Cover Image

Metaheuristics-based materials optimization : enhancing materials applications /

Metaheuristics-Based Materials Optimization: Enhancing Materials Applications provides a guide to using metaheuristics-based computational techniques to improve the design, performance, and broaden the applications of various materials. The book fuses optimization algorithms with materials engineeri...

Full description

Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Singh Harbinder (Editor), Rajput, Shailendra (Editor), Sharma, Abhishek (Editor)
Format: eBook
Language:English
Published: Cambridge, MA : Woodhead Publishing, 2025.
Series:Elsevier Series in Mechanics of Advanced Materials
Subjects:
Materials.
Metaheuristics.
Mathematical optimization.
Matériaux.
Métaheuristiques.
Optimisation mathématique.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Metaheuristics-Based Materials Optimization
  • Metaheuristics-Based Materials Optimization
  • Copyright
  • Contents
  • Contributors
  • Preface
  • 1
  • Metaheuristics algorithms: Fundamental aspects and applications in optimization problems
  • 1.1 Introduction
  • 1.1.1 Optimization problems
  • 1.1.2 Need for metaheuristics
  • 1.2 Metaheuristics fundamental concepts
  • 1.2.1 How do metaheuristics work?
  • 1.2.2 Characteristics of metaheuristics and generic framework
  • 1.3 Classification of metaheuristics methods
  • 1.3.1 Local search versus global search
  • 1.3.2 Single-solution versus population-based
  • 1.3.3 Hybridization and memetic algorithms
  • 1.4 Parallel metaheuristics
  • 1.4.1 Parallel design
  • 1.4.2 Parallel metaheuristic strategies
  • 1.4.3 Parallel implementation and time complexity
  • 1.5 Nature-inspired and metaphor-based metaheuristics
  • 1.6 Applications of metaheuristics
  • 1.7 Metaheuristic software frameworks (MSF)
  • 1.7.1 ParadisEO
  • 1.7.2 ACO-TSP framework
  • 1.7.2.1 ACO libraries in programming languages
  • 1.7.3 jMetal framework
  • 1.7.4 DEAP framework
  • 1.7.4.1 How to use DEAP?
  • 1.7.4.2 Core architecture of DEAP
  • 1.7.5 EMILI
  • 1.7.6 Jenetics Framework
  • 1.8 Challenges of metaheuristics
  • 1.9 Conclusion
  • References
  • 2
  • Metaheuristics approaches for sustainable material optimization: Enhancing environmental impact and efficiency
  • 2.1 Introduction
  • 2.2 Why do we need sustainable material optimization?
  • 2.3 Sustainable materials
  • 2.4 Metaheuristic approaches for the optimization of sustainable materials
  • 2.4.1 EAs-based metaheuristic approach
  • 2.4.2 Swarm intelligence-based algorithms
  • 2.4.3 Human and animal lifestyles-based algorithms
  • 2.5 Role of metaheuristic approaches for the optimization of sustainable material
  • 2.5.1 Environmental footprints.
  • 2.5.2 Problem complexity in optimization
  • 2.5.3 Flexibility and adaptability
  • 2.5.4 Competitiveness in the market
  • 2.6 Impact of metaheuristic approaches for sustainable material optimization
  • 2.6.1 Life cycle assessment
  • 2.6.2 Substitution of materials
  • 2.6.3 Circular economy
  • 2.7 Challenges and future prospects
  • 2.7.1 Challenges
  • 2.7.2 Future aspects
  • 2.8 Conclusion
  • References
  • 3
  • Review and application of material minimization of reinforced concrete members employing metaheuristics
  • 3.1 Introduction
  • 3.2 Metaheuristic
  • 3.2.1 Example of metaheuristics
  • 3.2.2 Objective function
  • 3.2.3 Single-objective function
  • 3.2.4 Multi-objective function
  • 3.2.5 Usage of the constraints in the optimization process
  • 3.2.6 Benefits and drawbacks of metaheuristic algorithms
  • 3.3 Reinforced concrete structures and members
  • 3.3.1 Reinforced concrete beams
  • 3.3.2 Reinforced concrete columns
  • 3.3.3 Reinforced concrete slabs
  • 3.3.4 Reinforced concrete retaining walls
  • 3.3.5 Reinforced concrete frames
  • 3.3.6 Reinforced concrete footings and piles
  • 3.4 Numerical example
  • 3.5 Conclusion
  • References
  • 4
  • A review on metaheuristic algorithms: Recent and future trends
  • 4.1 Introduction
  • 4.2 Background
  • 4.3 Metaheuristic algorithms
  • 4.3.1 Existing metaheuristic algorithms
  • 4.3.2 Other algorithms
  • 4.4 Challenges
  • 4.5 Future scope and applications
  • 4.6 Summary
  • References
  • 5
  • Introduction to optimization techniques commonly used in materials science
  • 5.1 Introduction
  • 5.2 Optimization algorithms
  • 5.2.1 Classification of nature-inspired optimization algorithms
  • 5.2.1.1 Evolutionary-based techniques
  • 5.2.1.2 Swarm-based techniques
  • 5.2.1.3 Human-based techniques
  • 5.2.1.4 Physic-based techniques
  • 5.3 Implementation of optimization algorithms for material science application.
  • 5.3.1 Optimization of photovoltaic cell using GA
  • 5.3.2 Optimizing fibre-reinforced polymer (FRP) applications in civil engineering
  • 5.3.3 Particle-ant colony optimization (PACO) enhanced artificial magnetic conductor (AMC) based liquid sensor
  • 5.3.4 Taguchi method for cementitious matrix in textile reinforce concrete (TRC) for enhanced compressive strength
  • 5.3.5 Additive manufacturing of aerospace components using topology optimizaton
  • 5.4 Conclusion
  • References
  • 6
  • Review of metaheuristic-based optimization in structural materials and design
  • 6.1 Introduction
  • 6.2 Structural materials
  • 6.3 Structural optimum design
  • 6.4 Structural control
  • 6.5 Conclusion
  • References
  • 7
  • Advanced computational techniques: Bridging metaheuristic optimization and deep learning for material design through im ...
  • 7.1 Introduction
  • 7.2 Metaheuristic optimization techniques
  • 7.2.1 Genetic algorithms (GAs)
  • 7.2.2 Particle swarm optimization (PSO)
  • 7.2.3 Simulated annealing (SA)
  • 7.2.4 Ant Colony Optimization (ACO)
  • 7.2.5 Differential Evolution (DE)
  • 7.2.6 Harmony Search (HS)
  • 7.2.7 Bat Algorithm (BA)
  • 7.2.8 Applications of metaheuristics in material design
  • 7.2.8.1 Challenges and limitations of metaheuristics
  • 7.3 DL for image enhancement in material science
  • 7.3.1 Application of image enhancement in material science
  • 7.3.2 Challenges of DL in material image processing
  • 7.4 Synergy between metaheuristics and DL
  • 7.4.1 Why combine metaheuristics with DL?
  • 7.4.1.1 Hybrid models
  • 7.4.1.2 Reducing dimensionality
  • 7.4.1.3 Integrating DL
  • 7.4.1.4 Computational resources
  • 7.4.1.5 Parameter sensitivity
  • 7.4.1.6 Dimensionality issues
  • 7.4.2 Hybrid approaches: Metaheuristic-DL models
  • 7.4.2.1 Case studies in material design.
  • 7.5 Applications of hybrid techniques in material design through image enhancement
  • 7.6 Challenges and future directions
  • 7.6.1 Future research directions
  • 7.6.1.1 Leveraging quantum algorithms for complex optimization
  • 7.6.1.2 Enhancing simulation and optimization at the atomic and molecular levels
  • 7.6.1.3 Using Reinforcement Learning (RL) for real-time optimization
  • 7.6.1.4 Improving efficiency and adaptability in production methods
  • 7.6.1.5 Applying hybrid models to soft materials
  • 7.6.1.6 Exploring applications in biomaterials
  • 7.6.1.7 Integrating quantum computing, machine learning, and material science
  • 7.7 Conclusion
  • References
  • 8
  • Application of metaheuristic spotted hyena optimization in strength prediction of concrete
  • 8.1 Introduction
  • 8.2 Methodology
  • 8.2.1 XGBoost: A machine learning algorithm
  • 8.2.2 Random search optimization
  • 8.2.3 Grid search optimization
  • 8.2.4 Spotted hyena optimization
  • 8.2.5 Incorporation of SHO in XGBoost
  • 8.3 Database for case study
  • 8.4 Model performance inspection parameters
  • 8.5 Results and discussion
  • 8.5.1 Hyperparameter optimization
  • 8.5.2 Effect of SHO on prediction performance
  • 8.5.3 Sensitivity analysis
  • 8.6 Conclusions
  • References
  • 9
  • Metaheuristic strategies for advancing energy storage material design
  • 9.1 Introduction
  • 9.2 Fundamentals of metaheuristic algorithms
  • 9.2.1 Common metaheuristic algorithms
  • 9.2.1.1 Single-solution-based algorithm
  • Simulated annealing (SA)
  • Tabu search (TS)
  • Micro-canonical annealing (MCA)
  • Guided local search (GLS)
  • 9.2.1.2 Population-based algorithm
  • Genetic algorithms (GA)
  • Particle swarm optimization (PSO)
  • Ant colony optimization (ACO)
  • Differential evolution (DE)
  • Hybrid metaheuristics (HM)
  • 9.2.1.3 Comparative analysis of metaheuristic algorithms.
  • 9.3 Energy storage materials: An overview
  • 9.4 Application of metaheuristics in energy storage
  • 9.4.1 Metaheuristic approaches in battery materials optimization
  • 9.4.1.1 Lithium-ion battery storage materials
  • 9.4.1.2 Optimization of solid-state electrolytes
  • 9.4.2 Supercapacitors and metaheuristic algorithms
  • 9.4.3 Metaheuristics in green hydrogen storage materials
  • 9.4.4 Role of metaheuristics in thermal energy storage optimizations
  • 9.5 Case studies and real-world applications
  • 9.5.1 Case study 1: Optimization of cathode materials using genetic algorithm
  • 9.5.2 Case study 2: Optimization of cathode materials using particle swarm optimization algorithm
  • 9.5.3 Comparative analysis of metaheuristic performance in energy storage applications
  • 9.5.3.1 Comparison between GA and PSO
  • 9.6 Challenges and future directions
  • 9.6.1 Limitation of current metaheuristic approaches
  • 9.6.2 Integration of machine learning with metaheuristics: A promising approach
  • 9.6.3 Future trends in metaheuristics for energy storage
  • 9.7 Conclusion
  • References
  • 10
  • Application of adaptive harmony search and machine learning on optimization problems about strength of materials
  • 10.1 Introduction
  • 10.2 Materials and methods
  • 10.2.1 Optimization
  • 10.2.1.1 Adaptive harmony search (AHS)
  • 10.2.2 Dataset (data description)
  • 10.2.3 Machine learning
  • 10.2.3.1 Machine learning algorithms
  • Linear regression (LR)
  • Decision tree regression (DTR)
  • ElasticNet regression
  • K-neighbours regression
  • Support vector regression (SVR)
  • Random forest regression (RFR)
  • Gradient boosting regression (GBR)
  • Histogram gradient boosting regression (HGBR)
  • Voting
  • Stacking
  • 10.2.3.2 Model performance metrics
  • R2 (coefficient of determination)
  • Root mean square error (RMSE)
  • Mean absolute error (MAE)
  • Mean square error (MSE).