Multi-Strategy Enhanced Slime Mould Algorithm for Optimization Problems

The slime mould algorithm (SMA) simulates the mechanism by which slime moulds optimize paths through chemical signaling and morphological changes, enabling efficient exploration and exploitation of the solution space. While SMA is simple and flexible, it faces challenges such as slow convergence and a tendency to become trapped in local optima. To address these limitations, this paper introduces an enhanced algorithm that integrates bloch sphere-based Elite Population Initialization with an adaptive search operator strategy based on cauchy inverse cumulative distribution(QCMSMA). The proposed algorithm employs a Bloch sphere-based elite population initialization strategy, which utilizes quantum state mapping to enhance diversity and incorporates elite selection to guarantee high-quality initial solutions, ultimately improving optimization performance. An adaptive search operator leveraging the Cauchy inverse cumulative distribution is employed to dynamically adjust step sizes, improving exploration and efficiency. Additionally, a local Gaussian perturbation mutation strategy is incorporated to mitigate the risk of premature convergence to local optima.The QCMSMA algorithm was rigorously evaluated using 23 benchmark functions and the CEC2017 test suite. Comparative analysis against several well-known optimization algorithms was performed, accompanied by statistical assessments using theWilcoxon rank-sum test and Friedman ranking analysis. Experimental results indicate that QCMSMA consistently outperforms its counterparts in terms of optimization efficiency, convergence speed, and stability. Finally, the algorithm was applied to a real-world unmanned aerial vehicle(UAV) path planning problem, demonstrating its practical engineering applicability and effectiveness in solving complex optimization tasks.