融合天鹰勘探思想的鲸鱼优化算法改进  

Whale Optimization Algorithm with Aquila Exploration Method

作　　者：齐欣 于延[1] 马宁 吴昊谦 QI Xin;YU Yan;MA Ning;WU Hao-qian(Harbin Normal University,Harbin 150500,Heilongjiang)

机构地区：[1]哈尔滨师范大学计算机科学与信息工程学院,黑龙江哈尔滨150500

出　　处：《电脑与电信》2023年第11期7-13,共7页Computer & Telecommunication

基　　金：教育部产学合作协同育人项目,项目编号:220601311200013;黑龙江省高等教育教学改革项目,项目编号:SJGY20220350;哈尔滨师范大学高等教育教学改革研究项目,项目编号:XJGYFW2022017、JKYJGY202201。

摘　　要：鲸鱼优化器(WOA)是一种有效的元启发式算法。但鲸鱼优化算法往往收敛速度慢并且容易陷入局部最优解。因此,提出了基于天鹰优化算法(AO)的勘探思想改进的算法来解决全局优化问题。改进后的算法受到了天鹰优化算法勘探思想的启发,首先,天鹰先进行勘探,扩大了搜索范围,以提高全局搜索能力并降低陷入局部最优的可能性;其次,鲸鱼游动围猎,以此平衡算法勘探和开发两个阶段。为了验证算法的有效性,本文算法以基准测试函数为实验对象,与其他流行的元启发式算法进行比较。实验结果证明本文所提出的算法具有良好的收敛速度、寻优精度和稳定性。The Whale Optimization Algorithm(WOA)is an effective meta-heuristic.However,whale optimization algorithms tend to converge slowly and are prone to fall into local optimal solutions.Therefore,this paper proposes an algorithm based on the exploration idea of the Aquila Optimizer(AO)to solve global optimization problems.The improved algorithm is inspired by the exploration idea of the Aquila Optimizer.Firstly,Aquilas conduct exploration to expand the search range,thereby enhancing the global search capability and reducing the likelihood of getting trapped in local optima.Secondly,it incorporates the hunting behavior of whales to balance the exploration and exploitation phases of the algorithm.To validate the effectiveness of the algorithm,this study uses benchmark test functions as experimental objects and compares it with other popular metaheuristic algorithms.The experimental results demonstrate that the proposed algorithm in this study has shown good convergence speed,optimization accuracy,and stability.

关 键 词：鲸鱼优化算法 天鹰优化算法 全局优化 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]