水准点(测量)
计算机科学
数学优化
进化算法
元启发式
优化算法
最优化问题
正规化(语言学)
多目标优化
人工智能
算法
机器学习
数学
地理
大地测量学
作者
Hernán Peraza-Vázquez,Adrián F. Peña-Delgado,Gustavo Echavarría-Castillo,Ana Beatriz Morales‐Cepeda,Jonás Velasco,F. Ruiz-Perez
摘要
A novel bio-inspired algorithm, namely, Dingo Optimization Algorithm (DOA), is proposed for solving optimization problems. The DOA mimics the social behavior of the Australian dingo dog. The algorithm is inspired by the hunting strategies of dingoes which are attacking by persecution, grouping tactics, and scavenging behavior. In order to increment the overall efficiency and performance of this method, three search strategies associated with four rules were formulated in the DOA. These strategies and rules provide a fine balance between intensification (exploitation) and diversification (exploration) over the search space. The proposed method is verified using several benchmark problems commonly used in the optimization field, classical design engineering problems, and optimal tuning of a Proportional-Integral-Derivative (PID) controller are also presented. Furthermore, the DOA’s performance is tested against five popular evolutionary algorithms. The results have shown that the DOA is highly competitive with other metaheuristics, beating them at the majority of the test functions.
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