灰狼优化算法的改进及其应用  

Improvement and Application of Grey Wolf Optimization Algorithm

作　　者：邓飞 魏祎璇 刘奕巧 王统照 Deng Fei;Wei Yixuan;Liu Yiqiao;Wang Tongzhao(School of Economics and Management,North China Institute of Science and Technology,Langfang Hebei 065201,China;Research Institute of Macro-safety Science,University of Science and Technology Beijing,Beijing 100083,China;School of Mechanical Engineering,University of Malaya,Kuala Lumpur Malaysia 50603;Fluid and Thermal Engineering Research Center,University of Nottingham Ningbo,Ningbo Zhejiang 315100,China)

机构地区：[1]华北科技学院经济管理学院,河北廊坊065201 [2]北京科技大学大安全科学研究院,北京100083 [3]马来亚大学机械工程学院,马来西亚吉隆坡50603 [4]宁波诺丁汉大学流体与热工程研究中心,浙江宁波315100

出　　处：《统计与决策》2023年第11期18-24,共7页Statistics & Decision

基　　金：全国统计科学研究项目(2022LY081);中央高校基本科研业务费专项资金资助项目(3142023044)。

摘　　要：灰狼优化算法因为具有调节参数少、结构简化易于程序实现、求解精度高等优点,被广泛应用于各领域。文章针对目前灰狼优化算法存在的缺陷,提出离散编码、解码策略以适配优化离散组合问题;引入遗传算法中的交叉操作和大规模邻域搜索算法中的破坏修复操作,在弥补全局搜索能力不足的同时,进一步加强局部搜索能力;融入NSGA-Ⅱ框架,实现多目标优化复杂问题的求解;以洪涝灾害中的两类无人机应急救援任务为应用场景,来验证所提出的两种改进算法的有效性。两种改进算法分别求解单目标和多目标泛化多旅行商问题的实验结果表明,相较于其他智能优化算法,改进算法在求解精度、解集优劣程度和多样性方面具有明显优势。Grey wolf optimization algorithm is widely used in various fields because of its advantages of less adjustment pa⁃rameters,simple structure,easy program implementation and high accuracy.Aiming at the shortcomings of the current gray wolf optimization algorithm,this paper proposes a discrete coding and decoding strategy so as to adapt to and optimize the discrete combination,introduces cross operation in genetic algorithm and damage repair operation in large-scale neighborhood search to make up for the insufficient global search ability and further strengthen the local search ability,and finally integrates the NS⁃GA-Ⅱframework to solve complex multi-objective optimization problems.Two kinds of UAV emergency rescue tasks in flood di⁃sasters are used as application scenarios to verify the effectiveness of the two proposed algorithms.The experimental results of the two improved algorithms for single-objective and multi-objective generalized multiple traveling salesman problems(MTSP)show that the two improved algorithms have obvious advantages over other intelligent optimization algorithms in terms of solution accu⁃racy,solution set quality and diversity.

关 键 词：灰狼优化算法 离散组合问题 泛化多旅行商问题 算法性能对比 

分 类 号：U116.2[交通运输工程]