The introduction of nature-inspired optimization algorithms (NIOAs), over the past three decades, helped solve nonlinear, high-dimensional, and complex computational optimization problems. NIOAs have been originally developed to overcome the challenges of global optimization problems such as nonlinearity, non-convexity, non-continuity, non-differentiability, and/or multimodality which traditional numerical optimization techniques had difficulties solving.
The main objective for this book is to make available a self-contained collection of modern research addressing the general bound-constrained optimization problems in many real-world applications using nature-inspired optimization algorithms. This book is suitable for a graduate class on optimization, but will also be useful for interested senior students working on their research projects.
Author(s): Ali Mohamed, Diego Oliva, Ponnuthurai Nagaratnam Suganthan
Series: Studies in Systems, Decision and Control, 212
Publisher: Springer
Year: 2022
Language: English
Pages: 281
City: Cham
Preface
Contents
Chaotic-SCA Salp Swarm Algorithm Enhanced with Opposition Based Learning: Application to Decrease Carbon Footprint in Patient Flow
1 Introduction
2 Preliminaries
2.1 Salp Swarm Algorithm
2.2 Sine Cosine Algorithm for Updating the Leader’s Position
2.3 Chaotic Maps for Updating the Followers’ Position
2.4 Opposition Based Learning
3 Proposed Algorithm
4 Experimental Results of CSOSSA and Discussion
5 Comparison of CSOSSA with Other Algorithms on De Jong's Functions
5.1 De Jong's Functions
5.2 Algorithms for Comparison
5.3 Experimental Results on De Jong's Function
6 Practical Problem: Carbon Footprint Minimization
6.1 Mathematical Formulation to Calculate CFP
6.2 Solution Representation
6.3 Experimental Results of CFP Minimization
7 Conclusions and Future Insights
Appendix
References
Design and Performance Evaluation of Objective Functions Based on Various Measures of Fuzzy Entropies for Image Segmentation Using Grey Wolf Optimization
1 Introduction
2 Problem Formulation Based on Shannon and Non-Shannon Fuzzy Entropies
2.1 Shannon Measure of Entropy
2.2 Havrda-Charvat Measure of Entropy
2.3 Renyi Measure of Entropy
2.4 Kapur Measure of Entropy
2.5 M. Masi Measure of Entropy
3 Grey Wolf Optimization (GWO)
3.1 Simulation of Social Leadership Hierarchy of Grey Wolves
3.2 Simulation of Strategy of Grey Wolves for Encircling of Prey
3.3 Simulation of Strategy of Grey Wolves for Hunting of Prey
3.4 General GWO Algorithm
4 Proposed Approach
4.1 GWO Based Image Segmentation Algorithm
4.2 Procedure for Computing Fitness of Search Agents of Population Using Fuzzy Shannon and Non-Shannon Entropies
5 Experimental Results and Performance Analysis
6 Conclusions
References
Improved Artificial Bee Colony Algorithm with Adaptive Pursuit Based Strategy Selection
1 Introduction
2 The Background
2.1 Single Objective Bound-Constrained Real-Parameter Numerical Optimization
2.2 Original ABC Algorithm
2.3 Improved ABC Algorithm
2.4 Adaptive Pursuit
3 The Proposed Adaptive Pursuit Based IABC Algorithm
4 Experimental Work
4.1 Experimental Setting
4.2 Numerical Results
4.3 Comparison and Discussion
5 Conclusion
References
Beetle Antennae Search Algorithm for the Motion Planning of Industrial Manipulator
1 Introduction
2 Problem Formulation
2.1 Kinematics Model
2.2 Joint Constraints
3 Beetle Antennae Search Application
3.1 Searching Design
3.2 State Update
3.3 Exploration Range and Step Size
4 Experiment Results
4.1 Implementation on Kuka Industrial Manipulator
4.2 Evaluation of Different Exploration Parameters
5 Conclusion
References
Solving Optimal Power Flow with Considering Placement of TCSC and FACTS Cost Using Cuckoo Search Algorithm
1 Introduction
2 Problem Formulation
2.1 Economic Load Dispatch
2.2 Optimal Power Flow Dispatch
2.3 Optimal Power Flow with FACTS Devices Cost (TCSC)
3 Cuckoo-Inspired Metaheuristics
3.1 Cuckoo Search Algorithm (CSA)
3.2 Cuckoo Search (CS) Strategy
4 Simulation Results and Discussion
4.1 Case I: OPF Without the Installation of TCSC
4.2 Case II: OPF with the Presence of TCSC
5 Conclusion
Appendix 1: List of Abbreviations and Acronyms
Appendix 2: List of Mathematical Symbols
References
Parameter Estimation of Per-Unit Photovoltaic Models Using Optimization Algorithms: Comparative Study
1 Introduction
2 Per-Unit Mathematical Models
2.1 Per-Unit Single-Diode Model
2.2 Per-Unit Double-Diode Model
2.3 Per-Unit Three-Diode Model
3 Problem Formulation and Metaheuristic Methods
3.1 Objective Function
3.2 Gaining-Sharing Knowledge
3.3 Differential Evolution
3.4 Flower Pollination Algorithm
3.5 Particle Swarm Optimization
3.6 Grey Wolf Optimizer
4 Photovoltaic Parameter Estimation: Results and Discussion
4.1 Case Study 1: Standard Photovoltaic Cell
4.2 Case Study 2: Standard Photovoltaic Module
5 Result Comparison
6 Conclusions
References
Space–Time Concept in Social Network Search Algorithm
1 Introduction
2 Social Network Search Algorithm
2.1 Mood 1: Imitation
2.2 Mood 2: Conversation
2.3 Mood 3: Disputation
2.4 Mood 4: Innovation
3 Space–Time Based Social Network Search (STSNS)
4 Numerical Results
4.1 Benchmark Optimization Problems
4.2 Contender Methods
4.3 Evaluation Criteria
4.4 Non-Parametric Statistical Tests
4.5 Results and Discussions
5 Conclusion
References
Genetic Algorithm Golden Ratio Design Model for Auto Arts
1 Introduction
2 Previous Work
3 Metaheuristic and Genetic Algorithms
3.1 Metaheuristic
3.2 Genetic Algorithms (GA)
4 Material & Methods
4.1 Materials
4.2 Methods
5 Results & Evaluation
5.1 Before Setting Boundaries
5.2 After Setting Boundaries
5.3 Evaluation
6 Conclusion
References
Antenna Array Design Using Differential Evolution with Ranking-Based Mutation Operators
1 Introduction
2 Related Work
3 Differential Evolution
3.1 Self-adaptive de Algorithms
3.2 DE with Ranking-Based Mutation Operators (RankDE)
4 Linear Array Design
5 Numerical Results
5.1 Position only Synthesis
5.2 Position Phase Synthesis
6 Conclusion
References
Battle Royale Optimizer with a New Movement Strategy
1 Introduction
2 A New Movement Strategy
3 Experimental Results and Performance Evaluation
4 Conclusion
References
