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Li H, Yang Y, Wang Y, Li J, Yang H, Tang J, Gao S. Population interaction network in representative gravitational search algorithms: Logistic distribution leads to worse performance. Heliyon 2024; 10:e31631. [PMID: 38828319 PMCID: PMC11140721 DOI: 10.1016/j.heliyon.2024.e31631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 05/20/2024] [Accepted: 05/20/2024] [Indexed: 06/05/2024] Open
Abstract
In this paper, a novel study on the way inter-individual information interacts in meta-heuristic algorithms (MHAs) is carried out using a scheme known as population interaction networks (PIN). Specifically, three representative MHAs, including the differential evolutionary algorithm (DE), the particle swarm optimization algorithm (PSO), the gravitational search algorithm (GSA), and four classical variations of the gravitational search algorithm, are analyzed in terms of inter-individual information interactions and the differences in the performance of each of the algorithms on IEEE Congress on Evolutionary Computation 2017 benchmark functions. The cumulative distribution function (CDF) of the node degree obtained by the algorithm on the benchmark function is fitted to the seven distribution models by using PIN. The results show that among the seven compared algorithms, the more powerful DE is more skewed towards the Poisson distribution, and the weaker PSO, GSA, and GSA variants are more skewed towards the Logistic distribution. The more deviation from Logistic distribution GSA variants conform, the stronger their performance. From the point of view of the CDF, deviating from the Logistic distribution facilitates the improvement of the GSA. Our findings suggest that the population interaction network is a powerful tool for characterizing and comparing the performance of different MHAs in a more comprehensive and meaningful way.
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Affiliation(s)
- Haotian Li
- Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan
| | - Yifei Yang
- Graduate School of Science and Technology, Hirosaki University, Hirosaki-shi, 036-8561, Japan
| | - Yirui Wang
- Faculty of Electrical Engineering and Computer Science, Ningbo University, Zhejiang 315211, China
- Zhejiang Key Laboratory of Mobile Network Application Technology, Zhejiang 315211, China
| | - Jiayi Li
- Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan
| | - Haichuan Yang
- Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Tokushima, 770-8506, Japan
| | - Jun Tang
- Wicresoft Co Ltd, 13810 SE Eastgate Way, Bellevue, WA 98005, USA
| | - Shangce Gao
- Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan
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Sakhaei A, Zamir SM, Rene ER, Veiga MC, Kennes C. Neural network-based performance assessment of one- and two-liquid phase biotrickling filters for the removal of a waste-gas mixture containing methanol, α-pinene, and hydrogen sulfide. ENVIRONMENTAL RESEARCH 2023; 237:116978. [PMID: 37633629 DOI: 10.1016/j.envres.2023.116978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/04/2023] [Accepted: 08/23/2023] [Indexed: 08/28/2023]
Abstract
The performance of one- and two-liquid phase biotrickling filters (OLP/TLP-BTFs) treating a mixture of gas-phase methanol (M), α-pinene (P), and hydrogen sulfide (H) was assessed using artificial neural network (ANN) modeling. The best ANN models with the topologies 3-9-3 and 3-10-3 demonstrated an exceptional capacity for predicting the performance of O/TLP-BTFs, with R2 > 99%. The analysis of causal index (CI) values for the model of OLP-BTF revealed a negative impact of M on P removal (CI = -2.367), a positive influence of P and H on M removal (CI = +7.536 and CI = +3.931) and a negative effect of H on P removal (CI = -1.640). The addition of silicone oil in TLP-BTF reduced the negative impact of M and H on P degradation (CI = -1.261 and CI = -1.310, respectively) compared to the OLP-BTF. These findings suggested that silicone oil had the potential to improve P availability to the biofilm by increasing the concentration gradient of P between the air/gas and aqueous phases. Multi-objective particle swarm optimization (MOPSO) suggested an optimum operational condition, i.e. inlet M, P, and H concentrations of 1.0, 1.1, and 0.3 g m-3, respectively, with elimination capacities (ECs) of 172.1, 26.5, and 0.025 g m-3 h-1 for OLP-BTF. Likewise, one of the optimum operational conditions for TLP-BTF is achievable at inlet concentrations of 4.9, 1.7, and 0.8 g m-3, leading to the optimum ECs of 299.7, 52.9, and 0.072 g m-3 h-1 for M, P, and H, respectively. These results provide important insights into the treatment of complex waste gas mixtures, addressing the interactions between the pollutant removal characteristics in OLP/TLP-BTFs and providing novel approaches in the field of biological waste gas treatment.
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Affiliation(s)
- Amirmohammad Sakhaei
- Biochemical Engineering Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, P.O. Box 14115-114, Iran
| | - Seyed Morteza Zamir
- Biochemical Engineering Department, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, P.O. Box 14115-114, Iran.
| | - Eldon R Rene
- Department of Water Supply, Sanitation and Environmental Engineering, IHE Delft Institute for Water Education, Westvest 7, P. O. Box 3015, 2611AX, Delft, the Netherlands
| | - María C Veiga
- Chemical Engineering Laboratory, Faculty of Sciences and Centre for Advanced Scientific Research - Centro de Investigaciones Científicas Avanzadas (CICA), BIOENGIN Group, University of La Coruña, E - 15008, A Coruña, Spain
| | - Christian Kennes
- Chemical Engineering Laboratory, Faculty of Sciences and Centre for Advanced Scientific Research - Centro de Investigaciones Científicas Avanzadas (CICA), BIOENGIN Group, University of La Coruña, E - 15008, A Coruña, Spain
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Cavlak Y, Ateş A, Abualigah L, Elaziz MA. Fractional-order chaotic oscillator-based Aquila optimization algorithm for maximization of the chaotic with Lorentz oscillator. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08945-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 08/01/2023] [Indexed: 09/02/2023]
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Yang J, Zhang Y, Jin T, Lei Z, Todo Y, Gao S. Maximum Lyapunov exponent-based multiple chaotic slime mold algorithm for real-world optimization. Sci Rep 2023; 13:12744. [PMID: 37550464 PMCID: PMC10406909 DOI: 10.1038/s41598-023-40080-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 08/04/2023] [Indexed: 08/09/2023] Open
Abstract
Slime mold algorithm (SMA) is a nature-inspired algorithm that simulates the biological optimization mechanisms and has achieved great results in various complex stochastic optimization problems. Owing to the simulated biological search principle of slime mold, SMA has a unique advantage in global optimization problem. However, it still suffers from issues of missing the optimal solution or collapsing to local optimum when facing complicated problems. To conquer these drawbacks, we consider adding a novel multi-chaotic local operator to the bio-shock feedback mechanism of SMA to compensate for the lack of exploration of the local solution space with the help of the perturbation nature of the chaotic operator. Based on this, we propose an improved algorithm, namely MCSMA, by investigating how to improve the probabilistic selection of chaotic operators based on the maximum Lyapunov exponent (MLE), an inherent property of chaotic maps. We implement the comparison between MCSMA with other state-of-the-art methods on IEEE Congress on Evolution Computation (CEC) i.e., CEC2017 benchmark test suits and CEC2011 practical problems to demonstrate its potency and perform dendritic neuron model training to test the robustness of MCSMA on classification problems. Finally, the parameters' sensitivities of MCSMA, the utilization of the solution space, and the effectiveness of the MLE are adequately discussed.
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Affiliation(s)
- Jiaru Yang
- Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan
| | - Yu Zhang
- Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan
| | - Ting Jin
- School of Science, Nanjing Forestry University, Nanjing, 210037, China
| | - Zhenyu Lei
- Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan
| | - Yuki Todo
- Faculty of Electrical, Information and Communication Engineering, Kanazawa University, Ishikawa, 9201192, Japan
| | - Shangce Gao
- Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan.
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Turgut OE, Turgut MS, Kırtepe E. A systematic review of the emerging metaheuristic algorithms on solving complex optimization problems. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08481-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Fahmy H, El-Gendy EM, Mohamed M, Saafan MM. ECH 3OA: An Enhanced Chimp-Harris Hawks Optimization Algorithm for copyright protection in Color Images using watermarking techniques. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Seyedmohammadi J, Zeinadini A, Navidi MN, McDowell RW. A new robust hybrid model based on support vector machine and firefly meta-heuristic algorithm to predict pistachio yields and select effective soil variables. ECOL INFORM 2023. [DOI: 10.1016/j.ecoinf.2023.102002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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Teodosio B, Wasantha PLP, Yaghoubi E, Guerrieri M, C. van Staden R, Fragomeni S. Shrink–swell index prediction through deep learning. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07764-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
AbstractGrowing application of artificial intelligence in geotechnical engineering has been observed; however, its ability to predict the properties and nonlinear behaviour of reactive soil is currently not well considered. Although previous studies provided linear correlations between shrink–swell index and Atterberg limits, obtained model accuracy values were found unsatisfactory results. Artificial intelligence, specifically deep learning, has the potential to give improved accuracy. This research employed deep learning to predict more accurate values of shrink–swell indices, which explored two scenarios; Scenario 1 used the features liquid limit, plastic limit, plasticity index, and linear shrinkage, whilst Scenario 2 added the input feature, fines percentage passing through a 0.075-mm sieve (%fines). Findings indicated that the implementation of deep learning neural networks resulted in increased model measurement accuracy in Scenarios 1 and 2. The values of accuracy measured in this study were suggestively higher and have wider variance than most previous studies. Global sensitivity analyses were also conducted to investigate the influence of each input feature. These sensitivity analyses resulted in a range of predicted values within the variance of data in Scenario 2, with the %fines having the highest contribution to the variance of the shrink–swell index and a relevant interaction between linear shrinkage and %fines. The proposed model Scenario 2 was around 10–65% more accurate than the preceding models considered in this study, which can then be used to expeditiously estimate more accurate values of shrink–swell indices.
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Baghdadi NA, Malki A, Magdy Balaha H, AbdulAzeem Y, Badawy M, Elhosseini M. Classification of breast cancer using a manta-ray foraging optimized transfer learning framework. PeerJ Comput Sci 2022; 8:e1054. [PMID: 36092017 PMCID: PMC9454783 DOI: 10.7717/peerj-cs.1054] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 07/07/2022] [Indexed: 06/15/2023]
Abstract
Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast cancer survival chances can be improved by early detection and diagnosis. For medical image analyzers, diagnosing is tough, time-consuming, routine, and repetitive. Medical image analysis could be a useful method for detecting such a disease. Recently, artificial intelligence technology has been utilized to help radiologists identify breast cancer more rapidly and reliably. Convolutional neural networks, among other technologies, are promising medical image recognition and classification tools. This study proposes a framework for automatic and reliable breast cancer classification based on histological and ultrasound data. The system is built on CNN and employs transfer learning technology and metaheuristic optimization. The Manta Ray Foraging Optimization (MRFO) approach is deployed to improve the framework's adaptability. Using the Breast Cancer Dataset (two classes) and the Breast Ultrasound Dataset (three-classes), eight modern pre-trained CNN architectures are examined to apply the transfer learning technique. The framework uses MRFO to improve the performance of CNN architectures by optimizing their hyperparameters. Extensive experiments have recorded performance parameters, including accuracy, AUC, precision, F1-score, sensitivity, dice, recall, IoU, and cosine similarity. The proposed framework scored 97.73% on histopathological data and 99.01% on ultrasound data in terms of accuracy. The experimental results show that the proposed framework is superior to other state-of-the-art approaches in the literature review.
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Affiliation(s)
- Nadiah A. Baghdadi
- College of Nursing, Nursing Management and Education Department, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Amer Malki
- College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
| | - Hossam Magdy Balaha
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Yousry AbdulAzeem
- Computer Engineering Department, Misr Higher Institute for Engineering and Technology, Mansoura, Egypt
| | - Mahmoud Badawy
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
| | - Mostafa Elhosseini
- College of Computer Science and Engineering, Taibah University, Yanbu, Saudi Arabia
- Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
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Design of Aquila Optimization Heuristic for Identification of Control Autoregressive Systems. MATHEMATICS 2022. [DOI: 10.3390/math10101749] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Swarm intelligence-based metaheuristic algorithms have attracted the attention of the research community and have been exploited for effectively solving different optimization problems of engineering, science, and technology. This paper considers the parameter estimation of the control autoregressive (CAR) model by applying a novel swarm intelligence-based optimization algorithm called the Aquila optimizer (AO). The parameter tuning of AO is performed statistically on different generations and population sizes. The performance of the AO is investigated statistically in various noise levels for the parameters with the best tuning. The robustness and reliability of the AO are carefully examined under various scenarios for CAR identification. The experimental results indicate that the AO is accurate, convergent, and robust for parameter estimation of CAR systems. The comparison of the AO heuristics with recent state of the art counterparts through nonparametric statistical tests established the efficacy of the proposed scheme for CAR estimation.
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