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Introduction

Quantum-inspired gravitational search algorithms (QGSA) are a class of optimization algorithms that are inspired by both quantum computing principles and the gravitational search algorithm. These algorithms have gained popularity in recent years due to their ability to efficiently solve complex optimization problems by utilizing the principles of quantum mechanics and gravitational forces. In this thesis, we will explore the theoretical foundations of QGSA and investigate their application in solving optimization problems.

1.1 Introduction

1.2 Background of study

1.3 Problem Statement

1.4 Objective of study

1.5 Limitation of study

1.6 Scope of study

1.7 Significance of study

1.8 Structure of the Thesis

1.9 Definition of Terms

Chapter Two: Literature Review

2.1 Introduction to optimization algorithms

2.2 Quantum computing principles

2.3 Gravitational search algorithms

2.4 Quantum-inspired algorithms

2.5 Applications of QGSA in optimization

2.6 Comparison with other optimization algorithms

2.7 Previous studies on QGSA

2.8 Challenges and limitations of QGSA

2.9 Future research directions

Chapter Three: System Design and Methodology

3.1 Introduction to system design

3.2 Problem formulation

3.3 Design of Quantum-inspired gravitational search algorithm

3.4 Implementation of QGSA

3.5 Parameter tuning and optimization

3.6 Experimental setup

3.7 Evaluation metrics

3.8 Data collection and analysis

Chapter Four: System Implementation

4.1 Introduction to system implementation

4.2 Software development

4.3 Algorithm implementation

4.4 Testing and validation

4.5 Performance optimization

4.6 Simulation results

4.7 Comparative analysis

4.8 Discussion of results

Chapter Five: Conclusion and Summary

5.1 Summary of findings

5.2 Contributions of the study

5.3 Implications for future research

5.4 Conclusion and recommendations

In this thesis, we will explore the theoretical foundations of Quantum-inspired gravitational search algorithms, review existing literature on optimization algorithms, present the system design and methodology for implementing QGSA, detail the system implementation process, and conclude with a summary of the project’s findings and implications for future research.

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