Review of chaotic mapping enabled nature-inspired algorithms
Chaotic maps were frequently introduced to generate random numbers and used to replace the pseudo-random numbers distributed in Gauss distribution in computer engineering. These improvements in optimization were called the chaotic improved optimization algorithm, most of them were reported better in...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2022-06-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2022383?viewType=HTML |
| _version_ | 1811241779482066944 |
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| author | Zheng-Ming Gao Juan Zhao Yu-Jun Zhang |
| author_facet | Zheng-Ming Gao Juan Zhao Yu-Jun Zhang |
| author_sort | Zheng-Ming Gao |
| collection | DOAJ |
| description | Chaotic maps were frequently introduced to generate random numbers and used to replace the pseudo-random numbers distributed in Gauss distribution in computer engineering. These improvements in optimization were called the chaotic improved optimization algorithm, most of them were reported better in literature. In this paper, we collected 19 classical maps which could all generate pseudo-random numbers in an interval between 0 and 1. Four types of chaotic improvement to original optimization algorithms were summarized and simulation experiments were carried out. The classical grey wolf optimization (GWO) and sine cosine (SC) algorithms were involved in these experiments. The final simulation results confirmed an uncertainty about the performance of improvements applied in different algorithms, different types of improvements, or benchmark functions. However, Results confirmed that Bernoulli map might be a better choice for most time. The code related to this paper is shared with https://gitee.com/lvqing323/chaotic-mapping. |
| first_indexed | 2024-04-12T13:41:23Z |
| format | Article |
| id | doaj.art-abd8e1aa085b4c538fb08a8c6d56643e |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-12T13:41:23Z |
| publishDate | 2022-06-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-abd8e1aa085b4c538fb08a8c6d56643e2022-12-22T03:30:50ZengAIMS PressMathematical Biosciences and Engineering1551-00182022-06-011988215825810.3934/mbe.2022383Review of chaotic mapping enabled nature-inspired algorithmsZheng-Ming Gao0Juan Zhao 1Yu-Jun Zhang21. School of computer engineering, Jingchu university of technology, Jingmen 448000, China2. School of electronics and information engineering, Jingchu university of technology, Jingmen 448000, China2. School of electronics and information engineering, Jingchu university of technology, Jingmen 448000, ChinaChaotic maps were frequently introduced to generate random numbers and used to replace the pseudo-random numbers distributed in Gauss distribution in computer engineering. These improvements in optimization were called the chaotic improved optimization algorithm, most of them were reported better in literature. In this paper, we collected 19 classical maps which could all generate pseudo-random numbers in an interval between 0 and 1. Four types of chaotic improvement to original optimization algorithms were summarized and simulation experiments were carried out. The classical grey wolf optimization (GWO) and sine cosine (SC) algorithms were involved in these experiments. The final simulation results confirmed an uncertainty about the performance of improvements applied in different algorithms, different types of improvements, or benchmark functions. However, Results confirmed that Bernoulli map might be a better choice for most time. The code related to this paper is shared with https://gitee.com/lvqing323/chaotic-mapping.https://www.aimspress.com/article/doi/10.3934/mbe.2022383?viewType=HTMLchaotic mapsnature-inspired algorithmsbenchmark functionssimulation experimentschaotic improvements |
| spellingShingle | Zheng-Ming Gao Juan Zhao Yu-Jun Zhang Review of chaotic mapping enabled nature-inspired algorithms Mathematical Biosciences and Engineering chaotic maps nature-inspired algorithms benchmark functions simulation experiments chaotic improvements |
| title | Review of chaotic mapping enabled nature-inspired algorithms |
| title_full | Review of chaotic mapping enabled nature-inspired algorithms |
| title_fullStr | Review of chaotic mapping enabled nature-inspired algorithms |
| title_full_unstemmed | Review of chaotic mapping enabled nature-inspired algorithms |
| title_short | Review of chaotic mapping enabled nature-inspired algorithms |
| title_sort | review of chaotic mapping enabled nature inspired algorithms |
| topic | chaotic maps nature-inspired algorithms benchmark functions simulation experiments chaotic improvements |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2022383?viewType=HTML |
| work_keys_str_mv | AT zhengminggao reviewofchaoticmappingenablednatureinspiredalgorithms AT juanzhao reviewofchaoticmappingenablednatureinspiredalgorithms AT yujunzhang reviewofchaoticmappingenablednatureinspiredalgorithms |