水准点(测量)
趋同(经济学)
人工蜂群算法
算法
计算机科学
数学优化
局部最优
过程(计算)
混乱的
蜜蜂算法
收敛速度
人工智能
数学
元启发式
钥匙(锁)
大地测量学
地理
经济
经济增长
操作系统
计算机安全
作者
Chenjun Tang,Wei Sun,Min Xue,Wei Sun,Hongwei Tang,Wei Wu
出处
期刊:Soft Computing
[Springer Nature]
日期:2022-01-28
卷期号:26 (5): 2075-2097
被引量:32
标识
DOI:10.1007/s00500-021-06623-2
摘要
In this paper, the defects and deficiencies of the recently proposed whale optimization algorithm (WOA) are improved. A whale optimization algorithm mixed with an artificial bee colony (ACWOA) is proposed to solve the WOA problems of slow convergence, low precision, and easy to fall into local optimum. The ACWOA algorithm integrates the artificial bee colony algorithm and chaotic mapping, effectively avoiding the local optimal situation and improving the quality of the initial solution. Also, nonlinear convergence factors and adaptive inertia weight coefficients are added to accelerate the convergence rate. To verify the performance of the improved algorithm, 20 benchmark functions and CEC2019 multimodal multi-objective benchmark functions have been used to compare ACWOA with the classical intelligent population algorithms (PSO, MVO, and GWO) and the recent state-of-the-art algorithms (CWOA, HWPSO, and HIWOA) in recent years. The proposed algorithm is applied to two well-known engineering mathematical models and a real application (the quality process control). The experiments show that the ACWOA algorithm has strong competitiveness in convergence speed and solution accuracy and has certain practical value in complex mathematical model scenarios.
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