An Estimation of Distribution Algorithm With Multi-Leader Search
The estimation of distribution algorithm (EDA) is a well-known stochastic search method but is easily affected by the ill-shaped distribution of solutions and can thus become trapped in stagnation. In this paper, we propose a novel modified EDA with a multi-leader search (MLS) mechanism, namely, the...
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doaj-3bbde7130c884f16bffeea08e81061662021-03-30T02:30:27ZengIEEEIEEE Access2169-35362020-01-018373833740510.1109/ACCESS.2020.29754689005218An Estimation of Distribution Algorithm With Multi-Leader SearchXiaofei Wang0https://orcid.org/0000-0002-8482-6138Tong Han1Hui Zhao2Aeronautics Engineering College, Air Force Engineering University, Xi’an, ChinaAeronautics Engineering College, Air Force Engineering University, Xi’an, ChinaAeronautics Engineering College, Air Force Engineering University, Xi’an, ChinaThe estimation of distribution algorithm (EDA) is a well-known stochastic search method but is easily affected by the ill-shaped distribution of solutions and can thus become trapped in stagnation. In this paper, we propose a novel modified EDA with a multi-leader search (MLS) mechanism, namely, the MLS-EDA. To strengthen the exploration performance, an enhanced distribution model that considers the information of population and distribution is utilized to generate new candidates. Moreover, when the algorithm stagnates, the MLS mechanism will be activated to perform a local search and shrink the search scope. The performance of the MLS-EDA in addressing complex optimization problems is verified using the CEC 2014 and CEC 2017 testbeds with 30D, 50D and 100D tests. Several modern algorithms, including the top-performing methods in the CEC 2014 and CEC 2017 competitions, are considered as competitors. The competitive performance of our proposed MLS-EDA is discussed based on the comparison results.https://ieeexplore.ieee.org/document/9005218/Estimation of distribution algorithmreal-numerical optimizationCEC 2014CEC 2017evolutionary computation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaofei Wang Tong Han Hui Zhao |
spellingShingle |
Xiaofei Wang Tong Han Hui Zhao An Estimation of Distribution Algorithm With Multi-Leader Search IEEE Access Estimation of distribution algorithm real-numerical optimization CEC 2014 CEC 2017 evolutionary computation |
author_facet |
Xiaofei Wang Tong Han Hui Zhao |
author_sort |
Xiaofei Wang |
title |
An Estimation of Distribution Algorithm With Multi-Leader Search |
title_short |
An Estimation of Distribution Algorithm With Multi-Leader Search |
title_full |
An Estimation of Distribution Algorithm With Multi-Leader Search |
title_fullStr |
An Estimation of Distribution Algorithm With Multi-Leader Search |
title_full_unstemmed |
An Estimation of Distribution Algorithm With Multi-Leader Search |
title_sort |
estimation of distribution algorithm with multi-leader search |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
The estimation of distribution algorithm (EDA) is a well-known stochastic search method but is easily affected by the ill-shaped distribution of solutions and can thus become trapped in stagnation. In this paper, we propose a novel modified EDA with a multi-leader search (MLS) mechanism, namely, the MLS-EDA. To strengthen the exploration performance, an enhanced distribution model that considers the information of population and distribution is utilized to generate new candidates. Moreover, when the algorithm stagnates, the MLS mechanism will be activated to perform a local search and shrink the search scope. The performance of the MLS-EDA in addressing complex optimization problems is verified using the CEC 2014 and CEC 2017 testbeds with 30D, 50D and 100D tests. Several modern algorithms, including the top-performing methods in the CEC 2014 and CEC 2017 competitions, are considered as competitors. The competitive performance of our proposed MLS-EDA is discussed based on the comparison results. |
topic |
Estimation of distribution algorithm real-numerical optimization CEC 2014 CEC 2017 evolutionary computation |
url |
https://ieeexplore.ieee.org/document/9005218/ |
work_keys_str_mv |
AT xiaofeiwang anestimationofdistributionalgorithmwithmultileadersearch AT tonghan anestimationofdistributionalgorithmwithmultileadersearch AT huizhao anestimationofdistributionalgorithmwithmultileadersearch AT xiaofeiwang estimationofdistributionalgorithmwithmultileadersearch AT tonghan estimationofdistributionalgorithmwithmultileadersearch AT huizhao estimationofdistributionalgorithmwithmultileadersearch |
_version_ |
1724185054264950784 |