Description

Artificial Intelligence has revolutionized nearly every scientific and industrial discipline, from healthcare and finance to autonomous systems and intelligent robotics. At the core of these intelligent systems lies one of the most important disciplines in computer science—optimization. While Volume 1 established the mathematical foundations, linear programming, convex optimization, and gradient-based learning techniques, Volume 2 takes readers into the advanced world of evolutionary computation, swarm intelligence, reinforcement learning optimization, hybrid optimization methods, and emerging AI optimization technologies.

Optimization Techniques in Artificial Intelligence (Vol-2) is a comprehensive continuation that explores optimization beyond traditional mathematical programming. It introduces readers to nature-inspired algorithms, population-based search methods, neural network optimization, constrained optimization, multi-objective optimization, and practical AI optimization frameworks used in modern research and industry.

This volume is carefully designed to connect optimization theory with cutting-edge Artificial Intelligence applications, enabling readers to understand how intelligent systems solve complex real-world problems where classical optimization techniques alone are insufficient.

Whether optimizing deep neural networks, autonomous robots, recommendation engines, financial portfolios, healthcare systems, or foundation models, this book provides the mathematical principles, algorithmic understanding, and practical insights required by today’s AI professionals.

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What You Will Learn

✔ Evolutionary Optimization Algorithms

✔ Genetic Algorithms (GA)

✔ Particle Swarm Optimization (PSO)

✔ Ant Colony Optimization (ACO)

✔ Bee Colony Optimization

✔ Firefly Algorithm

✔ Whale Optimization Algorithm (WOA)

✔ Differential Evolution

✔ Evolution Strategies

✔ Genetic Programming

✔ Simulated Annealing

✔ Memetic Algorithms

✔ Hybrid Optimization Techniques

✔ Optimization in Deep Neural Networks

✔ CNN Optimization

✔ RNN Optimization

✔ Transformer Optimization

✔ Hyperparameter Optimization

✔ Constrained Optimization

✔ Multi-Objective Optimization

✔ Pareto Optimization

✔ Reinforcement Learning Optimization

✔ Policy Optimization

✔ Exploration vs Exploitation

✔ Optimization for Robotics

✔ AI Decision-Making Systems

✔ Real-World AI Optimization Applications

✔ Python Optimization Libraries

✔ SciPy

✔ CVXPy

✔ PyTorch Optimizers

✔ TensorFlow Optimizers

✔ Gurobi

✔ IBM CPLEX

✔ MATLAB Optimization Toolbox

✔ Performance Benchmarking

✔ AI Ethics in Optimization

✔ Energy-Efficient AI

✔ Quantum Optimization

✔ Optimization for Foundation Models

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Key Features

• Comprehensive coverage of advanced AI optimization techniques

• Evolutionary and Nature-Inspired Algorithms explained from fundamentals

• Step-by-step explanation of Genetic Algorithms and Swarm Intelligence

• Mathematical foundations with practical intuition

• Optimization methods for Machine Learning and Deep Learning

• Reinforcement Learning optimization strategies

• Multi-objective optimization and Pareto analysis

• Neural network training optimization

• Practical AI case studies across multiple domains

• Industry-standard optimization tools and software

• Future directions including Quantum AI Optimization

• Suitable for academic learning, research, and industrial applications

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Table of Contents Highlights

Part V – Evolutionary and Nature-Inspired Optimization

• Introduction to Evolutionary Algorithms
• Search Spaces and Fitness Functions
• Genetic Algorithms
• Selection, Crossover and Mutation
• Particle Swarm Optimization
• Ant Colony Optimization
• Bee Colony Optimization
• Firefly Algorithm
• Whale Optimization Algorithm
• Differential Evolution
• Simulated Annealing
• Genetic Programming
• Hybrid Evolutionary Optimization

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Part VI – Advanced and Hybrid Optimization in AI

• Neural Network Optimization
• CNN Optimization
• RNN Optimization
• Transformer Optimization
• Hyperparameter Optimization
• Constrained Optimization
• Penalty and Barrier Methods
• Pareto Optimality
• Multi-Objective Evolutionary Algorithms
• Reinforcement Learning Optimization
• Policy Optimization
• Exploration Strategies

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Part VII – Applications, Tools, and Future Directions

• Computer Vision Optimization
• Natural Language Processing
• Recommendation Systems
• Finance Optimization
• Healthcare AI
• Smart Cities
• Industrial AI
• Python Optimization Libraries
• SciPy
• CVXPy
• PyTorch
• TensorFlow
• Gurobi
• IBM CPLEX
• MATLAB Optimization Toolbox
• Performance Benchmarking
• AI Ethics
• Energy-Efficient Optimization
• Quantum Optimization
• Foundation Models
• Open Research Problems

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Who Should Read This Book?

This book is ideal for:

• Undergraduate Students
• Postgraduate Students
• PhD Scholars
• AI Researchers
• Machine Learning Engineers
• Deep Learning Engineers
• Data Scientists
• Computer Science Faculty
• Software Developers
• AI Professionals
• Research Laboratories
• Industry Practitioners
• University Libraries

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Why This Book?

Modern Artificial Intelligence extends far beyond gradient descent and conventional optimization methods. Many practical AI problems involve highly complex, nonlinear, stochastic, and non-differentiable search spaces where traditional mathematical optimization is insufficient.

This volume provides a unified understanding of evolutionary computation, swarm intelligence, neural network optimization, reinforcement learning, hybrid optimization, and emerging optimization paradigms that are shaping the future of Artificial Intelligence.

By integrating mathematical foundations, algorithmic explanations, AI applications, software tools, and future research trends, this book serves as a comprehensive guide for anyone seeking expertise in advanced AI optimization.

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Suitable For

• B.Tech
• BCA
• MCA
• M.Tech
• M.Sc. Computer Science
• Artificial Intelligence Programs
• Machine Learning Courses
• Deep Learning Courses
• Data Science Programs
• Reinforcement Learning Courses
• Research Projects
• Competitive Examinations (GATE, UGC-NET, PhD Entrance)
• Academic Libraries
• Professional AI Training

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Book Details

Title: Optimization Techniques in Artificial Intelligence

Subtitle: Foundations, Mathematical Models, Algorithms, and Real-World Applications (Vol-2)

Author: Anshuman Mishra

Language: English

Subject: Artificial Intelligence, Optimization, Machine Learning, Deep Learning, Evolutionary Computing

Level: Intermediate to Advanced

Category: Artificial Intelligence | Machine Learning | Data Science | Optimization | Evolutionary Computing | Computer Science