Improved puzzle optimization algorithm with multi-strategy integration and its application
Xiaogang Liu, Wenbo Gao · 2022
Aiming at the problems of low solution accuracy, slow convergence speed and easy to fall into local optimization, a multi-strategy fusion improved puzzle optimization algorithm (IPOA) is proposed. Improved Circle mapping strategy, dwarf mongoose optimization (DMO) strategy, and adaptive t-distribution variation strategy were used to increase the diversity of puzzle populations, improve global search capabilities and local development performance. Through the analysis of multiple benchmark function simulation experiments and PID controller parameter optimization problems, the results show that the IPOA algorithm has high optimization accuracy, fast convergence speed and effectiveness in solving practical engineering problems.