy Mutation 5 Numerical Analysis of Results Obtained by the Proposed Version of SCA Problem 1 Problem 2 6 Result Analysis 7 Conclusion References Chapter 7: Detecting Group Shilling Profiles in Recommender Systems: A Hybrid Clustering and Grey Wolf Optimizer Technique 1 Introduction 2 Related Work and Motivation 3 Shilling Attacks 4 Grey Wolf Optimizer (GWO) Motivation Description and Algorithm 5 GWODS Motivation Proposed Approach 6 Experiments and Results Dataset and Experimental Setup Parameter Setting Evaluation Metrics Experiments and Results Comparison of Binary Operators Result Analysis Comparison of GWODS with State-of-the-Art Approaches 7 Conclusion and Future Work References Chapter 8: Single Image Reflection Removal Using Deep Learning 1 Introduction 2 Literature Survey Multi-image Methods Single Input Methods Traditional Approaches Learning-Based Approaches 3 Proposed Method Training Dataset Model Description (Table 8.1) Loss Function 4 Experiment and Results Training Details Experimental Set-Up Performance Evaluation Metrics Testing Dataset 5 Conclusion and Future Work References Chapter 9: Social Media Analysis: A Tool for Popularity Prediction Using Machine Learning Classifiers 1 Introduction 2 Related Works 3 Proposed Methodology Problem Identification Data Gathering Data Filtering Fetching Features Classification Using ML Classifier Comparative Study of Different Models Implementing Tools Python Jupyter Notebook Statistical NLP, Machine Learning, and Deep Learning Application of NLP 4 Result and Discussion Real-Time Applications Experimental Validation and Accuracy 5 Conclusion and Future Scope 6 Challenges and Limitations References Appendix Benchmark Problems Real-Life Applications RF1: Parameter Estimation for Frequency-Modulated (FM) Sound Waves RF2: Optimal Thermohydraulic Performance of an Artificially Roughened Air Heater RF3: Spread-Spectrum Radar Polyphase Code Design References Index