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.

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

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

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

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

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

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

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.

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

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

Value Function Approximation

Optimization Techniques in Artificial Intelligence: Foundations, Mathematical Models, Algorithms, and Real-World Applications (Vol-2)