CAJ | 학술논문

针对基本萤火虫优化(GSO)算法在求解函数全局最优值时，存在着易陷入局部最优、收敛速度慢和求解精度低等问题，提出了1种基于生物捕食-被捕食(Predator-Prey)行为的双种群GSO算法(GSOPP)。该算法通过引入种群间的追逐与逃跑以及变异等策略加快了收敛速度，且能获得精度更高的解。最后，通过对8个标准测试函数进行测试，结果表明，改进后的 GSOPP算法比基本GSO算法有更优的性能。
침대기본형화충우화(GSO)산법재구해함수전국최우치시，존재착역함입국부최우、수렴속도만화구해정도저등문제，제출료1충기우생물포식-피포식(Predator-Prey)행위적쌍충군GSO산법(GSOPP)。해산법통과인입충군간적추축여도포이급변이등책략가쾌료수렴속도，차능획득정도경고적해。최후，통과대8개표준측시함수진행측시，결과표명，개진후적 GSOPP산법비기본GSO산법유경우적성능。
s: According to the basic glowworm swarm optimization (GSO) algorithm in solving the function of global optimal value existing some problems, such as easy to fall into local optimum, slow convergence and low precision, an artificial glowworm swarm optimization algorithm based biological predator-prey behavior (GSOPP) is proposed. The algorithm through populations chase and escape, and the mutation strategy to speed up the convergence rate, and can obtain a more accurate solution. Finally, the test results of 8 standard test functions show that, the improved GSOPP algorithm than the basic GSO algorithm has Better performance.