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Curve fitting using heuristics and bio-inspired optimization algorithms for experimental data processing in chemistry

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2009年第1期96卷 34-42页

作者： Polo-Corpa, M. J. Salcedo-Sanz, S. Perez-Bellido, A. M. Lopez-Espi, P. Benavente, R. Perez, E. Univ Alcala de Henares Dept Teoria Senal & Comunicac Madrid 28871 Spain CSIC Inst Ciencia & Tecnol Polimeros E-28006 Madrid Spain

This paper analyzes the application of different bio-inspired and heuristic techniques to the problem of curve fitting in experimental chemistry applications. Two different curve models are considered, the well-known Pearson VII function, used before in different curve fitting applications, and a novel hybrid model for transmittance curves, mainly used in the estimation of nitrates concentration in water samples. We describe the performance of an Evolutionary Programming algorithm, a Particle Swarm optimization technique, a Variable Neighborhood Search algorithm and a Cooperative Coevolution approach in several curve fitting problems, including the processing of real curves such as X-ray diffraction patterns, differential scanning calorimetry curves and transmittance curves of contaminated water samples. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Curve fitting Experimental chemistry data processing Heuristics bio-inspired optimization algorithms

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Can bio-inspired optimization algorithms be used to further improve the collective computing performance? Comment on review article "Does being multi-headed make you better at solving problems? A survey of Physarum-based models and computations" by Chao Gao et al.

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PHYSICS OF LIFE REVIEWS 2019年 29卷 48-50页

作者： Xia, Chengyi Huang, Jiechen Tianjin Univ Technol Tianjin Key Lab Intelligence Comp & Novel Softwar Tianjin 300384 Peoples R China Tianjin Univ Technol Minist Educ Key Lab Comp Vis & Syst Tianjin 300384 Peoples R China

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Multi-objective Mapping for Network-on-Chip Based on bio-inspired optimization algorithms

Multi-objective Mapping for Network-on-Chip Based on Bio-ins...

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Prognostics and System Health Management Conference (PHM- Hunan)

作者： Zhuo Qingqi Qian Yanling Li Yue Wang Nantian Natl Univ Def Technol Sci & Technol Integrated Logist Support Lab Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781479979585

The network-on-chip (NoC) mapping confronts a balance between energy consumption, area and the performance of system-on-chip (SoC) in order to achieve optimum performance. In this article, we want to solve a two-objective optimization problem in terms of NoC energy consumption and communication latency by optimizing distribution of link load. Traditional heuristic approaches such as genetic algorithm are likely to trap into local optimal solutions. We present a new application specific multi-objective mapping algorithm based on bio-inspired optimization algorithms combining membrane computing and conventional genetic algorithms. Then this approach is applied to two real applications with different numbers of cores. Experimental results demonstrate that the represented mapping is a feasible way to obtain better NoC architecture in term of energy consumption, latency and traffic balance than mapping based on genetic algorithm.

关键词： NoC mapping placement P systems bio-inspired optimization algorithms

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Evaluation of Objective Functions Used in bio-inspired Harmonic optimization algorithms for Multilevel Converters 13

Evaluation of Objective Functions Used in Bio-inspired Harmo...

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13th IEEE Energy Conversion Congress and Exposition (IEEE ECCE)

作者： Ren, Kaiqi Li, Zhaoyuan Yang, Kehu China Univ Min & Technol Sch Mech Elect & Informat Engn Beijing Peoples R China

ISBN: (纸本)9781728151359

Various optimization objective functions have been proposed for harmonic optimization problems to eliminate unwanted specific harmonic or reduce total harmonic distortion (THD). However, the objective functions are usually intuitively selected, which lack a theoretical basis. In this paper, five objective functions with different mathematical forms are evaluated by two commonly used bio-inspired optimization algorithms, namely the particle swarm optimizers (PSO), and differential evolution (DE). By comparing the THDs, the low-order individual harmonics, and the computational time, resulted from the five objective functions, some guidelines are summarized for selecting objective functions for the bio-inspired harmonic optimization algorithms.

关键词： Harmonic optimization objective function bio-inspired optimization algorithms

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Selfish herd optimizer with levy-flight distribution strategy for global optimization problem

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2020年 538卷 122687-122687页

作者： Zhao, Ruxin Wang, Yongli Liu, Chang Hu, Peng Li, Yanchao Li, Hao Yuan, Chi Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Yangzhou Polytech Inst Sch Mech & Automot Engn Yangzhou 225127 Jiangsu Peoples R China

In recent years, research on stochastic optimization algorithms has received more and more attention from researchers, especially bio-inspired optimization algorithms. Selfish herd optimizer (SHO) is a novel bio-inspired optimization algorithm. It has features that are easy to understand and implement. However, its global search ability is insufficient and precision needs to be further improved. Therefore, we add levy-flight distribution strategy to improve its global search ability and precision. Our main contribution is to use selfish herd optimizer with levy-flight distribution strategy (LFSHO) to solve function and engineering example optimization problem. From experiment results, we can see that LFSHO has more advantages than other algorithms, according to precision, convergence speed and standard variance. It can conclude that LFSHO is a new method for solving function optimization problem and engineering example optimization problem. (C) 2019 Elsevier B.V. All rights reserved.

关键词： bio-inspired optimization algorithms Engineering example optimization problem Function optimization problem Levy-flight distribution strategy Selfish herd optimizer

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Performance evaluation of fuzzy genetic, fuzzy particle swarm and similar insects' optimization algorithms on denoising problem based on novel combined filter for digital X-ray and CT images in Pelvic Region

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第6期83卷 15483-15531页

作者： Rafati, Mehravar Kalantari, Nadia Azadbakht, Javid Nickfarjam, Ali Mohammad Hosseini, Farahnaz Kashan Univ Med Sci Sch Allied Med Sci Dept Med Phys & Radiol Kashan Iran Kashan Univ Med Sci Radiol Dept Kashan Iran Kashan Univ Med Sci Sch Allied Med Sci Dept Hlth Informat Management & Technol Kashan Iran Kashan Univ Med Sci Hlth Informat Management Res Ctr Kashan Iran Univ Kashan Fac Elect & Comp Engn Dept Comp Engn Kashan Iran

This study proposes a novel combined filter accompanied with different optimization algorithms for Poisson noise reduction and increases image quality in digital X-ray and CT images. This filter uses 4th-order PDE, TV, Bayes shrink threshold with optimization algorithms and an exact unbiased inverse of generalized Anscombe transformation (EUIGAT). Experiments were conducted on the basis of displaying the influence of denoising filter on 105 simulated, 102 radiographic and 102 CT images of individuals aged 20-70 years old;53 men and 49 women. Experimental results demonstrated the lowest value for MSE and the highest values for PSNR, IQI, SSIM, FOM and CNR in different kinds of kernels and images compared with the other fuzzy bio-inspired algorithms. The results showed proposed method helps physicians and orthopedists in order to enhance their performances in treating injuries of the pelvic region such as acetabulum fossa and head and neck femur bone.

关键词： Pelvic Region Poisson Noise Anscombe Transformation bio-inspired optimization algorithms Insects'-inspired optimization algorithms

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Harris hawks optimization algorithm for load frequency control of isolated multi-source power generating systems

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2024年第4期25卷 555-571页

作者： Karnavas, Yannis L. Nivolianiti, Evaggelia Democritus Univ Thrace Dept Elect & Comp Engn Elect Machines Lab Xanthi 67100 Greece

This paper presents a recent bio-inspired optimization algorithm for the critical topic of load frequency control (LFC) in an isolated multi-source power generating system. A specific model that emerges from the scientific literature and consists of reheated thermal, hydro and gas-turbine power sources is examined. The main goal is to tune the controller of each generating source i.e. find their optimal gains through bio-inspired optimization algorithms. Namely, the algorithms applied are the Whale optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), Particle Swarm optimization (PSO), and the newly proposed Harris Hawks optimization (HHO). It was observed that all optimization algorithms succeeded in obtaining controllers' gain values which ensure stable system operation. Also, the simulation results indicated the superiority of the proposed HHO algorithm over the other algorithms for all the considered scenarios. In this article, for the first time to the authors' knowledge extend an attempt has been made to optimize an isolated multi-source system using the bio-inspired HHO algorithm. The present study considered the LFC problem with a variety of different scenarios, taking into account both sub-cases of nonlinearities (e.g., generation rate constraint, boiler dynamics, etc.) and their combinations, as well as combinations of different controllers and load disturbances, which are not found in the literature. Last but not least, the robustness of the selected controller was further evaluated. The obtained results clearly demonstrated that the controller's gains established in normal conditions do not require retuning when critical system parameters undergo a significant variation.

关键词： automatic generation control bio-inspired optimization algorithms Harris hawks optimization isolated multi-source power system load frequency control

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bio-inspired Based optimization of Machine Learning Techniques for Energy Efficient Routing for UWSN

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SN Computer Science 2024年第8期5卷 983页

作者： Patil, Vijayalaxmi R. Kanavalli, Anita Chaudhari, Shilpa Aisiri, K.S. Department of CSE Ramaiah Institute of Technology (Affiliated to VTU) Karnataka Bangalore India

Underwater Wireless Sensor Networks (UWSN) is one of the most emerging branches of wireless sensor networks. They are used extensively in environmental monitoring, oceanography, and marine biology. Energy efficiency, signal attenuation, and changeable climatic conditions are just a few of the major obstacles these networks must overcome. The criteria for their functionality differs a lot from a basic wireless sensor network. UWSN are way more complicated when compared to basic wireless sensor networks. Energy Efficient Multipath Routing allows the functioning of UWSN in a highly efficient manner with higher energy efficiency and delivery ratio. This paper proposes a hybrid energy-efficient routing protocol for UWSNs that combines neural network approaches with bio-inspired algorithms. firefly optimization, kookaburra optimization, hippopotamus optimization algorithms from bio-inspired based are used to achieve energy efficiency. Comprehensive performance analysis show that the firefly optimization based hybrid achieves better accuracy and energy efficiency. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： bio-inspired optimization algorithms Energy efficient routing Underwater wireless sensor networks (UWSN)

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Soil moisture estimation using novel bio-inspired soft computing approaches

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ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS 2022年第1期16卷 826-840页

作者： Moazenzadeh, Roozbeh Mohammadi, Babak Safari, Mir Jafar Sadegh Chau, Kwok-wing Shahrood Univ Technol Fac Agr Dept Water Engn Shahrood Iran Lund Univ Dept Phys Geog & Ecosyst Sci Lund Sweden Yasar Univ Dept Civil Engn Izmir Turkey Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China

Soil moisture (SM) is of paramount importance in irrigation scheduling, infiltration, runoff, and agricultural drought monitoring. This work aimed at evaluating the performance of the classical ANFIS (Adaptive Neuro-Fuzzy Inference System) model as well as ANFIS coupled with three bio-inspired metaheuristic optimization methods including whale optimization algorithm (ANFIS-WOA), krill herd algorithm (ANFIS-KHA) and firefly algorithm (ANFIS-FA) in estimating SM. Daily air temperature, relative humidity, wind speed and sunshine hours data at Istanbul Bolge station in Turkey and soil temperature values measured over 2008-2009 were fed into the models under six different scenarios. ANFIS-WOA (RMSE = 1.68, MAPE = 0.04) and ANFIS (RMSE = 2.55, MAPE = 0.07) exhibited the best and worst performance in SM estimation, respectively. All three hybrid models (ANFIS-WOA, ANFIS-KHA and ANFIS-FA) improved SM estimates, reducing RMSE by 34, 28 and 27% relative to the base ANFIS model, respectively. A more detailed analysis of model performances in estimating moisture content over three intervals including [15-25), [25-35) and >= 35% revealed that ANFIS-WOA has had the lowest errors with RMSEs of 1.69, 1.89 and 1.55 in the three SM intervals, respectively. From the perspective of under- or over-estimation of moisture values, ANFIS-WOA (RMSE = 1.44, MAPE = 0.03) in under-estimation set and ANFIS-KHA (RMSE = 1.94, MAPE = 0.05) in over-estimation set showed the highest accuracies. Overall, all three hybrid models performed better in the underestimation set compared to overestimation set.

关键词： ANFIS bio-inspired optimization algorithms data-driven models meteorological variables soil moisture Turkey

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Development of optimised software fault prediction model using machine learning

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年第2期18卷 1355-1376页

作者： Juneja, Shallu Bhathal, Gurjit Singh Sidhu, Brahmaleen K. Punjabi Univ Comp Sci Engn Dept Patiala India Maharaja Agrasen Inst Technol Comp Sci Engn Dept Delhi India

Software fault prediction is a crucial task, especially with the rapid improvements in software technology and increasing complexity of software. As identifying and addressing bugs early in the development process can significantly minimize the costs and enhance the software quality. Software fault prediction using machine learning algorithms has gained significant attention due to its potential to improve software quality and save time in the testing phase. This research paper investigates the impact of classification models on bug prediction performance and explores the use of bio-inspired optimization techniques to enhance model results. Through experiments, it is demonstrated that applying bio-inspired algorithms improves the accuracy of fault prediction models. The evaluation is based on multiple performance metrics and the results show that KNN with BACO (Binary Ant Colony optimization) generally outperform the other models in terms of accuracy. The BACO-KNN fault prediction model attains the accuracy of 96.39% surpassing the previous work.

关键词： Fault prediction classifiers bio-inspired optimization algorithms binary ant colony optimization (BACO) performance metrics

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