CAJ | 학술논문

为了提高萤火虫算法GSO（Glowworm Swarm Optimization algorithm）多模函数优化性能，针对GSO峰值发现率低、收敛速度慢和求解精度不高的缺点，提出萤火虫个体可自适应搜索峰值且移动步长可变的改进萤火虫算法 IGSO （Improved Glowworm Swarm Optimization algorithm）。IGSO引入尝试性移动策略以增强算法的搜索能力，同时，以邻域平均距离为参考，对个体移动步长进行调整。采用典型多模函数进行测试，实验结果表明，I GS O峰值发现率高，收敛速度快且求解精度高，比GS O具有更优的多模函数优化性能。
위료제고형화충산법GSO（Glowworm Swarm Optimization algorithm）다모함수우화성능，침대GSO봉치발현솔저、수렴속도만화구해정도불고적결점，제출형화충개체가자괄응수색봉치차이동보장가변적개진형화충산법 IGSO （Improved Glowworm Swarm Optimization algorithm）。IGSO인입상시성이동책략이증강산법적수색능력，동시，이린역평균거리위삼고，대개체이동보장진행조정。채용전형다모함수진행측시，실험결과표명，I GS O봉치발현솔고，수렴속도쾌차구해정도고，비GS O구유경우적다모함수우화성능。
In order to improve the performance of multimodal function optimisation with glowworm swarm optimisation (GSO),and to solve the problems of GSO in low peaks discovery rate,slow convergence speed and low computational accuracy,we propose an improved glowworm swarm optimisation (IGSO),in which the individual glowworm (agent)can adaptively search the peaks,and its moving step is variable.The IGSO introduces the tentative moving strategy to enhance the searching ability of the algorithm,and meantime it uses average neighbourhood distance as the reference to adjust agent’s moving step.The results of experiment on typical multimodal functions indicate that the IGSO is superior to GSO in multimodal function optimisation with high peaks discovery rate,fast convergence speed and high computational accuracy.