1
|
Zhou H, Xu Z, Chen Y, Yan Y, Zhang S, Lin X, Cui D, Yang J. The combined multilayer perceptron and logistic regression (MLP-LR) method better predicted the spread of Hyphantria cunea (Lepidoptera: Erebidae). JOURNAL OF ECONOMIC ENTOMOLOGY 2025:toaf087. [PMID: 40353742 DOI: 10.1093/jee/toaf087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2024] [Revised: 03/18/2025] [Accepted: 03/25/2025] [Indexed: 05/14/2025]
Abstract
Hyphantria cunea (Lepidoptera: Erebidae) is one of the pests that pose a serious threat to forest and agronomic crops in China. Its spread is influenced by various factors, including environmental factors and anthropogenic factors, and the available data on pest spread and the influencing factor has nonlinear relationship. Additionally, the collection of pest data is often constrained, resulting in small datasets, a lack of long-term time series data, and issues such as missing data and anomalies. Traditional model-driven approaches have limitations in handling nonlinear relationships and high-dimensional data, while data-driven methods often lack interpretability and are prone to overfitting, ultimately leading to insufficient prediction accuracy. Therefore, this paper proposes the MLP-LR method, which combines logistic regression (LR) with a multilayer perceptron (MLP) to overcome these limitations. The model also used the Bayesian adaptive lasso method to select important influencing factors, that further improved the prediction accuracy. Based on H. cunea occurrence data in China, the current study demonstrated the stability and accuracy of the MLP-LR model on small datasets. The results showed that compared to traditional LR models and MLP independently, MLP-LR performs better in predicting the spread of H. cunea, effectively addressing the shortcomings of traditional methods. This study provides a new tool and perspective for forecasting and early warning of H. cunea outbreaks, offering important references for future research and its applications in the field.
Collapse
Affiliation(s)
- Hongwei Zhou
- Northeast Forestry Univ, Coll Comp & Control Engn, Harbin, Peoples R China
| | - Zihan Xu
- Northeast Forestry Univ, Coll Comp & Control Engn, Harbin, Peoples R China
| | - Yifan Chen
- National Forestry and Grassland Administration, Biological Disaster Prevention and Control Center, Shenyang, China
| | - Yunbo Yan
- Northeast Forestry Univ, Coll Comp & Control Engn, Harbin, Peoples R China
| | - Siyan Zhang
- Northeast Forestry Univ, Coll Comp & Control Engn, Harbin, Peoples R China
| | - Xiao Lin
- National Forestry and Grassland Administration, Biological Disaster Prevention and Control Center, Shenyang, China
| | - Di Cui
- Heilongjiang Forestry Technology Service Center, Harbin, China
| | - Jun Yang
- Forestry &Grassland Investigation and Planning Institute of Heilongjiang Province, Harbin, China
| |
Collapse
|
2
|
Alweshah M, Aldabbas Y, Abu-Salih B, Oqeil S, Hasan HS, Alkhalaileh S, Kassaymeh S. Hybrid black widow optimization with iterated greedy algorithm for gene selection problems. Heliyon 2023; 9:e20133. [PMID: 37809602 PMCID: PMC10559925 DOI: 10.1016/j.heliyon.2023.e20133] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 09/03/2023] [Accepted: 09/12/2023] [Indexed: 10/10/2023] Open
Abstract
Gene Selection (GS) is a strategy method targeted at reducing redundancy, limited expressiveness, and low informativeness in gene expression datasets obtained by DNA Microarray technology. These datasets contain a plethora of diverse and high-dimensional samples and genes, with a significant discrepancy in the number of samples and genes present. The complexities of GS are especially noticeable in the context of microarray expression data analysis, owing to the inherent data imbalance. The main goal of this study is to offer a simplified and computationally effective approach to dealing with the conundrum of attribute selection in microarray gene expression data. We use the Black Widow Optimization algorithm (BWO) in the context of GS to achieve this, using two unique methodologies: the unaltered BWO variation and the hybridized BWO variant combined with the Iterated Greedy algorithm (BWO-IG). By improving the local search capabilities of BWO, this hybridization attempts to promote more efficient gene selection. A series of tests was carried out using nine benchmark datasets that were obtained from the gene expression data repository in the pursuit of empirical validation. The results of these tests conclusively show that the BWO-IG technique performs better than the traditional BWO algorithm. Notably, the hybridized BWO-IG technique excels in the efficiency of local searches, making it easier to identify relevant genes and producing findings with higher levels of reliability in terms of accuracy and the degree of gene pruning. Additionally, a comparison analysis is done against five modern wrapper Feature Selection (FS) methodologies, namely BIMFOHHO, BMFO, BHHO, BCS, and BBA, in order to put the suggested BWO-IG method's effectiveness into context. The comparison that follows highlights BWO-IG's obvious superiority in reducing the number of selected genes while also obtaining remarkably high classification accuracy. The key findings were an average classification accuracy of 94.426, average fitness values of 0.061, and an average number of selected genes of 2933.767.
Collapse
Affiliation(s)
- Mohammed Alweshah
- Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan
| | - Yasmeen Aldabbas
- Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan
| | - Bilal Abu-Salih
- Department of Computer Science, King Abdullah II School of Information Technology, The University of Jordan, Amman, Jordan
| | - Saleh Oqeil
- Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan
| | - Hazem S. Hasan
- Department of Plant Production and Protection, Faculty of Agricultural Technology, Al-Balqa Applied University, Al-Salt, Jordan
| | - Saleh Alkhalaileh
- Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan
| | - Sofian Kassaymeh
- Software Engineering Department, Faculty of Information Technology, Aqaba University of Technology, Aqaba, Jordan
| |
Collapse
|
3
|
Sadeghian Z, Akbari E, Nematzadeh H, Motameni H. A review of feature selection methods based on meta-heuristic algorithms. J EXP THEOR ARTIF IN 2023. [DOI: 10.1080/0952813x.2023.2183267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Affiliation(s)
- Zohre Sadeghian
- Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
| | - Ebrahim Akbari
- Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
| | - Hossein Nematzadeh
- Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
| | - Homayun Motameni
- Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
| |
Collapse
|
4
|
Hussain SF, Butt IA, Hanif M, Anwar S. Clustering uncertain graphs using ant colony optimization (ACO). Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07063-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
5
|
PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review. Symmetry (Basel) 2022. [DOI: 10.3390/sym14030455] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
Companies are constantly changing in their organization and the way they treat information. In this sense, relevant data analysis processes arise for decision makers. Similarly, to perform decision-making analyses, multi-criteria and metaheuristic methods represent a key tool for such analyses. These analysis methods solve symmetric and asymmetric problems with multiple criteria. In such a way, the symmetry transforms the decision space and reduces the search time. Therefore, the objective of this research is to provide a classification of the applications of multi-criteria and metaheuristic methods. Furthermore, due to the large number of existing methods, the article focuses on the particle swarm algorithm (PSO) and its different extensions. This work is novel since the review of the literature incorporates scientific articles, patents, and copyright registrations with applications of the PSO method. To mention some examples of the most relevant applications of the PSO method; route planning for autonomous vehicles, the optimal application of insulin for a type 1 diabetic patient, robotic harvesting of agricultural products, hybridization with multi-criteria methods, among others. Finally, the contribution of this article is to propose that the PSO method involves the following steps: (a) initialization, (b) update of the local optimal position, and (c) obtaining the best global optimal position. Therefore, this work contributes to researchers not only becoming familiar with the steps, but also being able to implement it quickly. These improvements open new horizons for future lines of research.
Collapse
|
6
|
Li J, Song L, Cao L. An improved firefly algorithm with distance-guided selection strategy and its application. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In this paper, to reduce the redundant attractions and incorrect directions of firefly algorithm (FA), a distance-guided selection approach (DSFA) is proposed, which consists of a distance-guided mechanism and selection strategy. Where the designed distance-guided mechanism reduces the attractions and plays as a classifier for global search and local search, the suggested selection strategy can avoid local search falling into traps, thereby increasing the probability of correct direction. With the good cooperation of these two approaches, DSFA obtains a good balance of exploration and exploitation. To confirm the performance of the proposed algorithm, excessive experiments are conducted on CEC2013 benchmark functions, large-scale optimization problems CEC2008, and software defect prediction (SDP). In the comparison with the 5 advanced FA variants, DSFA provides the optimal solutions to most CEC2013 problems. Besides, when facing the problems of class imbalance and the dimensional explosion of datasets, DSFA greatly improves the performance of machine learning classifiers employed by SDP. It can be concluded that DSFA is an effective method for global continuous optimization problems.
Collapse
Affiliation(s)
- Jie Li
- Jiujiang University, Jiujiang, China
| | - Li Song
- Jiujiang University, Jiujiang, China
| | - Lianglin Cao
- Jiujiang University, Jiujiang, China
- Naval University of Engineering, WuHan, China
| |
Collapse
|
7
|
|
8
|
Kyaw KS, Limsiroratana S, Sattayaraksa T. A Comparative Study of Meta-Heuristic and Conventional Search in Optimization of Multi-Dimensional Feature Selection. INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING 2022. [DOI: 10.4018/ijamc.292517] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Algorithmic – based search approach is ineffective at addressing the problem of multi-dimensional feature selection for document categorization. This study proposes the use of meta heuristic based search approach for optimal feature selection. Elephant optimization (EO) and Ant Colony optimization (ACO) algorithms coupled with Naïve Bayes (NB), Support Vector Machin (SVM), and J48 classifiers were used to highlight the optimization capability of meta-heuristic search for multi-dimensional feature selection problem in document categorization. In addition, the performance results for feature selection using the two meta-heuristic based approaches (EO and ACO) were compared with conventional Best First Search (BFS) and Greedy Stepwise (GS) algorithms on news document categorization. The comparative results showed that global optimal feature subsets were attained using adaptive parameters tuning in meta-heuristic based feature selection optimization scheme. In addition, the selected number of feature subsets were minimized dramatically for document classification.
Collapse
Affiliation(s)
- Khin Sandar Kyaw
- Department of International Business Management, Didyasarin International College, Hatyai University, Thailand
| | | | - Tharnpas Sattayaraksa
- Department of International Business Management, Didyasarin International College, Hatyai University, Thailand
| |
Collapse
|
9
|
Cao L, Ben K, Peng H. Enhancing firefly algorithm with multiple swarm strategy. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-200619] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Firefly algorithm (FA) is one of most important nature-inspired algorithm based on swarm intelligence. Meanwhile, FA uses the full attraction model, which results too many unnecessary movements and reduces the efficiency of searching the optimal solution. To overcome these problems, this paper presents a new job, how the better fireflies move, which is always ignored. The novel algorithm is called multiple swarm strategy firefly algorithm (MSFFA), in which multiple swarm attraction model and status adaptively switch approach are proposed. It is characterized by employing the multiple swarm attraction model, which not only improves the efficiency of searching the optimal solution, but also quickly finds the better fireflies that move in free status. In addition, the novel approach defines that the fireflies followed different rules in different status, and can adaptively switch the status of fireflies between the original status and the free status to balance the exploration and the exploitation. To verify the robustness of MSFFA, it is compared with other improved FA variants on CEC2013. In one case of 30 dimension on 28 test functions, the proposed algorithm is significantly better than FA, DFA, PaFA, MFA, NaFA,and NSRaFA on 24, 23, 23, 17, 15, and 24 functions, respectively. The experimental results prove that MSFFA has obvious advantages over other FA variants.
Collapse
Affiliation(s)
- Lianglin Cao
- School of Electronics and Engineering, Naval University of Engineering, WuHan, China
| | - Kerong Ben
- School of Electronics and Engineering, Naval University of Engineering, WuHan, China
| | - Hu Peng
- School of Information Science and Technology, Jiujiang University, Jiujiang, China
| |
Collapse
|
10
|
Torre-Bastida AI, Díaz-de-Arcaya J, Osaba E, Muhammad K, Camacho D, Del Ser J. Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions. Neural Comput Appl 2021:1-31. [PMID: 34366573 PMCID: PMC8329000 DOI: 10.1007/s00521-021-06332-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Accepted: 07/12/2021] [Indexed: 02/07/2023]
Abstract
This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.
Collapse
Affiliation(s)
| | - Josu Díaz-de-Arcaya
- TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
| | - Eneko Osaba
- TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain
| | - Khan Muhammad
- Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Software, Sejong University, Seoul, 143-747 Republic of Korea
| | - David Camacho
- Universidad Politécnica de Madrid, 28031 Madrid, Spain
| | - Javier Del Ser
- University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
| |
Collapse
|
11
|
Zhu W, Peng H, Leng C, Deng C, Wu Z. Surrogate-assisted firefly algorithm for breast cancer detection. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-201124] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Breast cancer is a severe disease for women health, however, with expensive diagnostic cost or obsolete medical technique, many patients are hard to obtain prompt medical treatment. Thus, efficient detection result of breast cancer while lower medical cost may be a promising way to protect women health. Breast cancer detection using all features will take a lot of time and computational resources. Thus, in this paper, we proposed a novel framework with surrogate-assisted firefly algorithm (FA) for breast cancer detection (SFA-BCD). As an advanced evolutionary algorithm (EA), FA is adopted to make feature selection, and the machine learning as classifier identify the breast cancer. Moreover, the surrogate model is utilized to decrease computation cost and expensive computation, which is the approximation function built by offline data to the real object function. The comprehensive experiments have been conducted under several breast cancer dataset derived from UCI. Experimental results verified that the proposed framework with surrogate-assisted FA significantly reduced the computation cost.
Collapse
Affiliation(s)
- Wenhua Zhu
- School of Information Science and Technology, Jiujiang University, Jiujiang, China
| | - Hu Peng
- School of Information Science and Technology, Jiujiang University, Jiujiang, China
| | - Chaohui Leng
- Affiliated Hospital, Jiujiang University, Jiujiang, China
| | - Changshou Deng
- School of Information Science and Technology, Jiujiang University, Jiujiang, China
| | - Zhijian Wu
- School of Computer Science, Wuhan University, Wuhan, China
| |
Collapse
|
12
|
Internet of things with bio-inspired co-evolutionary deep-convolution neural-network approach for detecting road cracks in smart transportation. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05401-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
13
|
Wang XH, Zhang Y, Sun XY, Wang YL, Du CH. Multi-objective feature selection based on artificial bee colony: An acceleration approach with variable sample size. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2019.106041] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
14
|
Emerging intelligent algorithms: challenges and applications. Neural Comput Appl 2019. [DOI: 10.1007/s00521-018-3930-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|