A new and efficient firefly algorithm for numerical optimization problems

作者：Pan, Xiuqin^*; Xue, Limiao; Li, Ruixiang

来源：Neural Computing & Applications, 2019, 31(5): 1445-1453.

DOI：10.1007/s00521-018-3449-6

• Firefly algorithm
• Convergence speed
• Attraction
• Adaptive parameter

Firefly algorithm (FA) is an excellent global optimizer based on swarm intelligence. Some recent studies show that FA was used to optimize various engineering problems. However, there are some drawbacks for FA, such as slow convergence rate and low precision solutions. To tackles these issues, a new and efficient FA (namely NEFA) is proposed. In NEFA, three modified strategies are employed. First, a new attraction model is used to determine the number of attracted fireflies. Second, a new search operator is designed for some better fireflies. Third, the step factor is dynamically updated during the iterations. Experiment verification is carried out on ten famous benchmark functions. Experimental results demonstrate that our new approach NEFA is superior to three other different versions of FA.

• 出版日期2019-5
• 单位中央民族大学