Swarm Intelligence (SI) has grown significantly, both from the perspective of algorithmic development and applications covering almost all disciplines science and technology. This book emphasizes the studies of existing SI techniques, their variants and applications. The book also contains reviews of new developments in SI techniques and hybridizations. Algorithm specific studies covering basic introduction and analysis of key components of these algorithms, such as convergence, balance of solution accuracy, computational costs, tuning and control of parameters. Application specific studies incorporating the ways of designing objective functions, solution representation and constraint handling. The book also includes studies on application domain specific adaptations in the SI techniques. The book will be beneficial for academicians and researchers from various disciplines of engineering and science working in applications of SI and other optimization problems.
Author(s): Anupam Biswas, Can B. Kalayci, Seyedali Mirjalili
Series: Studies in Computational Intelligence, 1054
Publisher: Springer
Year: 2022
Language: English
Pages: 415
City: Cham
Preface
Contents
State-of-the-Art
A Brief Tutorial on Optimization Problems, Optimization Algorithms, Meta-Heuristics, and Swarm Intelligence
1 Optimization Problems
2 Optimization Algorithms
2.1 Derivative Dependent Algorithms
2.2 Derivative Free Algorithms
3 Meta-Heuristics
4 Swarm Intelligence Algorithms
References
Introductory Review of Swarm Intelligence Techniques
1 Introduction
2 Generic Framework of Swarm Intelligence Techniques
3 Evolution of Swarm Intelligence Techniques
4 Prominent Swarm Intelligence Algorithms
4.1 Particle Swarm Optimization
4.2 Firefly Algorithm
4.3 Bacteria Colony Algorithm
4.4 Crow Search Algorithm
4.5 Grey Wolf Optimization
4.6 Sperm Whale Algorithm
5 Applications of SI Techniques
6 Discussions and Conclusion
References
Swarm Intelligence for Deep Learning: Concepts, Challenges and Recent Trends
1 Introduction
2 Swarm Intelligence Algorithms: An Overview
3 Deep Learning: An Overview
4 Swarm Intelligence with Deep Learning
4.1 Neuroevolution
4.2 Other Popular Deep Frameworks with SI
5 Challenges and Future of Intelligent Deep Learning
6 Conclusion
References
Advances on Particle Swarm Optimization in Solving Discrete Optimization Problems
1 Introduction
2 Particle Swarm Optimization (PSO)
3 Knapsack Problem (KP) and Solution Using PSO
3.1 KP Basics and Constraints
3.2 Solution of KP Using PSO
4 Traveling Salesman Problem (TSP) and Solution Using PSO
4.1 TSP Basics and Constrains
4.2 Solution of TSP Using PSO
5 Vehicle Routing Problem (VRP) and Solution Using PSO
5.1 CVRP Basics and Constrains
5.2 Solution of CVRP Using PSO
6 University Course Scheduling Problem (UCSP) and Solution Using PSO
6.1 UCSP Basics and Constraints
6.2 Solution of UCSP Using PSO
7 Conclusions
References
Performance Analysis of Hybrid Memory Based Dragonfly Algorithm in Engineering Problems
1 Introduction
2 Mathematical Interpretation of DADE
3 Performance Evaluation
3.1 Experimental Results
3.2 Analysis of DADE
3.3 Statistical Analysis
4 Convergence Analysis
5 Conclusion
References
Engineering Problems
Optimum Design and Tuning Applications in Structural Engineering via Swarm Intelligence
1 Introduction
2 Review on Design Optimization via Swarm Intelligence and Metaheuristic Algorithm
2.1 Optimization of Truss Structures
2.2 Reinforced Concrete Members
2.3 Frame Structures
3 Review on Tuning Optimization of Structural Control Systems Via Swarm Intelligence and Metaheuristic Algorithms
3.1 Passive Tuned Mass Dampers (TMD)
3.2 Active Tuned Mass Dampers (TMD)
4 Application Results of Several Problems
4.1 Optimum Design of Retaining Walls
4.2 Span Optimization of Frame Structures
4.3 Optimum Design of Passive and Active Mass Dampers
5 Conclusion
References
Bee Colony Optimization with Applications in Transportation Engineering
1 Introduction
2 Bee Colony Optimization
2.1 Constructive Version of the Algorithm (BCOc)
2.2 Improvement Version of the Algorithm (BCOi)
3 Review of BCO Applications in Transportation Engineering
4 Conclusions
References
Application of Swarm Based Approaches for Elastic Modulus Prediction of Recycled Aggregate Concrete
1 Introduction
2 Data Collection
3 Artificial Neural Network
4 Elephant Herding Optimization
5 Hybrid of ANN and EHO
6 Results and Discussions
6.1 Performance of Artificial Neural Network
6.2 Performance of Artificial Neural Network-Elephant Herding Optimization
7 Conclusions
References
Grey Wolf Optimizer, Whale Optimization Algorithm, and Moth Flame Optimization for Optimizing Photonics Crystals
1 Introduction
2 Photonic Crystal Waveguide and the Optimization Problem Targeted in This Work
3 Swarm Intelligence Algorithm
4 Results
5 Conclusion
References
Intelligent and Reliable Cognitive 5G Networks Using Whale Optimization Techniques
1 Introduction
2 Challenges in 5G
3 Cognitive Radio
4 Low-Density Parity-Check (LDPC) in 5G Channel
5 Proposed Approach: Integration of CR, LDPC in 5G with Whale Optimization Algorithm
5.1 Whale Optimization Technique
6 Results and Discussion
7 Conclusion
References
Machine Learning
Automatic Data Clustering Using Farmland Fertility Metaheuristic Algorithm
1 Introduction
2 Related Works
3 Farmland Fertility Algorithm
4 Proposed Method
5 Result and Discussion
6 Conclusion and Future Works
References
A Comprehensive Review of the Firefly Algorithms for Data Clustering
1 Introduction
2 Contribution of the FA for Data Clustering
2.1 Use of Different Variants of FA Without Hybridization
2.2 FA Combined with K-Means Algorithm
2.3 FA Combined with Evolutionary Computing Algorithms
2.4 FA Combined with the Particle Swarm Optimization Algorithm
2.5 FA Combined with Fuzzy C-Means Algorithm
2.6 FA Combined with Density Peaks Clustering Algorithms
2.7 FA Combined with Markov Clustering Algorithms
3 Representations, Initialization of Fireflies and Cluster Validation Measures Used by the FA Based Methods for Data Clustering
3.1 Definition of Individuals (Representations)
3.2 Initializations
3.3 Performance Measures
4 Possible Further Enhancements to FA Based Clustering Algorithms
5 Conclusion
References
A Hybrid African Vulture Optimization Algorithm and Harmony Search: Algorithm and Application in Clustering
1 Introduction
2 Related Work
3 Background Knowledge
3.1 Data Clustering Problem
3.2 African Vulture Optimization Algorithm
3.3 Harmony Search Algorithm
4 AVOAHS Algorithm
5 Result and Discussion
6 Conclusion and Future Work
References
Estimation Models for Optimum Design of Structural Engineering Problems via Swarm-Intelligence Based Algorithms and Artificial Neural Networks
1 Introduction
2 Artificial Neural Networks (ANNs)
3 Applications via ANNs
4 Numerical Results
5 Results and Conclusion
References
A Novel Codebook Generation by Lévy Flight Based Firefly Algorithm
1 Introduction
2 Codebook Generation Algorithms
2.1 Vector Quantization and LBG Algorithm
2.2 PSO Algorithm
2.3 Fruit Fly Algorithm
2.4 Firefly Algorithm
3 Lévy Flight Based Firefly Algorithm for Codebook Generation
4 Parameters
5 Simulations and Results
6 Conclusion
References
Novel Chaotic Best Firefly Algorithm: COVID-19 Fake News Detection Application
1 Introduction
2 Background
2.1 Feature Selection
2.2 Metaheuristic Algorithms
3 Proposed Method
3.1 The Firefly Algorithm
3.2 Drawbacks of the FA
3.3 Proposed Chaotic-Based FA Metaheuristics
4 Experimental Setup
4.1 Dataset Collection
4.2 Data Preprocessing and Feature Extraction
4.3 Feature Selection
4.4 Model Development
4.5 Evaluation and Assesment
5 Results and Discussion
6 Conclusion
References
Other Applications
Artificial Bee Colony and Genetic Algorithms for Parameters Estimation of Weibull Distribution
1 Introduction
2 Weibull Distribution
2.1 Maximum Likelihood Inference
2.2 Moment Inference
2.3 Proposed Functions
3 Swarm Intelligence Methods
3.1 Artificial Bee Colony
3.2 Genetic Algorithms
4 Simulation Study
4.1 Swarm Parameter Settings
4.2 Evaluation Criteria
4.3 Computational Implementation
4.4 Simulation Results
4.5 Real Data Example
5 Conclusion
References
Graph Structure Optimization for Agent Control Problems Using ACO
1 Introduction
2 Previous Works
3 Proposed Method
3.1 GNP Algorithm
3.2 Ant Colony Network Programming
4 Experimental Results
4.1 Pursuit Domain
4.2 Experimental Results and Analysis
5 Conclusions
References
A Bumble Bees Mating Optimization Algorithm for the Discrete and Dynamic Berth Allocation Problem
1 Introduction
2 Berth Allocation Problem
3 Bumble Bees Mating Optimization
4 Bumble Bees Mating Optimization for the Discrete and Dynamic Berth Allocation Problem
5 Computational Results
5.1 Overall Results
6 Conclusions
References
Applying the Population-Based Ant Colony Optimization to the Dynamic Vehicle Routing Problem
1 Introduction
2 Dynamic Vehicle Routing Problem
2.1 Problem Formulation
2.2 Generating Dynamic Test Cases
3 Population-based Ant Colony Optimization
3.1 Constructing Solutions
3.2 Updating Population-List
3.3 Updating Pheromone Trails
3.4 Responding to Dynamic Changes
4 Experimental Results
4.1 Experimental Setup
4.2 Comparison of P-ACO+H Against ACO+H
4.3 Comparison of P-ACO+H Against Other P-ACO Algorithms
5 Conclusions
References
An Improved Cuckoo Search Algorithm for the Capacitated Green Vehicle Routing Problem
1 Introduction
2 Literature Review
3 Material and Method
3.1 A Classical Green VRP Model
3.2 Solution Method
3.3 The VLCS Algorithm
4 Experimental Study
5 Conclusion and Future Research
References
Multi-Objective Artificial Hummingbird Algorithm
1 Introduction
2 Artificial Hummingbird Algorithm (AHA)
2.1 Guided Foraging
2.2 Territorial Foraging
2.3 Migration Foraging
3 Multi-objective Artificial Hummingbird Algorithm
4 Experimental Results and Discussions for Engineering Problems
5 Conclusion
References
