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<p>This paper presents an improved triangular topology aggregation optimizer (AITTAO) for solving global optimization and engineering design problems. This improvement aims to solve engineering optimization problems in the real world. First, a cyclic step exploration and exploitation strategy (CSEES) is proposed; second, a dynamic disturbance exact elimination strategy (DDEES) is proposed; third, the greedy learning strategy (GLS) is proposed; fourth, the piecewise mapping method is adopted to initialize the population. The performance of AITTAO compared with 13 algorithms PSO, GSA, GWO, AVOA, COA, GTO, ABC, AO, DE, GA, HHO, OMA, and TTAO on CEC2017 functions, the effectiveness of improved algorithms was verified by the Wilcoxon rank sum test. The experimental results show that the TTAO algorithm obtains the 16, 17, and 18 best average results compared with the 13 compared algorithms on 30, 50, and 100 dimensions for 29 CEC2017 functions, respectively. In addition, the performance of the AITTAO algorithm was compared with 10 algorithms (WSO, SFOA, LSHADE, JAD, OIO, RIME, IAO, INFO.ESC, ITTAO) on the CEC2017 functions, and the effectiveness of the improved algorithm was verified by the Friedman mean rank test. The experimental results show that the AITTAO algorithm performs the best. The improved algorithm solved six engineering problems, two constrained problems, and the PID parameter optimization problem.</p>

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AITTAO: an improved triangular topology aggregation optimizer for parameter optimization and real-world problem processing

  • 期刊论文

  • 发表日期：

    2025年9月19日

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  • Langlang Zhang,
  • Ben Niu,
  • Haisong Huang,
  • Yongpeng Zhao,
  • Jianlin Liu

摘要

This paper presents an improved triangular topology aggregation optimizer (AITTAO) for solving global optimization and engineering design problems. This improvement aims to solve engineering optimization problems in the real world. First, a cyclic step exploration and exploitation strategy (CSEES) is proposed; second, a dynamic disturbance exact elimination strategy (DDEES) is proposed; third, the greedy learning strategy (GLS) is proposed; fourth, the piecewise mapping method is adopted to initialize the population. The performance of AITTAO compared with 13 algorithms PSO, GSA, GWO, AVOA, COA, GTO, ABC, AO, DE, GA, HHO, OMA, and TTAO on CEC2017 functions, the effectiveness of improved algorithms was verified by the Wilcoxon rank sum test. The experimental results show that the TTAO algorithm obtains the 16, 17, and 18 best average results compared with the 13 compared algorithms on 30, 50, and 100 dimensions for 29 CEC2017 functions, respectively. In addition, the performance of the AITTAO algorithm was compared with 10 algorithms (WSO, SFOA, LSHADE, JAD, OIO, RIME, IAO, INFO.ESC, ITTAO) on the CEC2017 functions, and the effectiveness of the improved algorithm was verified by the Friedman mean rank test. The experimental results show that the AITTAO algorithm performs the best. The improved algorithm solved six engineering problems, two constrained problems, and the PID parameter optimization problem.