Multi-strategy quantum particle swarm optimization for efficient path planning of mobile robots

The quantum-behaved particle swarm optimization (QPSO) algorithm utilizes quantum probability density functions to guide particles toward optimal states, rendering it an effective tool for mobile robot path planning. However, conventional QPSO algorithms often struggle with issues such as suboptimal convergence precision and a tendency to become trapped in local optima. To surmount these obstacles, this study proposes a multi-strategy QPSO scheme, termed MS-QPSO, which integrates a suite of innovative tactics, including a nonlinear cosine decreasing function, an improved local attractor mechanism, a novel particle position updating approach, and a robot trajectory smoothing technique. These strategies synergize to enhance accuracy, accelerate convergence, and strengthen the ability to escape local optima, resulting in a substantial improvement in mobile robot path planning. The effectiveness of the MS-QPSO algorithm is comprehensively validated using experiments on 49 benchmark test functions and two-dimensional grid maps of varying complexity. Experimental results indicate that MS-QPSO outperforms competing algorithms on 33 out of the 49 benchmarks, including classical test functions as well as those from the CEC-2017 and CEC-2020 test suites. In grid map path planning, MS-QPSO consistently achieves superior performance, reducing the path length by 23.8% (from 96.094 to 73.244) compared to classical QPSO, which demonstrates its significant advantages in both efficiency and path quality.