Quantum-Inspired Genetic Algorithms (QIGAs) are a trailblazing force in the ever-evolving field of optimization, combining traditional genetic algorithms with quantum concepts to solve challenging combinatorial problems. By contrasting QIGAs with traditional Genetic Algorithms (GAs) in the setting of the Traveling Salesman Problem (TSP), this study explores the potential of QIGAs. The research reveals the transformational potential of quantum-inspired techniques through a thorough investigation of convergence speed, solution quality, and scalability.
Quantum-Inspired Genetic Algorithms (QIGAs) are a trailblazing force in the ever-evolving field of optimization, combining traditional genetic algorithms with quantum concepts to solve challenging combinatorial problems. By contrasting QIGAs with traditional Genetic Algorithms (GAs) in the setting of the Traveling Salesman Problem (TSP), this study explores the potential of QIGAs. The research reveals the transformational potential of quantum-inspired techniques through a thorough investigation of convergence speed, solution quality, and scalability.
This paper explores the application of a quantum-inspired genetic algorithm (QIGA) for solving complex optimization problems. Traditional genetic algorithms (GAs) can struggle with the intricacies of high-dimensional and non-linear problems, often encountering issues with premature convergence and local optima. This research proposes a novel approach where a standard GA is modified to incorporate principles inspired by quantum mechanics, specifically utilizing quantum-inspired concepts such as superposition and interference, to enhance the exploration and exploitation phases. The core claim is that QIGA can effectively tackle complex optimization scenarios. The mechanism involves mapping the original problem into the fitness function of the GA, and subsequently using the QIGA to perform the optimization. The results demonstrate the potential of this hybrid approach to improve solution quality and speed, particularly in problems with challenging characteristics. The research highlights a promising direction for combining classical optimization techniques with quantum-inspired ideas, offering a new tool for tackling computationally intensive optimization tasks. ---
The development in the field of quantum computing gives us a significant edge over classical computing in terms of time and efficiency.This is particularly useful for NP-hard problems such as graph layout problems.Since many real world problems are effectively solved by genetic algorithm (GA) and the performance of GA highly depends upon the setting of its parameters, therefore this paper focuses on a Quantum Inspired Genetic Algorithm (QIGA) and develops and evaluates adaptive strategies for the same.QIGA adapts ideas of Q-bits, superposition of Q-bits from quantum computing.The effectiveness and the applicability of adaptive QIGA is demonstrated by experimental results on the benchmark Knapsack, Maxcut and Onemax combinatorial optimization problems.The results show that adaptive QIGA is superior to QIGAs.
Quantum-Inspired Algorithms (QIAs) combine principles of quantum computing with classical evolutionary strategies to address complex optimization problems. This research explores the potential of QIAs in improving optimization processes, particularly in combinatorial and multi-objective optimization scenarios. The study focuses on the application of Quantum-Inspired Genetic Algorithms (QIGAs) and Quantum-Inspired Evolutionary Algorithms (QIEAs), assessing their effectiveness in solving classical problems like the Traveling Salesman Problem (TSP) and Minimum Spanning Tree (MST). Through computational simulations, the research compares the time convergence and solution accuracy of QIAs against traditional classical algorithms. The findings demonstrate that QIAs achieve faster convergence rates and higher-quality solutions, with accuracy levels reaching 98-99% of the global optimal solutions, while significantly reducing computational time. These results underline the advantages of QIAs in solving large and complex optimization problems, making them a promising alternative to traditional algorithms. Additionally, QIAs excel in avoiding local minima, a common pitfall of classical methods, due to their ability to explore the solution space more efficiently through quantum principles like superposition and interference. The implications of this study suggest that QIAs can be a valuable tool for tackling real-world optimization challenges, with potential applications in fields such as finance, logistics, telecommunications, and energy management. The research also indicates the necessity for further improvements in quantum-inspired algorithms' scalability and hardware integration, particularly for larger, more intricate optimization problems, to fully realize their potential in practical industrial applications.
This paper presents a comprehensive review of recent advancements in Genetic Quantum Algorithms (GQAs) and their application to combinatorial optimization problems.As quantum computing gains momentum, researchers are increasingly integrating quantum principles with classical metaheuristics like genetic algorithms to address NP-hard challenges such as the traveling salesman problem (TSP), job-shop scheduling, and graph coloring.This review synthesizes peer-reviewed studies published between 2023 and 2025.It highlights key innovations, performance outcomes, convergence behavior, scalability, and hardware constraints.The analysis reveals that GQAs offer significant improvements in solution accuracy, search efficiency, and robustness over classical techniques, though issues such as decoherence, quantum noise, and circuit depth remain open areas of exploration.
This paper implements a new way of solving a problem called the traveling salesman problem (TSP) using quantum genetic algorithm (QGA). We compared how well this new approach works to the traditional method known as a classical genetic algorithm (CGA). The TSP is a well-established challenge in combinatorial optimization where the objective is to find the most efficient path to visit a series of cities, minimizing the total distance, and returning to the starting point. We chose the TSP to test the performance of both algorithms because of its computational complexity and importance in practical applications. We choose the dataset from the international standard library TSPLIB for our experiments. By designing and implementing both algorithms and conducting experiments on various sizes and types of TSP instances, we provide an in-depth analysis of the accuracy of the optimal solution, the number of iterations, the execution time, and the stability of the algorithms for both. The empirical findings indicate that the CGA outperforms the QGA in terms of finding superior solutions more quickly in most of the test instances, especially when the problem size is large. This suggests that although the principle of quantum computing provides a new way to solve complex combinatorial optimisation problems, the implementation of quantum phenomena and the setting of parameters such as the optimal angle for a quantum revolving gate is challenging and need further optimisation to achieve the desired results. Additionally, it is important to note that the QGA has not been tested on real quantum hardware, so its true performance remains unverified. These limitations provide rich opportunities for further research in the future.
Quantum-Inspired Evolutionary Algorithms (QIEAs) represent a novel fusion of quantum computing principles and classical evolutionary strategies, offering a powerful approach to solving complex optimization problems. This chapter provides a comprehensive overview, detailing foundational concepts, implementation techniques, and potential applications of QIEAs. By leveraging quantum phenomena such as superposition and entanglement, QIEAs enhance the exploitation and exploration capabilities of traditional evolutionary algorithms (EAs), thus enabling an efficient search of vast solution spaces. The study examines key components of QIEA, including quantum encoding, rotation gates, and entanglement operations, and compares its performance with classical EAs. By analyzing real-life examples of combinatorial optimization problems (COPs) such as the minimum spanning tree (MST), traveling salesman problem (TSP), and graph coloring, the chapter demonstrates QIEA’s superior efficiency and solution quality. Despite implementation challenges, QIEA shows promise in various domains, including machine learning, finance, healthcare, telecommunications, and e-commerce. Future research directions are outlined, emphasizing the need for simplified quantum-inspired operators, hybrid quantum-classical systems, and interdisciplinary collaboration. This chapter highlights the transformative potential of QIEA, setting the stage for further advancements and applications in optimization and computational efficiency.