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International Journal of Advanced Research in Computer Science and Engineering

Quantum-Inspired Genetic Algorithms for Large-Scale Optimization

Andrei Popescu

Published Apr 4, 2026• 0 citations
OpenAlex

Abstract

Large-scale optimization (LSO) problems—those with hundreds to thousands of decision variables—are prevalent in engineering design, logistics, telecommunications, and machine learning hyperparameter search. Classical evolutionary algorithms often struggle in this regime due to premature convergence and prohibitive search costs. This manuscript presents a Quantum-Inspired Genetic Algorithm (QIGA) that transfers three ideas from quantum computation into a purely classical algorithm: (i) Q-bit angle encoding that maintains a compact, uncertainty-aware distribution over decision values, (ii) rotation-gate adaptation that updates search probabilities toward promising regions while preserving exploration, and (iii) quantum observation that draws diverse candidate solutions from the encoded amplitudes at each generation. We further couple the core QIGA with (a) an island-model decomposition for ultra-high dimensionality, (b) surrogate-assisted local refinement for exploitation at low cost, and (c) a feasibility-aware penalty for constraints. We evaluate the approach on five canonical continuous benchmarks in 1,000 dimensions (Sphere, Rosenbrock, Rastrigin, Ackley, Griewank) under a fixed computational budget. Across 30 independent runs per function, QIGA achieves lower final objective values and higher success rates than classical GA, PSO, and DE baselines. A non-parametric Wilcoxon signed-rank test indicates statistically significant improvements for QIGA (adjusted p < 0.01) on all functions. We analyze the algorithm’s scaling behavior, the role of angle-space step control, and the contribution of island-level diversity. The results suggest that quantum-inspired probability evolution provides a robust inductive bias for LSO by decoupling representation capacity from point estimates and by enabling distribution-aware search moves. We conclude with practical guidelines for tuning and discuss extensions to constrained and mixed-variable problems.

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•Sep 6, 2026•0 citations
Large-scale optimization (LSO) problems—those with hundreds to thousands of decision variables—are prevalent in engineering design, logistics, telecommunications, and machine learning hyperparameter search. Classical evolutionary algorithms often struggle in this regime due to premature convergence and prohibitive search costs. This manuscript presents a Quantum-Inspired Genetic Algorithm (QIGA) that transfers three ideas from quantum computation into a purely classical algorithm: (i) Q-bit angle encoding that maintains a compact, uncertainty-aware distribution over decision values, (ii) rotation-gate adaptation that updates search probabilities toward promising regions while preserving exploration, and (iii) quantum observation that draws diverse candidate solutions from the encoded amplitudes at each generation. We further couple the core QIGA with (a) an island-model decomposition for ultra-high dimensionality, (b) surrogate-assisted local refinement for exploitation at low cost, and (c) a feasibility-aware penalty for constraints. We evaluate the approach on five canonical continuous benchmarks in 1,000 dimensions (Sphere, Rosenbrock, Rastrigin, Ackley, Griewank) under a fixed computational budget. Across 30 independent runs per function, QIGA achieves lower final objective values and higher success rates than classical GA, PSO, and DE baselines. A non-parametric Wilcoxon signed-rank test indicates statistically significant improvements for QIGA (adjusted p < 0.01) on all functions. We analyze the algorithm’s scaling behavior, the role of angle-space step control, and the contribution of island-level diversity. The results suggest that quantum-inspired probability evolution provides a robust inductive bias for LSO by decoupling representation capacity from point estimates and by enabling distribution-aware search moves. We conclude with practical guidelines for tuning and discuss extensions to constrained and mixed-variable problems.
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. ---
This research explores the application of a Quantum-Inspired Genetic Algorithm (QIGA) to optimize complex systems, utilizing a numerical experiment with a focus on the objective function... The QIGA integrates quantum-inspired principles, including crossover, entanglement, and evolution, to strike a balance between exploration and exploitation within the solution space. A 100-generation experiment with a population size of 50 reveals the algorithm's adaptability and gradual convergence towards optimal solutions. The linear combination crossover, guided by quantum principles, enhances diversity, while entanglement and evolution operations introduce correlations between quantum states. The results underscore the algorithm's potential, prompting discussions on parameter tuning, comparisons with classical algorithms, and considerations for transitioning to real quantum hardware. The findings contribute to the understanding of quantum-inspired optimization and pave the way for further research in quantum computing applications for complex system optimization.
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.
As modern data environments continue to scale in volume and dimensionality, classical metaheuristics like Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) struggle to balance convergence efficiency with solution quality. The challenge is further amplified in big data optimization, where stagnation in local minima and prohibitive computation time impede real-time applicability. Existing methods fail to simultaneously address the need for global search intelligence and computational scalability, especially under high-dimensional constraints and distributed workloads. To bridge this gap, we propose QIESO—a Quantum-Inspired Evolutionary Swarm Optimizer that integrates quantum amplitude encoding, probabilistic gate rotation, swarm coordination, and tunnelingbased mutation to escape local traps and accelerate convergence. Unlike traditional optimizers, QIESO leverages quantum principles simulated classically to maintain coherence across the search space while reducing evaluation overhead. Empirical validation across datasets up to 100 million records shows QIESO achieves a 51.7% reduction in runtime over GA and 42.7% faster convergence than PSO, with a relative error margin consistently under 2.1%. The method exhibits sublinear scalability in time complexity due to dimensionality-agnostic encoding. This work significantly advances scalable optimization for high-throughput environments, making it ideal for applications in autonomous systems, real-time analytics, and next-gen intelligent infrastructures.
Purpose: The development of engineering decision support systems requires effective optimization techniques to deal with complex optimization problems such as energy management, logistics optimization, and resource management. The most popular classical optimization techniques, such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are often less effective in terms of optimization performance and are often likely to suffer from slow optimization speed and premature convergence to local optima when dealing with highdimensional optimization search space. The quantum-inspired optimization (QIO) techniques are an emerging class of optimization techniques that attempt to mimic some principles of quantum computing on classical computers to overcome the limitations of classical optimization techniques. The purpose of this research is to develop a quantum-inspired optimization framework to overcome the limitations of classical optimization techniques in engineering decision support systems. Method: The proposed optimization framework incorporates a probabilistic amplitude representation of solutions, rotation-based evolutionary optimization, and simulated annealing acceptance criteria. The optimization framework was tested on two optimization scenarios: a highdimensional optimization problem inspired by a Rastrigin-like function representing energy management optimization and a multi-objective optimization problem inspired by logistics optimization with the goal of minimizing transportation costs and time. The optimization performance and effectiveness of the proposed optimization framework were compared with classical optimization techniques such as GA and PSO. Findings: The proposed optimization framework outperforms traditional methods such as GA and PSO in terms of speed and quality of results. In the high-dimensional problem, the proposed framework achieved an optimal value of 38.96, which is better compared to the results of PSO, which achieved 69.42, and GA, which achieved 137.22. In the multi-objective problem, the proposed framework achieved Pareto results, which show better performance and effectiveness compared to the traditional methods. Conclusion: The optimization results indicate that the quantum-inspired optimization framework is an effective optimization tool that can be used to overcome the limitations of classical optimization techniques in engineering decision support systems. The proposed optimization framework provides a link between classical optimization techniques and emerging quantum optimization techniques while being able to run on classical computers.
Large-scale optimization problems, prevalent in diverse fields such as machine learning, logistics, and engineering design, pose significant challenges to classical optimization algorithms. These algorithms often suffer from slow convergence rates, premature convergence, and difficulty in exploring complex solution spaces. This paper explores the application of quantum-inspired search algorithms to address these limitations. We introduce a novel approach that draws inspiration from quantum measurement theory, specifically superposition and interference, to enhance the exploration capabilities of classical optimization techniques. The core idea is to mimic the probabilistic nature of quantum measurements to navigate the solution space more effectively. This approach allows for a more efficient exploration of potential solutions, leading to improved convergence rates and the ability to tackle larger and more complex problems. The algorithms presented are designed to be implementable within standard computational environments without requiring specialized quantum hardware. The performance of these algorithms is evaluated through simulations, demonstrating their potential to outperform traditional optimization methods in specific scenarios.