摘要
人工蜂群算法具有控制参数少、复杂度低、鲁棒性强的优点,在每次迭代过程中进行全局和局部最优搜索,都具有良好的局部收敛和寻优能力,但它收敛慢,易陷入局部最优。I-ABC算法在人工蜂群算法中引入了邻域变换、轮盘赌选择、侦察蜂计数等方法。经实证分析发现,I-ABC算法在收敛性、非劣解集、多样性指标、世代距离、计算精度、收敛速度、运算效率等方面均优于人工蜂群算法,更适合解决多目标优化问题。
The artificial bee colony(hereinafter referred to as“ABC”)algorithm has the advantages of less control parameters,low complexity and strong robustness.It performs global and local optimal search in each iteration process with good local convergence and optimization ability,but it tends to fall into local optimal because of slow convergence.The I-ABC algorithm introduces such methods as neighborhood transformation,roulette selection,and reconnaissance bee counting into the ABC algorithm.The results from empirical analysis show that the I-ABC algorithm was superior to the ABC algorithm in terms of convergence,non-inferior solution set,diversity indicators,generational dis tance,computational accuracy,convergence speed and operational efficiency,more suitable for realizing multi-objective optimization.
作者
张平华
杨粟涵
ZHANG Pinghua;YANG Suhan
出处
《芜湖职业技术学院学报》
2023年第4期38-43,共6页
Journal of Wuhu Institute of Technology
基金
安徽省高等学校2020年拔尖学科(专业)人才学术资助项目“拔尖学科(专业)人才”(项目号:gxbjZD2020116)
2022年度安徽省高校自然科学研究项目“基于人工蜂群算法的城市生活垃圾回收物流网络优化设计研究”(项目号:2022AH052236)。
关键词
人工蜂群算法
智能算法
多目标约束优化
artificial bee colony algorithm
intelligent algorithms
multi-objective constraints optimization
