Niching Grey Wolf Optimizer for Multimodal Optimization Problems
Metaheuristic algorithms are widely used for optimization in both research and the industrial community for simplicity, flexibility, and robustness. However, multi-modal optimization is a difficult task, even for metaheuristic algorithms. Two important issues that need to be handled for solving mult...
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doaj-25bbc804bb4247c29b5c4d70905c2e702021-06-01T00:54:03ZengMDPI AGApplied Sciences2076-34172021-05-01114795479510.3390/app11114795Niching Grey Wolf Optimizer for Multimodal Optimization ProblemsRasel Ahmed0Amril Nazir1Shuhaimi Mahadzir2Mohammad Shorfuzzaman3Jahedul Islam4Department of Chemical Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak Darul Ridzuan, MalaysiaDepartment of Information Systems, College of Technological Innovation, Abu Dhabi Campus, Zayed University, Abu Dhabi P.O. Box 144534, United Arab EmiratesDepartment of Chemical Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak Darul Ridzuan, MalaysiaDepartment of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi ArabiaDepartment of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak Darul Ridzuan, MalaysiaMetaheuristic algorithms are widely used for optimization in both research and the industrial community for simplicity, flexibility, and robustness. However, multi-modal optimization is a difficult task, even for metaheuristic algorithms. Two important issues that need to be handled for solving multi-modal problems are (a) to categorize multiple local/global optima and (b) to uphold these optima till the ending. Besides, a robust local search ability is also a prerequisite to reach the exact global optima. Grey Wolf Optimizer (GWO) is a recently developed nature-inspired metaheuristic algorithm that requires less parameter tuning. However, the GWO suffers from premature convergence and fails to maintain the balance between exploration and exploitation for solving multi-modal problems. This study proposes a niching GWO (NGWO) that incorporates personal best features of PSO and a local search technique to address these issues. The proposed algorithm has been tested for 23 benchmark functions and three engineering cases. The NGWO outperformed all other considered algorithms in most of the test functions compared to state-of-the-art metaheuristics such as PSO, GSA, GWO, Jaya and two improved variants of GWO, and niching CSA. Statistical analysis and Friedman tests have been conducted to compare the performance of these algorithms thoroughly.https://www.mdpi.com/2076-3417/11/11/4795metaheuristic algorithmswarm intelligencemulti-modal optimizationGrey Wolf Optimizerniching techniquelocal search |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Rasel Ahmed Amril Nazir Shuhaimi Mahadzir Mohammad Shorfuzzaman Jahedul Islam |
spellingShingle |
Rasel Ahmed Amril Nazir Shuhaimi Mahadzir Mohammad Shorfuzzaman Jahedul Islam Niching Grey Wolf Optimizer for Multimodal Optimization Problems Applied Sciences metaheuristic algorithm swarm intelligence multi-modal optimization Grey Wolf Optimizer niching technique local search |
author_facet |
Rasel Ahmed Amril Nazir Shuhaimi Mahadzir Mohammad Shorfuzzaman Jahedul Islam |
author_sort |
Rasel Ahmed |
title |
Niching Grey Wolf Optimizer for Multimodal Optimization Problems |
title_short |
Niching Grey Wolf Optimizer for Multimodal Optimization Problems |
title_full |
Niching Grey Wolf Optimizer for Multimodal Optimization Problems |
title_fullStr |
Niching Grey Wolf Optimizer for Multimodal Optimization Problems |
title_full_unstemmed |
Niching Grey Wolf Optimizer for Multimodal Optimization Problems |
title_sort |
niching grey wolf optimizer for multimodal optimization problems |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-05-01 |
description |
Metaheuristic algorithms are widely used for optimization in both research and the industrial community for simplicity, flexibility, and robustness. However, multi-modal optimization is a difficult task, even for metaheuristic algorithms. Two important issues that need to be handled for solving multi-modal problems are (a) to categorize multiple local/global optima and (b) to uphold these optima till the ending. Besides, a robust local search ability is also a prerequisite to reach the exact global optima. Grey Wolf Optimizer (GWO) is a recently developed nature-inspired metaheuristic algorithm that requires less parameter tuning. However, the GWO suffers from premature convergence and fails to maintain the balance between exploration and exploitation for solving multi-modal problems. This study proposes a niching GWO (NGWO) that incorporates personal best features of PSO and a local search technique to address these issues. The proposed algorithm has been tested for 23 benchmark functions and three engineering cases. The NGWO outperformed all other considered algorithms in most of the test functions compared to state-of-the-art metaheuristics such as PSO, GSA, GWO, Jaya and two improved variants of GWO, and niching CSA. Statistical analysis and Friedman tests have been conducted to compare the performance of these algorithms thoroughly. |
topic |
metaheuristic algorithm swarm intelligence multi-modal optimization Grey Wolf Optimizer niching technique local search |
url |
https://www.mdpi.com/2076-3417/11/11/4795 |
work_keys_str_mv |
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1721413600795426816 |