Applied Multi-objective Optimization (Springer Tracts in Nature-Inspired Computing)

Preface
Contents
Editor and Contributors
1 A Guide to Meta-Heuristic Algorithms for Multi-objective Optimization: Concepts and Approaches
	1 Introduction
	2 Meta-Heuristic Algorithms in Solving Multi-objective Problems
		2.1 Genetic Algorithm in Multi-objective Problems
		2.2 Particle Swarm Optimization Algorithm in Multi-objective Problems
		2.3 Ant Colony Optimization in Multi-objective Problems
		2.4 Multi-objective Differential Evolution Algorithm
		2.5 Other Multi-objective Meta-Heuristic Algorithms
	3 Various Multi-objective Optimization Techniques
		3.1 Weighted Sum Method
		3.2 Epsilon Constraint Method
		3.3 Pareto Dominance Method
		3.4 Other Multi-objective Optimization Techniques
	4 Applications
	5 Challenges and Future Scope
	6 Conclusions
	References
2 Counterfactual Explanations and Federated Learning for Enhanced Data Analytics Optimisation
	1 Introduction
	2 Literature Review
	3 Methodology
		3.1 Federated Learning
		3.2 Graph Theory in Gene Expression Analysis for Cancer Research
		3.3 Hierarchical Bayesian Models for Gene Expression in Cancer Research
		3.4 Counterfactual Explanations
		3.5 Structural Causal Models for Gene Expression Data
		3.6 Components
		3.7 Interventions
	4 Experimental Results
		4.1 Description of Data
		4.2 Optimal Gene Analysis
		4.3 Network Graph for the Optimal Genes
		4.4 Genes Analysis for Individual Patient
	5 Discussion
	6 Conclusion
	References
3 Multi-objective Adaptive Guided Differential Evolution for Passively Controlled Structures Equipped with a Tunned Mass Damper
	1 Introduction
	2 Related Works on TMD-Based Optimal Control
	3 Multi-objective Adaptive Guided Differential Evolution
		3.1 Fundamentals of the AGDE
		3.2 AGDE for Multi-objective Optimization
	4 Outline for Optimum Design of TMDs
	5 Numerical Study
	6 Results and Discussion
	7 Conclusions
	References
4 Evolutionary Approaches for Multi-objective Optimization and Pareto-Optimal Solution Selection in Data Analytics
	1 Introduction
	2 Overview of Multi-objective Optimization (MOO)
		2.1 Terminology in MOO
		2.2 Definition and Formulation
		2.3 Traditional Methods for MOO
		2.4 Traditional Methods for Pareto-Optimal Solution Selection
	3 MOO Problems (MOOPs) in Data Analytics
		3.1 Overview of Data Analytics
		3.2 MOO View of Data Analytics Problems
	4 Overview of Nature-Inspired Algorithms (NIAs)
		4.1 Ushering into a New Era of NIAs
		4.2 Evolutionary Algorithms
		4.3 Swarm Optimization Algorithms
		4.4 Ant Colony Optimization
		4.5 Neural Networks
		4.6 Overview of MOO Using NIAs
	5 Evolutionary MOO (EMOO)
		5.1 Formulation of EMOO Problems
		5.2 Overview of Approaches and Techniques Involved in EMOO
		5.3 EMOO in Data Analytics
		5.4 Evolutionary Pareto-Optimal Solution Selection
		5.5 Latest Research in EMOO
	6 Conclusion
	References
5 Multi-objective Lichtenberg Algorithm for the Optimum Design of Truss Structures
	1 Introduction
	2 Related Works on Multi-objective Truss Optimization
	3 Multi-objective Lichtenberg Algorithm
	4 Multi-objective Truss Optimization Problem
	5 Results and Discussion on Numerical Examples
		5.1 The 60-Bar Truss Structure
		5.2 The 200-Bar Truss Structure
	6 Conclusions
	References
6 Performance Analysis of a Multi-objective Function-Based PID Controller for System Frequency Regulation
	1 Introduction
		1.1 Organization of the Chapter
	2 System of Investigation
	3 Controller Approach with Objective Function
	4 Mayfly Algorithm
	5 Outcome and Discussion
		5.1 Case 1: Performance Analysis of Weighted Sum of PID Controller
		5.2 Case 2: Performance Analysis of Weighted Sum of PID Controller with Classical Controller
	6 Conclusion
	References
7 Multi-modal Routing in Urban Transportation Network Using Multi-objective Quantum Particle Swarm Optimization
	1 Introduction
	2 Related Works on Multi-modal Routing
	3 Multi-objective Algorithm of Quantum Particle Swarm Optimization
		3.1 Basic Concept of Particle Swarm Optimization (PSO)
		3.2 Quantum-Behaved Particle Swarm Optimization (QPSO)
		3.3 Multi-Objective Quantum-Behaved Particle Swarm Optimization (MOQPSO)
	4 State of the Optimization Problem
		4.1 Considered Area for the Study
		4.2 Multi-modal Routing
		4.3 Modeling
	5 Result Discussion on the Case Studies
	6 Conclusions
	References
8 Multi-objective Optimization for Feature Selection: A Review
	1 Introduction
	2 Multi-objective Optimization
	3 Feature Selection
	4 Feature Selection Algorithms Based on MOP
		4.1 Filter MOP-Based Feature Selection Methods
		4.2 Wrapper MOP-Based Feature Selection Methods
		4.3 Hybrid MOP-Based Feature Selection Methods
		4.4 Classifying Based on Supervision Perspective
	5 Conclusions
	References