Chaotic dynamic weight grey wolf optimizer for numerical function optimization

2019 ◽  
Vol 37 (2) ◽  
pp. 2367-2384 ◽  
Author(s):  
Jianzhong Xu ◽  
Fu Yan ◽  
Oluwafolakemi Grace Ala ◽  
Lifei Su ◽  
Fengshu Li
Keyword(s):  
Chaotic Dynamic ◽  
Function Optimization ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Numerical Function ◽  
Dynamic Weight ◽  
Numerical Function Optimization

Inspired grey wolf optimizer for solving large-scale function optimization problems

2018 ◽  
Vol 60 ◽  
pp. 112-126 ◽  
Author(s):  
Wen Long ◽  
Jianjun Jiao ◽  
Ximing Liang ◽  
Mingzhu Tang
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Function Optimization ◽  
Grey Wolf Optimizer ◽  
Scale Function ◽  
Grey Wolf

scholarly journals Gaussian Quantum Bat Algorithm with Direction of Mean Best Position for Numerical Function Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Xingwang Huang ◽  
Chaopeng Li ◽  
Yunming Pu ◽  
Bingyan He
Keyword(s):  
Probability Distribution ◽  
Bat Algorithm ◽  
Random Coefficients ◽  
Function Optimization ◽  
Premature Convergence ◽  
Local Optimum ◽  
Search Range ◽  
Numerical Function ◽  
Numerical Function Optimization ◽  
Gaussian Probability Distribution

Quantum-behaved bat algorithm with mean best position directed (QMBA) is a novel variant of bat algorithm (BA) with good performance. However, the QMBA algorithm generates all stochastic coefficients with uniform probability distribution, which can only provide a relatively small search range, so it still faces a certain degree of premature convergence. In order to help bats escape from the local optimum, this article proposes a novel Gaussian quantum bat algorithm with mean best position directed (GQMBA), which applies Gaussian probability distribution to generate random number sequences. Applying Gaussian distribution instead of uniform distribution to generate random coefficients in GQMBA is an effective technique to promote the performance in avoiding premature convergence. In this article, the combination of QMBA and Gaussian probability distribution is applied to solve the numerical function optimization problem. Nineteen benchmark functions are employed and compared with other algorithms to evaluate the accuracy and performance of GQMBA. The experimental results show that, in most cases, the proposed GQMBA algorithm can provide better search performance.

Artificial bee colony algorithm with gene recombination for numerical function optimization

2017 ◽  
Vol 52 ◽  
pp. 146-159 ◽  
Author(s):  
Genghui Li ◽  
Laizhong Cui ◽  
Xianghua Fu ◽  
Zhenkun Wen ◽  
Nan Lu ◽  
...  
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Function Optimization ◽  
Gene Recombination ◽  
Numerical Function ◽  
Bee Colony ◽  
Numerical Function Optimization
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