An Improved Advanced Social Memory Optimization Framework for Global Optimization Problem and UAV Obstacle Avoidance Trajectory Planning
4 affiliations
- 1Information Engineering School, Shandong Youth University of Political Science, Jinan 250103, China
- 2School of Software, Shandong University, Jinan 250101, China
- 3School of Engineering and Applied Science, University of California, Los Angeles, CA 90095, USA
- 4School of Qilu Transportation, Shandong University, Jinan 250061, China
Abstract
This paper addresses the limitations of the Advanced Social Memory Optimization (ASMO) algorithm in solving high-dimensional complex optimization problems, such as premature convergence, insufficient population diversity, and limited local search capability. To overcome these issues, an Improved Advanced Social Memory Optimization (IASMO) algorithm is proposed. By incorporating a better–worse collaborative guidance strategy, an adaptive Cauchy–Gaussian exploitation strategy, and a financial investment strategy, the proposed algorithm aims to enhance global exploration and local exploitation while improving search stability through a return–risk balancing mechanism. Experimental results on the IEEE CEC2017 (100-dimensional) and CEC2022 (10- and 20-dimensional) benchmark functions demonstrate that IASMO achieves competitive overall optimization performance. The Friedman average ranking results show that IASMO obtains mean ranks of 1.63, 2.42, and 1.83 on CEC2017, CEC2022 (10D), and CEC2022 (20D), respectively, ranking first among the compared algorithms in all three benchmark settings. The Wilcoxon rank-sum test provides further statistical evidence of performance differences between IASMO and the competing algorithms. To further evaluate its applicability to engineering optimization problems, IASMO is applied to three-dimensional unmanned aerial vehicle (UAV) obstacle avoidance trajectory planning and hybrid microgrid economic dispatch. In the UAV path planning experiment, IASMO achieved the lowest mean path cost of 292.5748, representing a reduction of approximately 16.91% compared with the original ASMO. In the microgrid economic dispatch problem, IASMO an average operating cost of 1.2102E+06, which is lower than those of ESLPSO (2.1004E+06), EGWO (4.8763E+06), and the original ASMO (5.5609E+07). Overall, the experimental findings indicate that IASMO provides a competitive optimization approach for complex global optimization problems, UAV trajectory planning, and microgrid economic dispatch.
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BibTeX
@article{zhang2026improved,
title = {{An Improved Advanced Social Memory Optimization Framework for Global Optimization Problem and UAV Obstacle Avoidance Trajectory Planning}},
author = {Meng Zhang and Ruibo Sun and Lei Kou},
journal = {Mathematics},
year = {2026},
month = oct,
doi = {10.3390/math14213955},
url = {https://www.mdpi.com/2227-7390/14/21/3955},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - An Improved Advanced Social Memory Optimization Framework for Global Optimization Problem and UAV Obstacle Avoidance Trajectory Planning
AU - Meng Zhang
AU - Ruibo Sun
AU - Lei Kou
PY - 2026
DA - 2026/10/29
JO - Mathematics
DO - 10.3390/math14213955
UR - https://www.mdpi.com/2227-7390/14/21/3955
AB - This paper addresses the limitations of the Advanced Social Memory Optimization (ASMO) algorithm in solving high-dimensional complex optimization problems, such as premature convergence, insufficient population diversity, and limited local search capability. To overcome these issues, an Improved Advanced Social Memory Optimization (IASMO) algorithm is proposed. By incorporating a better–worse collaborative guidance strategy, an adaptive Cauchy–Gaussian exploitation strategy, and a financial investment strategy, the proposed algorithm aims to enhance global exploration and local exploitation while improving search stability through a return–risk balancing mechanism. Experimental results on the IEEE CEC2017 (100-dimensional) and CEC2022 (10- and 20-dimensional) benchmark functions demonstrate that IASMO achieves competitive overall optimization performance. The Friedman average ranking results show that IASMO obtains mean ranks of 1.63, 2.42, and 1.83 on CEC2017, CEC2022 (10D), and CEC2022 (20D), respectively, ranking first among the compared algorithms in all three benchmark settings. The Wilcoxon rank-sum test provides further statistical evidence of performance differences between IASMO and the competing algorithms. To further evaluate its applicability to engineering optimization problems, IASMO is applied to three-dimensional unmanned aerial vehicle (UAV) obstacle avoidance trajectory planning and hybrid microgrid economic dispatch. In the UAV path planning experiment, IASMO achieved the lowest mean path cost of 292.5748, representing a reduction of approximately 16.91% compared with the original ASMO. In the microgrid economic dispatch problem, IASMO an average operating cost of 1.2102E+06, which is lower than those of ESLPSO (2.1004E+06), EGWO (4.8763E+06), and the original ASMO (5.5609E+07). Overall, the experimental findings indicate that IASMO provides a competitive optimization approach for complex global optimization problems, UAV trajectory planning, and microgrid economic dispatch.
SN - 2227-7390
ER -