Nature-Inspired Methods for Metaheuristics Optimization: Algorithms and Applications in Science and Engineering (Modeling and Optimization in Science and Technologies (16), Band 16)

Preface
Contents
Contributors
Part I Algorithms
	1 Genetic Algorithms: A Mature Bio-inspired Optimization Technique for Difficult Problems
		1.1 Introduction
		1.2 The Basic Idea and the Terminology
		1.3 Genetic Operators
			1.3.1 Selection
				1.3.1.1 The Biased Roulette Wheel
				1.3.1.2 The Tournament Method
				1.3.1.3 The Elitist Approach
			1.3.2 Reproduction Operators
				1.3.2.1 Crossover
				1.3.2.2 Mutation
				1.3.2.3 Mutation and Crossover Probability
				1.3.2.4 Niche, Speciation, Sharing, Crowding, Migration
				1.3.2.5 Antimetathesis
		1.4 Termination of the Optimization Procedure
		1.5 Constraint Handling
		1.6 Steady State Genetic Algorithms
		1.7 Selection of Optimization Technique-Advantages and Disadvantages of Genetic Algorithms
		1.8 Overall Accuracy vs Accuracy of the Optimization Procedure
		1.9 Teaching Course Modules on Genetic Algorithms
		1.10 Concluding Remarks
		References
	2 Introduction to Genetic Algorithm with a Simple Analogy
		2.1 Introduction
		2.2 A Simple Analogy to GA
		2.3 Conclusion
		References
	3 Interactive Genetic Algorithm to Collect User Perceptions. Application to the Design of Stemmed Glasses
		3.1 Introduction
		3.2 Background on Genetic Algorithms
			3.2.1 Definition
			3.2.2 Encoding of the Design Variables
			3.2.3 The Genetic Operators
		3.3 Interactive Genetic Algorithm
			3.3.1 Synoptic of the IGA Process
			3.3.2 Challenges of IGA
			3.3.3 Set up of the Genetic Algorithms
		3.4 Application Case: Protocol and Results
			3.4.1 Goal-Seeking Task
			3.4.2 Free Task on “Elegant” Glass: Protocol
			3.4.3 Free Task on “Elegant” Glass: Results
			3.4.4 Conclusions on the Two Tests on the Glasses
		3.5 Synthesis and Perspectives on the Use of IGA for Design
		3.6 Conclusion
		References
	4 Differential Evolution and Its Application in Identification of Virus Release Location in a Sewer Line
		4.1 Introduction
		4.2 Structure of the Algorithm
			4.2.1 Initialization of the Population
			4.2.2 Mutation with Difference Vectors
			4.2.3 Recombination or Crossover
			4.2.4 Selection
		4.3 Parameters and Sensitivity
		4.4 Differential Evolution on Mathematical Functions
			4.4.1 Cross-in-Tray Function
			4.4.2 Rastrigin Function
			4.4.3 Goldstein-Price Function
		4.5 Conclusions
		References
	5 Artificial Bee Colony Algorithm and an Application to Software Defect Prediction
		5.1 Introduction
		5.2 ABC Algorithm
		5.3 An Engineering Application: Software Defect Prediction
			5.3.1 Artificial Neural Networks for Predicting Software Defects
			5.3.2 ABC Algorithm in Training an ANN for Software Defect Prediction
			5.3.3 Experiments
				5.3.3.1 Data Set and Metrics
				5.3.3.2 Prediction Performance Evaluation
		5.4 Conclusion
		References
	6 Firefly Algorithm and Its Applications in EngineeringOptimization
		6.1 Introduction
		6.2 Firefly Algorithm
			6.2.1 Philosophy of the Algorithm
			6.2.2 Mathematical Background for the Algorithm
			6.2.3 Modified Firefly Algorithm
			6.2.4 Advantages of FA
		6.3 Parameters of the Algorithm and Their Sensitivity
			6.3.1 Light Absorption Coefficient `γ\'
			6.3.2 Maximum Attractiveness `β0\'
			6.3.3 Randomness Parameter `α\'
		6.4 Firefly Algorithm Applied to a Mathematical Function
		6.5 Conclusion
		References
	7 Introduction to Shuffled Frog Leaping Algorithm and Its Sensitivity to the Parameters of the Algorithm
		7.1 Introduction
		7.2 Methodology for SFLA
			7.2.1 Frog Leaping Algorithm
			7.2.2 Parameters and Sensitivity
		7.3 SFLA on Mathematical Functions
			7.3.1 Himmelblau Function
			7.3.2 Rosenbrock Function
			7.3.3 Sphere Function
		7.4 Conclusions
		References
	8 Groundwater Management Using Coupled Analytic Element Based Transient Groundwater Flow and Optimization Model
		8.1 Introduction
		8.2 Formulation of AEM-PSO Model
			8.2.1 AEM Flow Model
			8.2.2 Optimization Model
			8.2.3 Simulation-Optimization Model
		8.3 Model Application and Discussions
			8.3.1 Sensitivity Analysis
		8.4 Results and Discussions
			8.4.1 Scenario-I (Static Pumping Rate)
			8.4.2 Scenario-II (Dynamic Pumping Rate)
		8.5 Conclusions
		References
	9 Investigation of Bacterial Foraging Algorithm Applied for PV Parameter Estimation, Selective Harmonic Elimination in Inverters and Optimal Power Flow for Stability
		9.1 Introduction
		9.2 Bacterial Foraging Algorithm
			9.2.1 Chemotaxis
			9.2.2 Swarming
			9.2.3 Reproduction
			9.2.4 Elimination and Dispersal
			9.2.5 Movement of Bacteria in Search Space
			9.2.6 Verification of BFA with Mathematical Equations
			9.2.7 Modified Bacterial Foraging Algorithm
		9.3 BFA for PV Parameter Estimation
			9.3.1 PV Modelling
			9.3.2 Problem Formulation
			9.3.3 Results and Discussion
		9.4 BFA for Selective Harmonic Elimination in PWM Inverter
			9.4.1 Problem Formulation
			9.4.2 Simulation Results and Discussion
		9.5 Modified BFA for Optimal Power Flow
			9.5.1 Modelling of FACTS Devices
				9.5.1.1 Modeling of SVC
				9.5.1.2 Modeling of TCSC
			9.5.2 Formulation of Objective Function
				9.5.2.1 Cost Function
			9.5.3 Optimal Cost Minimization Using BFA
				9.5.3.1 Optimal FACTS allocation
			9.5.4 Results and Discussion
		9.6 Conclusion
		References
	10 Application of Artificial Immune System in Optimal Design of Irrigation Canal
		10.1 Introduction
		10.2 Overview of AIS Algorithms
			10.2.1 Clonal Selection Algorithm
			10.2.2 Negative Selection Algorithm
			10.2.3 Immune Network Algorithms
		10.3 Formulation of AIS Algorithm
		10.4 Model Application
			10.4.1 Design Problem
			10.4.2 Optimization Using CSA
		10.5 Results and Discussion
		10.6 Summary and Conclusions
		Appendix: MATLAB Code for Real Coded Clonal Selection Algorithm
			Optimization Module
			Initialization Function
			Cloning Function
			Mutation Function
		References
	11 Biogeography Based Optimization for Water Pump Switching Problem
		Nomenclature
		Greek letters
		Subscript
		Superscript
		Biogeography-Based Optimization
		Water Pump Switching Problem
		Mathematical Formulation for Water Pump Switching Optimization Problem
			Objective Function
			Discharge Pressure Constraints
			Discharge Pressure Bound Constraints
			Suction Pressure Constraints
			Suction Pressure Bound Constraints
			Initial Suction Pressure Constraints
			Binary Decision Variable Constraints
		Results and Discussion
		Summary
		References
	12 Introduction to Invasive Weed Optimization Method
		Introduction
		Working Procedure of Invasive Weed Optimization Algorithm (IWO)
			Initialize a Population
			Reproduction
			Spatial Distribution of Seeds
			Competitive Elimination
		Standard Examples
			Sphere Function
			Himmelblau Function
			Ackley Function
		Conclusions
		References
	13 Single-Level Production Planning in Petrochemical Industries Using Novel Computational Intelligence Algorithms
		Nomenclature
		Introduction
		Problem Description
		Solution Strategy
		Brief Description of CI Techniques
			Sanitized–Teaching–Learning–Based Optimization Algorithm
			Moth Flame Optimization Algorithm
			Flower Pollination Optimization Algorithm
			Water Cycle Optimization Algorithm
			Adaptive Wind Driven Optimization Algorithm
		Results and Discussion
		Conclusions
		References
	14 A Multi-Agent Platform to Support Knowledge Based Modelling in Engineering Design
		Introduction
		Background
		Modeling the Knowledge
			Variable Model
			Modelling Process
		Multi Agent System (MAS)
			Environmental Entities and Properties
			The State of the Constituents
			Agents Embodied as Environmental Entities
		Agents Analysis
			Inter-Agent Analysis: Communication Among Agents
			Intra-Agent Analysis: Standardization
		Implementation
			Agents Definition and Communication
			Experiences Re-Use: Model Construction
		Conclusion
		References
Part II Applications
	15 Synthesis of Reference Trajectories for Humanoid Robot Supported by Genetic Algorithm
		Introduction
		Fundamentals of Genetic Algorithms
		Gait Generation Using Coupled Oscillators
		Genetic Algorithm Applied for Parameters Search
		Fine Tuning of Gait Generator
		Final Proof
		Conclusions
		References
	16 Linked Simulation Optimization Model for Evaluation of Optimal Bank Protection Measures
		Introduction
		Hydrodynamic Model
			Governing Equations and Solution Technique
			Boundary Condition
			Courant-Friedrichs-Lewy Condition
			Artificial Viscosity
			Hydrodynamic Model Validation
		Optimization Model Formulation
			Formulation I
			Formulation II
		Solution of Linked Simulation-Optimization Model Using Genetic Algorithm
		Application of the Proposed Methodology
			Case A: Hypothetical Channel Bend
			Case B: Application to River Brahmaputra
		Results and Discussion
			Case A: Hypothetical Channel Bend
			Case B: Application to River Brahmaputra
			Computational Time Requirement
		Conclusions
		References
	17 A GA Based Iterative Model for Identification of Unknown Groundwater Pollution Sources Considering Noisy Data
		Introduction
		Methodology
		Source Identification Model
		Optimization Algorithm
		Simulation Model
		Measurement Errors
		Performance Evaluation Criteria
		Study Area
		Results and Discussion
		Conclusions
		References
	18 Efficiency of Binary Coded Genetic Algorithm in Stability Analysis of an Earthen Slope
		Introduction
		Optimization Model Formulation
		Genetic Algorithms
			Working Principle of GA
			Representation of a Solution String
			Fitness of a Solution String
			Reproduction Operator
			Crossover Operator
			Mutation Operator
			Elitism
		Results and Discussion
			Example Problem
		Conclusion
		References
	19 Corridor Allocation as a Constrained Optimization Problem Using a Permutation-Based Multi-objective Genetic Algorithm
		Introduction
		The Proposed cbCAP Model
		The Proposed Genetic Algorithm for the cbCAP Model
			Individual Representation and Initialization
				Splitting an Individual into Two Rows
				Forming cbCAP Individual
			Selection Operation
			Crossover Operation
			Mutation Operation
			Elite Preserving Mechanism
		Computational Experiment and Discussion
		Conclusion
		References
	20 The Constrained Single-Row Facility Layout Problem with Repairing Mechanisms
		Introduction
		The cSRFLP Formulation
		The Repairing Mechanisms
			Positioning Constraints
			Ordering Constraints With a Facility of a Pair in a Fixed Position
			Ordering Constraints with a Pair of Facilities in Two Adjacent Positions
			Ordering Constraints Allowing Other Facilities in Between a Pair of Facilities
			Illustration of the Repairing Mechanisms
			Pseudo-Codes of the Repairing Mechanisms
			Implementation of the Repairing Mechanisms
		Genetic Algorithm for Optimizing the cSRFLP Model
			Individual Initialization
			Individual Evaluation
			Selection Operator
			Crossover Operator
			Mutation Operator
			Elite Preserving Mechanism
		Computational Experiment
		Conclusion
		References
	21 Geometric Size Optimization of Annular Step Fin Array for Heat Transfer by Natural Convection
		Nomenclature
		Greek Symbols
		Introduction
		Thermal Modeling of Annular Stepped Fin
			Formulation of the Thermal Model
			Non-dimensional Formulation of the Thermal Model
		Optimization Modeling
		Solution Procedure
			Constraints Handling Through Variable Bounds
			Evaluation of Objective Functions
		Numerical Experimentation and Discussion
			Scenario I
			Scenario II
			Pareto Optimal Sensitivity Analysis
		Conclusion
		References
	22 Optimal Control of Saltwater Intrusion in Coastal Aquifers Using Analytical Approximation Based on Density Dependent Flow Correction
		Introduction
		Strack\'s Analytical Solution for Saltwater Intrusion
		Modified Ghyben-Herzberg Theory Based Analytical Solution of Saltwater Intrusion
		Optimization Formulation and Application
		Conclusions
		References
	23 Dynamic Nonlinear Active Noise Control: A Multi-objective Evolutionary Computing Approach
		Introduction
		Meta-Heuristic-Based NANC System
		Dynamic Nonlinear Active Noise Control System
		Simulation Study
			Case A: Random Input Noise
				Experiment 1
				Experiment 2
				Experiment 3
			Case B: Tonal Input Noise
				Experiment 4
				Experiment 5
			Case C: Logistic Chaotic Input Noise
				Experiment 6
				Experiment 7
			Case D: Dynamically Changing Environment
				Experiment 8
				Experiment 9
		Concluding Remarks
		References
	24 Scheduling of Jobs on Dissimilar Parallel Machine Using Computational Intelligence Algorithms
		Introduction
		Problem Statement
		Algorithm Description
			Artificial Bee Colony
			Dynamic Neighborhood Learning Based Particle Swarm Optimizer (DNLPSO)
			Genetic Algorithm (GA)
			Multi-population Ensemble Differential Evolution (MPEDE)
			Sanitized Teaching-Learning Based Optimization (s-TLBO)
		Experimental Settings
		Results and Discussions
			Time Complexity
		Conclusion
		References
	25 Branch-and-Bound Method for Just-in-Time Optimization of Radar Search Patterns
		Introduction and Context
		Problem Statement
			Definition
			Example
			Combinatorial Complexity
		Integer Programming
			Problem Formulation
			Linear Relaxation
			Linear Programming
			Integral Program and Total Unimodularity
			One-Dimensional Cover Problem
			Integrality Gap
			Dynamic Programming
		Branch&Bound
			Description
			Application Example
			Multiple Solutions Enumeration
			Just-in-Time Criteria
		Application to Radar Engineering
			Radar Model
			Simulation Parameters
			Optimal Solution
			Enumeration
		Conclusion
		References
	26 Optimization of the GIS-Based DRASTIC Model for Groundwater Vulnerability Assessment
		Introduction
		Study Area
		Methodology
			DRASTIC (Conventional Method)
			Optimization of Conventional DRASTIC
				Addition of Land Use Parameter with Conventional DRASTIC Parameters (DRASTICLu)
				Revising the Rates of the Parameters Using Quality Data
				Revising the Weights of the Parameters Using AHP (Modified DRASTICLu)
		Results
			Preparation of DRASTIC Thematic Map
			DRASTIC (Conventional Method)
			Optimization of Conventional DRASTIC
				Addition of Land Use Parameter with Conventional DRASTIC Parameters (DRASTICLu)
				Revising the Rates of the Parameters Using Quality Data
				Revising the Weights of the Parameters Using AHP (Modified DRASTICLu)
		Discussion and Conclusion
		References