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Swarm-intelligence-based quantum-inspired optimization techniques for enhancing algorithmic efficiency and empirical assessment

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Abstract

Optimizing algorithmic efficiency in today’s computing landscape remains a pressing concern across various fields. This study conducts an in-depth analysis to address this challenge by leveraging quantum-inspired optimization techniques (QIOT). Specifically, the research focuses on the integration of QIOT with deep neural networks (DNNs) to enhance model performance across diverse datasets. Through extensive experimentation, ten swarm-intelligence-based optimization techniques are investigated, including Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Grey Wolf Optimizer (GWO), Firefly Algorithm (FO), Bee Colony Optimization (BCO), Cuckoo Search (CS), Moth Flame Optimization (MFO), Flower Pollination Algorithm (FPA), Whale Optimization Algorithm (WOA), and Bat Algorithm (BA). These techniques were chosen for their decentralized, collective behavior, making them particularly suitable for integration with quantum-inspired methods. Evaluation metrics such as accuracy, precision, recall, F1 score, and training time are employed in this study. Furthermore, comparative analyses demonstrate the superiority of QIOT over traditional optimization methods in enhancing algorithmic efficiency. By deploying three different datasets, the study observes a reduction in training time by 27%, 25%, and 12%, respectively, after applying quantum techniques. Additionally, the study includes comprehensive analyses such as accuracy graphs, convergence analysis, and statistical analysis to provide a thorough understanding of the proposed techniques. These findings underscore the transformative potential of quantum computing in modern computing paradigms, paving the way for future research aimed at harnessing quantum-inspired techniques across various application domains.

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Data availability

The datasets cited in this manuscript are publicly available and can be accessed from the original sources as referenced in the text. No additional data were generated or analyzed during the current study.

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Authors and Affiliations

  1. School of Information, University of California, Berkeley, CA, USA

    Ishaani Priyadarshini

  2. Data Science, Cornell University, Ithaca, USA

    Ishaani Priyadarshini

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  1. Ishaani Priyadarshini
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I I.P. worked on conceptualization, data preparation, manuscript draft, experiments, and testing. This is a single author contribution.

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Correspondence to Ishaani Priyadarshini.

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Priyadarshini, I. Swarm-intelligence-based quantum-inspired optimization techniques for enhancing algorithmic efficiency and empirical assessment. Quantum Mach. Intell. 6, 69 (2024). https://doi.org/10.1007/s42484-024-00201-z

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