The book provides a collection of recent applications of nature inspired optimization in industrial fields. Different optimization techniques have been deployed, and different problems have been effectively analyzed. The valuable contributions from researchers focus on three ultimate goals (i) improving the accuracy of these techniques, (ii) achieving higher speed and lower computational complexity, and (iii) working on their proposed applications. The book is helpful for active researchers and practitioners in the field.
Author(s): Mahdi Khosravy, Neeraj Gupta, Nilesh Patel
Series: Springer Tracts in Nature-Inspired Computing
Publisher: Springer
Year: 2021
Language: English
Pages: 244
City: Singapore
Preface
Contents
Editors and Contributors
1 Association Rules over Time
1 Introduction
2 Basic Information
2.1 Association Rule Mining
2.2 Differential Evolution
2.3 Sankey Diagrams
2.4 Formal Definition of the Objectives
3 Method for Creating the Sankey Diagrams from the ARM Archive
3.1 Preprocessing
3.2 Optimization
3.3 Visualization
4 Experiments and Results
4.1 Transaction Database
4.2 The Results
4.3 Discussion
5 Conclusion
References
2 A Study of Crossover Operators in Genetic Algorithms
1 Introduction
2 Crossover Operator
3 Crossover Operators for Binary Encoding
3.1 Single-Point Crossover
3.2 k-point Crossover
3.3 Uniform Crossover
3.4 Discrete Crossover
3.5 Shuffle Crossover
3.6 Segmented Crossover
4 Crossover Operators for Real-Coded Genetic Algorithms
4.1 Arithmetic Crossover
4.2 Linear Crossover
4.3 Blend Crossover
4.4 Simulated Binary Crossover (SBX)
4.5 Partially Mapped Crossover Operator (PMX)
5 Other Crossover Operators
5.1 Three-Parent Crossover
5.2 One-Parent Crossover
6 Popular Design Choices
7 Conclusion
References
3 Memetic Strategies for Network Design Problems
1 Introduction
2 Related Work
3 Memetic Strategies
3.1 Solution Encoding/Decoding
3.2 Initial Solution Construction
3.3 Local Search
3.4 Crossover and Mutation
3.5 Population Management
4 Computational Experiments
5 Concluding Remarks
References
4 A Coronavirus Optimization Algorithm for Solving the Container Retrieval Problem
1 Introduction
2 Related Work
3 The CRP Definition
3.1 The Crane's Operating Time
4 The Coronavirus Optimization Algorithm
4.1 Implementation Details
4.2 Generating the Patient Zero
4.3 The Death of Infected Individuals
4.4 The Coronavirus Infection
4.5 Registering Newly Infected Individuals
5 Computational Experiments
5.1 Analysis of CVOA
6 Conclusion
References
5 Optimum Outlier Detection in Internet of Things Industries Using Autoencoder
1 Introduction
2 Related Works
3 Methodology
3.1 Neural Networks
3.2 Internet of Things
3.3 Outlier Detection
4 The Proposed Algorithm
4.1 Autoencoder Neural Network
4.2 Data Modeling for Outlier Detection Problem
4.3 Datasets
5 Experimental Results
6 Conclusion and the Future Work
References
6 Particle Swarm Optimization Advances in Internet of Things Industry
1 Introduction
2 Particle Swarm Optimization (PSO) Algorithm
3 Internet of Things (IoT)
4 Recent PSO Applications in IoT
4.1 Business Workflow Applications in Cloud-Edge Environments
4.2 Virtual Machines Scheduling in Cloud-IoT Environments
4.3 Optimal Parking Lot Selection Based on IoT
4.4 Observation and Prediction of CKD and its Level of Severity
4.5 Botnets Detection in IoT Environments
4.6 Multicasting Optimization in IoT-Based WSN
4.7 Shortcut Addition Strategy for Multi-sink IoT
5 Conclusions
References
7 Reconfiguration of Electric Power Distribution Networks: A Typical Application of Metaheuristics in Electrical Power Field
1 Introduction
2 Overview of Distribution Network and Its Operations
2.1 Service Restoration
2.2 Service Restoration with Distributed Generation Systems
2.3 Example of Actual-Scale Distribution Network Models
3 Traditional Problem Frameworks of Distribution Network Reconfiguration
3.1 Traditional Distribution Loss Minimizing Problem
3.2 Traditional Service Restoration Problem
4 Examples of Solution Methods
4.1 Enumeration-Based Solution Method
4.2 Tabu Search-Based Solution Method
4.3 Numerical Simulations
5 Examples of Extended Frameworks of Service Restoration Problem
5.1 Application of Robust Optimization
5.2 Application of Two-Stage Stochastic Programming
5.3 Solution Methods of Robust Optimization or Two-Stage Stochastic Programming Problems
5.4 Validations of Extended Problem Frameworks
6 Conclusions
References
8 Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection
1 Introduction
1.1 Main Contributions
1.2 Organization
2 Background and Related Works
3 Proposed Methodology for Slopes and Dams Inspection
3.1 Problem Formulation
3.2 Dynamic Coverage Path Planning
4 Results and Discussions
5 Conclusions and Future Work
References
9 Multi-objective Optimization and Decision-Making for Net-Zero Energy Smart House
1 Introduction
2 Stand-Alone ZESH Model
2.1 Load Characteristics
2.2 PV, SC, and HP System Model
2.3 Vehicle Operation and Energy Transportation by EV
3 Formulation of Optimization Problem
3.1 Objective Function
3.2 Constraints
4 A Preference and Decision-Making with IoT
4.1 Comfort Index
4.2 Decision-Making by Learning and Network
4.3 IoT Design for ZESH
5 Simulation Results and Discussion
5.1 Result of Multi-objective Optimization (Case 1)
5.2 Results on Decision-Making by Comfort Index and DQN (Case 2)
6 Conclusion
References
10 Using Fuzzy Approach in Determining Critical Parameters for Optimum Safety Functions in Mega Projects (Case Study: Iran’s Construction Industry)
1 Introduction
2 Literature Review
3 Materials and Method
3.1 Data Collection Method, Sample, Population, and Data Analysis Method
3.2 Fuzzy AHP Technique
4 Finding, Results, and Discussion
5 Conclusion
References
11 Optimum Lightweight AI End Device for Health Monitoring of Agriculture Vehicles
1 Introduction
2 The Conventional Manner of Health Monitoring of Agriculture Machinery
3 The Proposed Strategy of the Agriculture Machinery Health Monitoring
4 IoT-Specific Agriculture Machinery Health Monitoring
5 Machine Learning Model Optimization
6 Related Works on Lightweight AI for Health Monitoring of Agriculture Vehicles
6.1 Sound-Responsive Health Diagnosis Edge Computing Device for the Agriculture Machinery
6.2 Parallel Evolving Artificial Neural Networks by Genetic Algorithm
7 Conclusion
References
12 Evolutionary Machine Learning Powered by Genetics Algorithm for IoT-Specific Health Monitoring of Agriculture Vehicles
1 Introduction
2 Current Oil Filter Monitoring Systems
3 Laboratory Settings
3.1 Features
4 Genetic Algorithm (GA)
4.1 Chromosome Representation
4.2 Population Generation
4.3 Parent Selection for Crossover
4.4 Crossover Operation
4.5 Mutation Operation
4.6 Offspring Selection for New Population
5 ANN Trained with Scaled Conjugate Gradient Backpropagation
6 Objective Function
7 Simulation Results
8 Conclusion
References
