Frontier Applications of Nature Inspired Computation

Preface
Contents
About the Editors
Recent Advances of Nature-Inspired Metaheuristic Optimization
	1 1 Introduction
	2 2 Recent Novel Techniques
		2.1 Emperor Penguins Colony Algorithm
		2.2 Seagull Optimization Algorithm
		2.3 Sailfish Optimizer
		2.4 Pity Beetle Algorithm
		2.5 Emperor Penguin Optimizer
		2.6 Multi-objective Artificial Sheep Algorithm
	3 3 Conclusions
	References
Prediction in Nature-Inspired Dynamic Optimization
	1 Introduction
	2 Nature-Inspired Optimization
		2.1 Evolution Strategies
		2.2 Particle Swarm Optimization
	3 Characteristics of Dynamic Optimization Problems
	4 Dynamic Optimization Approaches Based on Prediction
	5 Prediction Methods
		5.1 Kalman Filter
		5.2 Autoregressive Model
		5.3 Recurrent Neural Networks
	6 Benchmark Problem Sets
		6.1 Moving Peaks Benchmark
		6.2 CEC Competition Benchmark
		6.3 Free Peaks Benchmark
		6.4 Dynamic Sine Benchmark
	7 Quality Measures
		7.1 Best of Generation
		7.2 Best Error Before Change
		7.3 Absolute Recovery Rate
		7.4 Relative Convergence Speed
	8 Predicting Optima for Evolution Strategies
	9 Predicting Optima for Particle Swarm Optimization
	10 Uncertainty Estimation
	11 Conclusions
	References
Plant Genetics-Inspired Evolutionary Optimization: A Descriptive Tutorial
	1 1 Introduction
	2 2 Features of METO
	3 3 Biological Inspiration
		3.1 Evolutionary Theory of Mendel
		3.2 The Biological Structure of DNA
		3.3 Epimutation and Rehabilitation as Self-organizing Behavior
	4 4 Implementation
		4.1 Binary Representation of Chromosome Strand
		4.2 Population Structure
		4.3 Construction of Heredity
		4.4 Implementation of Basic Operators
	5 5 METO Algorithm
	6 6 Conclusion
	References
Trends on Fitness Landscape Analysis in Evolutionary Computation and Meta-Heuristics
	1 Introduction
	2 Fitness Landscape Approximation Using Functional Approximation in Lower-Dimensional Space
		2.1 Functional Approximation Method
		2.2 Approximating Fitness Landscape in an Original Parameter Space
		2.3 Approximating Fitness Landscape in a Dimensionality Reduced Space
		2.4 Evaluations and Discussions
	3 Fourier Analysis of Fitness Landscape
		3.1 Fourier Transform
		3.2 Fast Fourier Transform in One Dimension
		3.3 Fitness Landscape Approximated Using Fourier Transform
		3.4 Evaluations and Discussions
	4 Estimation of a Convergence Point from Fitness Landscape
		4.1 Estimation Method of a Convergence Point
		4.2 Estimated Convergence Point to Accelerate Evolutionary Computation
		4.3 Evaluations and Discussions
	5 Conclusion
	References
Lion Algorithm and Its Applications
	1 1 Introduction
	2 2 Inspiration and Interpretation
		2.1 Social Behavior of Lions
		2.2 Interpretation
	3 3 Standard LA
	4 4 Problem Model and LA Optimization
	5 5 Variants of LA
	6 6 Notable Applications
		6.1 System Identification
		6.2 Other Applications
	7 7 Test Function
		7.1 Test Case 1
		7.2 Test Case 2
		7.3 Test Case 3
		7.4 Test Case 4
	8 8 Discussion and Conclusion
		8.1 Algorithmic Efficiency
		8.2 Future Scope
	References
A Self-adaptive Nature-Inspired Procedure for Solving the Quadratic Assignment Problem
	1 Introduction
	2 Problem Formulation
	3 Related Work
	4 The SAFI
	5 Computational Experiments
		5.1 Parameters Setting
		5.2 Comparing RH with Uniform
		5.3 Detailed Performance on Two Typical Instances with the Size of 20 and 60
		5.4 Performance on Real-Life Instances
		5.5 Performance on Taillard and Skorin-Kapov Instances
		5.6 Comparing the Performance of the SAFI with that of Other Procedures
	6 Concluding Remarks
	References
Modified Binary Grey Wolf Optimizer
	1 Introduction
		1.1 Chapter Structure
	2 Transmission Network Expansion Planning Problem Formulation
		2.1 Test Systems
	3 Grey Wolf Optimizer
		3.1 Continuous GWO
		3.2 Binary Grey Wolf Optimizer
		3.3 Modified Binary Grey Wolf Optimizer
	4 Results and Discussion
		4.1 Test Systems Analysis
		4.2 Literature Comparison
	5 Conclusion and Future Works
	References
Tracing the Points in Search Space in Plant Biology Genetics Algorithm Optimization
	0 1 Introduction
	0 2 Exploration of the Search Space
	0 3 Global Exploration
	0 4 Intermediate Search
	0 5 Local Exploration
	0 6 Conclusion
	References
Artificial Cell Swarm Optimization
	1 Introduction
	2 Artificial Cell Division
		2.1 Artificial Cell Swarm
		2.2 Population Control
	3 System Overview
	4 Proposed Work
	5 Experimental Result and Discussion
	6 Conclusion
	References
Application Example of Particle Swarm Optimization on Operation Scheduling of Microgrids
	1 1 Introduction
	2 2 Target Optimization Problems
		2.1 General Problem Framework
		2.2 Including Electricity Trade
		2.3 Considering Uncertainty
	3 3 Application of Particle Swarm Optimization
		3.1 Solution Method Based on Standard Particle Swarm Optimization
		3.2 Solution Method Based on Binary Particle Swarm Optimization and Quadratic Programming
	4 4 Performance Evaluation of Particle Swarm Optimization
		4.1 Basis of Discussions
		4.2 Results and Discussion in Performance of Particle Swarm Optimization
	5 5 Verification of Validity in Problem Frameworks
		5.1 Results and Discussion
		5.2 Additional Numerical Simulations
	6 6 Conclusions
	Acknowledgements
	References
Modified Monkey Search Technique Applied for Planning of Electrical Energy Distribution Systems
	1 1 Introduction
		1.1 Contributions
		1.2 Organization
	2 2 Background and Related Works
		2.1 Original Monkey Search
		2.2 Modified Monkey Search Algorithm
		2.3 MMS Flowchart
		2.4 Differences Between the MS and MMS Algorithms
		2.5 MS and MMS Algorithm Approach
	3 3 Problem Formulation
		3.1 Modeling of the Meter Allocation Problem via MMS
		3.2 Measurement Planning Methodology
	4 4 Results and Discussion
		4.1 The Parameters Used for the MMS and MS
		4.2 The Parameters Used for GA
		4.3 The Parameters Used for SA
		4.4 The Parameters of the Meter Allocation Problem
		4.5 The Machine Configuration and the Software
	5 5 Discussion and Conclusions
		5.1 Further Research Topics
	Acknowledgements
	References
Artificial Neural Network Trained by Plant Genetic-Inspired Optimizer
	1 1 Introduction
	2 2 Evolutionary Nature-Inspired Optimizers
	3 3 Artificial Neural Network (ANN)
	4 4 Complexity Level in Designing ANN
	5 5 Generation of Population
	6 6 Loss Function
	7 7 Feature Extraction
	8 8 Conclusion
	References
Continuous Optimizers for Automatic Design and Evaluation of Classification Pipelines
	1 Introduction
	2 AutoML
	3 Proposed NiaAML Method
		3.1 Composing the Classification Pipeline
		3.2 Model Evaluation
	4 Experiments and Results
		4.1 The Results on the Yeast Dataset
		4.2 The Results on the Ecoli Dataset
		4.3 The Results on the Abalone Dataset
		4.4 Summary
	5 Conclusions and Future Work
	References
Evolutionary Artificial Neural Networks: Comparative Study on State-of-the-Art Optimizers
	1 1 Introduction
	2 2 Agriculture Machinery
		2.1 System Context
		2.2 Scenarios
		2.3 Stakeholders and Needs
	3 3 Convergence Curve
	4 4 Kruskal–Wallis Statistical Analysis of the Results
	5 5 Conclusion
	References
Application of Recent Metaheuristic Techniques for Optimizing Power Generation Plants with Wind Energy
	1 1 Introduction
		1.1 Contributions
		1.2 Organization
	2 2 Problem Formulation
		2.1 Wake Effect
		2.2 General Formulation
	3 3 Metaheuristics
		3.1 Bat Algorithm—BA
		3.2 Grey Wolf Optimizer—GWO
		3.3 Sine Cosine Algorithm—SCA
		3.4 Adaptations and Chaotic Map
	4 4 Results and Discussion
		4.1 Case (I): North–South
		4.2 Case (II): Multiple Directions
	5 5 Conclusion
	Acknowledgements
	References
Design and Comparison of Two Evolutionary and Hybrid Neural Network Algorithms in Obtaining Dynamic Balance for Two-Legged Robots
	1 1 Introduction
	2 2 Description of the Two-Legged Robot
	3 3 Proposed Soft Computing-Based Approaches
		3.1 MCIWO Algorithm
	4 4 Results and Discussions
		4.1 MCIWO-NN Approach
		4.2 PSO-NN Approach
		4.3 Comparative Study
	5 5 Conclusions
	References
Optimizing the Crane\'s Operating Time with the Ant Colony Optimization and Pilot Method Metaheuristics
	1 Introduction
	2 Related Work
	3 Definition of the CRP
		3.1 The Crane\'s Operating Time
	4 The Heuristic Algorithm
	5 The ACO Algorithm
		5.1 The Pheromone Model
		5.2 The Transition Rule
		5.3 Global and Local Update Rules
		5.4 Implementation
	6 The Pilot Method Algorithm
	7 Computational Experiments
		7.1 Analysis of the Triad Heuristic Algorithm
		7.2 Analysis of the ACO Algorithm
		7.3 Analysis of the Pilot Method Algorithm
	8 Conclusion
	References