Schrodinger optimizer: A quantum duality-driven metaheuristic for stochastic optimization and engineering challenges

Abstract

This paper introduces the Schrodinger Optimizer (SRA), a new metaheuristic algorithm motivated by principles of quantum mechanics, specifically Schrodinger's equation and wave-particle duality. SRA possesses a twin update mechanism that balances probabilistic exploration and deterministic exploitation, facilitating effective navigation in high-dimensional, intricate search spaces. The algorithm was extensively tested on benchmark suites such as CEC 2019 (low-dimensional), CEC 2017 (50D and 100D), CEC 2022 (20D), and eight real-world engineering design optimization problems. Comparison tests with state-of-the-art physics-inspired and advanced metaheuristic algorithms revealed SRA's superior performance. In the 100D CEC 2017 benchmark, SRA ranked the best average rank (1.87) among the physics-based algorithms and performed better than its rivals on 20 of the 29 functions. It also performed best (2.92) among emerging metaheuristic variants. Statistical tests (Friedman and Wilcoxon signed rank) confirmed the significance of these results. In engineering applications, SRA consistently obtained better solutions with fewer computations. These findings accentuate SRA's potential in solving complex optimization problems efficiently. This study opens up new possibilities for powerful and versatile optimization methods through the integration of quantum-inspired concepts into the metaheuristic paradigm. The source code is available at https://github.com/MohammedQaraad/SRA/blob/main/SRA_framework.ipynb