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IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology

作者： Megha Sharma Amandeep Verma Research Scholar UIET Panjab University Chandigarh India Assistant Professor UIET Panjab University Chandigarh India

ISBN: (纸本)9781509007752

Cloud computing is a technique of utilizing resources over internet to achieve a certain goal. With massive increase in the number of users and their variable demand of different services, hundreds to thousands of servers have to be deployed. Equally massive cooling systems are required to keep the servers working under normal temperatures. This leads to high C02 emission and following current trends it is only expected to rise in the coming years. Thus cloud computing services are directly or indirectly contributing to global warming. Green computing is the solution to this problem in which same amount of weightage is given to makespan optimization as to the energy consumption and greenhouse gas emissions of a machine. In this paper, we perform energy analysis of a makespan-efficient algorithm Discrete Symbiotic organisms search (DSOS) and based on the results, we conclude that this algorithm can be made more energy-efficient which is also our area of future research.

关键词： DSOS metaheuristic algorithms green computing fitness value symbiotic behavior

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A Multiple-Search Multi-Start Framework for metaheuristics

A Multiple-Search Multi-Start Framework for Metaheuristics

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Chun-Wei Tsai Kai-Cheng Hu Ming-Chao Chiang Department of Computer Science and Information Engineering National Ilan University Department of Computer Science and Engineering National Sun Yat-sen University

ISBN: (纸本)9781479938414

Until now, most, if not all, of the metaheuristic algorithms have been extremely sensitive to the initial solutions and may even converge to a local optimum at early iterations for most optimization problems. This paper introduces an effective and efficient framework, called multiple-search multi-start (MSMS), to mitigate the impact of these problems. To evaluate the performance of the proposed framework, we apply it to k-means and particle swarm optimization for the clustering problem and compare the results with those of several well-known clustering algorithms. The experimental results show that the proposed framework can significantly enhance the performance of not only single-solution-based but also population-based metaheuristic algorithms in terms of both the quality and the computation time.

关键词： Multi-start Clustering metaheuristic algorithms

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Size, Topology, and Shape Optimization of Truss Structures using Symbiotic Organisms Search

Size, Topology, and Shape Optimization of Truss Structures u...

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International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA)

作者： Doddy Prayogo Kenneth Harsono Kelvin Eko Prasetyo Foek Tjong Wong Daniel Tjandra Dept. of Civil Engineering Petra Christian University Surabaya Indonesia

ISBN: (数字)9781728130903

ISBN: (纸本)9781728130910

Truss structures are common in the building industry. One way to contain construction costs is to implement structural optimization. Optimization has to consider cross-sectional size, area, topology, and node coordinates as design variables. However, each truss structure has numerous complex constraints and variables that make optimizing this structure complex and difficult. The metaheuristic method is efficient and effective in solving large and complex problems. This paper tested three metaheuristic algorithms: particle swarm optimization (PSO), differential evolution (DE), and symbiotic organisms search (SOS). Each algorithm was used to optimize a 10-bar planar truss structure and a 15-bar planar truss structure. SOS was found to have the best optimization results, convergence behavior, and consistency.

关键词： metaheuristic algorithms truss structure optimization

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Capacitated vehicle routing system applying Monte Carlo methods 2015

Capacitated vehicle routing system applying Monte Carlo meth...

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Proceedings of the annual conference on Brazilian Symposium on Information Systems: Information Systems: A Computer Socio-Technical Perspective - Volume 1

作者： Romulo Augusto de Carvalho Oliveira Karina Valdivia Delgado Universidade de Sao Paulo

The Vehicle Routing Problem (VRP) is one of the combinatorial optimization problems most studied in Computer Science and of great relevance to the areas of logistics and transport. This paper presents a new algorithm for solving the Capacitated Vehicle Routing Problem (CVRP) using Monte Carlo methods. Monte Carlo methods are statistical methods that use random sampling to solve probabilistic and deterministic problems. The proposed algorithm was developed based on Monte Carlo simulations and Clarke and Wright Savings heuristic and demonstrated results comparable to the best existing algorithms in the literature, it overcomes previous work with Monte Carlo methods. The comparison, analysis and evaluation of the algorithm were based on existing benchmarks in the literature.

关键词： Combinatorial Optimization Monte Carlo Methods Heuristic algorithms Capacitated Vehicle Routing Problem metaheuristic algorithms

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A comprehensive survey of honey badger optimization algorithm and meta-analysis of its variants and applications

Franklin Open

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Franklin Open 2024年 8卷

作者： Hassan, Ibrahim Hayatu Abdullahi, Mohammed Isuwa, Jeremiah Yusuf, Sahabi Ali Aliyu, Ibrahim Tetengi Institute for Agricultural Research Ahmadu Bello University Zaria Nigeria Department of Computer Science Ahmadu Bello University Zaria Nigeria Computer Science Deparment Federal University Kashere Gombe State Nigeria

metaheuristic algorithms are commonly used in solving complex and NP-hard optimization problems in various fields. These algorithms have become popular because of their ability to explore and exploit solutions in various problem domains. Honey Badger Algorithm (HBA) is a population-based metaheuristic optimization algorithm inspired by the dynamic hunting strategy of honey badgers, utilizing honey and digging-seeking techniques. Since its introduction in 2020, HBA has garnered widespread attention and has been applied across various domains. This review aims to comprehensively survey the improvement and application of HBA in solving various optimization problems. Additionally, the survey conducts a meta-analysis of the HBA's improvements, hybridization and application since its introduction. According to the result of the survey, 52 studies presented improved HBA using chaotic maps, levy flight mechanism, adaptive mechanisms, transfer functions, multi-objective mechanism and opposition based learning techniques, 20 studies presented a hybrid HBA with other metaheuristics algorithms and 101 studies uses the original HBA for solving various optimization problems. According to the survey, the wide acceptance of the HBA within the research community stems from its straightforwardness, ease of use, efficient computational time, accelerated convergence speed, high efficacy, and capability to address different kind of optimization issues, distinguishing it from the well-known optimization approches presented. © 2024

关键词： Honey badger algorithm metaheuristic algorithms Nature-inspired optimization algorithm Population-based optimization algorithms

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Deep CNN hyperparameter optimization algorithms for sensor-based human activity recognition

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Neuroscience Informatics 2022年第3期2卷 100078-100078页

作者： Raziani, Saeid Azimbagirad, Mehran Department of Computer Engineering Razi University Kermanshah Iran Faculty of Medicine and Health Sciences University of Western Brittany Brest France Laboratory of Medical Information Processing (LaTIM) UMR1101 INSERM Brest France

Human activity recognition (HAR) is an active field of research for the classification of human movements and applications in a wide variety of areas such as medical diagnosis, health care systems, elderly care, rehabilitation, surveillance in a smart home, and so on. HAR data are collected from wearable devices which include different types of sensors and/or with the smartphone sensor's aid. In recent years, deep learning algorithms have been showed a significant robustness for classifying human activities on HAR data. In the architecture of such deep learning networks, there are several hyperparameters to control the model efficiency which are mainly set by experiment. In this paper, firstly, we introduced one dimensional Convolutional neural network (CNN) as a model among supervised deep learning for an online HAR data classification. In order to automatically choose the optimum hyperparameters of the CNN model, seven approaches based on metaheuristic algorithms were investigated. The optimization algorithms were evaluated on the HAR dataset from the UCI Machine Learning repository. Furthermore, the performance of the proposed method was compared with several state-of-the-art evolutionary algorithms and other deep learning models. The experimental results showed the robustness of using metaheuristic algorithms to optimize the hyperparameters in CNN. © 2022 The Author(s)

关键词： Convolutional neural network Deep learning Human activity recognition metaheuristic algorithms

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An application of teaching-learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generators

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RESULTS IN CONTROL AND OPTIMIZATION 2023年 10卷

作者： Sulaiman, Mohd Herwan Mustaffa, Zuriani Rashid, Muhammad Ikram Mohd Univ Malaysia Pahang Fac Elect & Elect Engn Technol Pekan 26600 Pahang Malaysia Univ Malaysia Pahang Fac Comp Pekan 26600 Pahang Malaysia

This paper proposes the implementation of metaheuristic algorithm namely, teaching-learningbased optimization (TLBO) algorithm to solve optimal power flow (OPF) problem. TLBO is inspired by philosophy of teaching and learning in the classroom. OPF on the other hand, is one of the most complex problems in power system operation, where in this paper, two objective functions aimed to be minimized by TLBO namely cost minimization and combined cost and emission (CEE) minimization. The effectiveness of proposed TLBO in solving the OPF is tested on modified IEEE -57 bus system that integrated with stochastic wind and solar power generations. To show the effectiveness of the proposed TLBO, several recent algorithms that have been proposed in literature will be utilized and compared. The simulations demonstrate the superiority of TLBO as an effective alternative solution for the OPF problems, where for the cost minimization, TLBO able to obtained 0.16% cost saving per hour compared to the second best algorithm;and for the CEE minimization, TLBO outperformed the second best algorithm by 0.12% cost saving per hour.

关键词： Cost and emission minimizations metaheuristic algorithms Optimal power flow Teaching-learning based optimization Stochastic power generations

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New intelligent particle swarm optimization algorithm with extreme learning machine for forecasting Pattavia pineapple productivity: case study of Loei and Nong Khai provinces in Thailand

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COGENT ENGINEERING 2024年第1期11卷

作者： Wongsinlatam, Wullapa Khon Kaen Univ Fac Interdisciplinary Studies Nong Khai Campus Nong Khai Thailand

This research designs and develops a software innovation for Pattavia pineapple cultivation and productivity distribution planning to increase income for farmers. This research formulated and introduced an innovative machine learning (ML) model, the new model is called a new intelligent particle swarm optimization algorithm with extreme learning machine (NIPSO-ELM), to forecast Pattavia pineapple productivity with a notable degree of precision and dependability. In this work, the artificial neural network (ANN) and the standard ELM was built, and assessed for its ability to forecast the productivity of Pattavia pineapples. The findings indicate that the ELM neural network is an innovative model characterized by its straightforward architecture and exceptional performance. Moreover, the utilization of particle swarm optimization (PSO), ant colony optimization (ACO) and the NIPSO algorithms significantly enhanced ELM performance when forecasting the productivity of Pattavia pineapples. The NIPSO-ELM model emerged as the most optimal ML model for accurately, reliably and stably forecasting the productivity of Pattavia pineapples in practical scenarios. The most optimal NIPSO-ELM models for the Loei provinces, Thailand exhibit the following performance metrics: RMSE = 304.36389, MAE = 243.29531, MAPE = 0.03753 and MASE = 0.93157. The most optimal NIPSO-ELM models for the Nong Khai provinces, Thailand exhibit: RMSE = 304.57352, MAE = 244.67834, MAPE = 0.03756 and MASE = 0.93296, respectively.

关键词： Pineapple extreme learning machine forecasting metaheuristic algorithms applied mathematics optimization algorithms agricultural production planning artificial neural networks

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Particle swarm based algorithms for finding locally and Bayesian D-optimal designs

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Journal of Statistical Distributions and Applications 2019年第1期6卷 3-3页

作者： Shi, Yu Zhang, Zizhao Wong, Weng Kee Department of Biostatistics University of California at Los Angeles 10833 Le Conte Avenue Los Angeles United States

When a model-based approach is appropriate, an optimal design can guide how to collect data judiciously for making reliable inference at minimal cost. However, finding optimal designs for a statistical model with several possibly interacting factors can be both theoretically and computationally challenging, and this issue is rarely discussed in the literature. We propose nature-inspired metaheuristic algorithms, like particle swarm optimization (PSO) and its variants, to solve such optimization problems. We demonstrate that such techniques, which are easy to implement, can find different types of optimal designs for models with several factors efficiently. To facilitate use of such algorithms, we provide computer codes to generate tailor made optimal designs and evaluate efficiencies of competing designs. As applications, we apply PSO and find Bayesian optimal designs for Exponential models useful in HIV studies and re-design a car-refuelling study for a Logistic model with ten factors and some interacting factors. © 2019, The Author(s).

关键词： Bayesian design Design efficiency Generalized linear model metaheuristic algorithms

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Integrating NIR spectroscopy with machine learning and heuristic algorithm-assisted wavelength selection algorithms for protein content prediction in rice bean (Vigna umbellata L.)

Food and Humanity

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Food and Humanity 2024年 3卷

作者： Singh, Naseeb Kaur, Simardeep Philanim, W.S. Kumar, Amit Sharma, Paras Ananthan, R. Bhardwaj, Rakesh ICAR-Research Complex for North Eastern Hill Region Meghalaya Umiam 793103 India Food Chemistry Division ICMR-National Institute of Nutrition Telangana State Hyderabad 500007 India Department of Food Technology Mizoram University Tanhril Mizoram Aizawl 796004 India Germplasm Evaluation Division National Bureau of Plant Genetic Resources New Delhi 110012 India

Protein-energy malnutrition (PEM) is a global concern, particularly in underdeveloped and underprivileged regions dependent on plant-based diets. Plant-based meat alternatives offer a potential solution, with rice bean emerging as a promising crop due to its high protein content. However, traditional methods for determining protein content in rice bean are labor-intensive, costly, and time-consuming, thus necessitating more efficient approaches. This study addresses the need for efficient, rapid technique by integrating Near-Infrared Spectroscopy (NIRS) with machine learning models to predict protein content in rice bean (Vigna umbellata L.) germplasm. We developed predictive models using Support Vector Regressor (SVR), Random Forest Regressor (RFR), and Modified Partial Least Square (MPLS) coupled with key wavelengths selection algorithms including Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Competitive Adaptive Reweighted Sampling (CARS). The MPLS model demonstrated superior performance, achieving an R² (Coefficient of Determination) of 0.81 and an RPD (Residual Prediction Deviation) of 2.14 when combined with the ACO algorithm. The SVR model also performed well when combined with ACO and PSO, yielding an R² of 0.78, an RPD of ∼1.95. In contrast, the RFR model showed relatively lower performance (R² of ∼0.45 and RPD ≤ 1.74). These results indicate that MPLS and SVR, combined with selected wavelength selection algorithms significantly improves prediction accuracy. The study highlights the importance of appropriate model and algorithm selection in enhancing predictive performance of protein content. The developed technology provides a rapid and efficient method for selecting nutritionally superior crop germplasm present in global respositories. © 2024 Elsevier B.V.

关键词： Germplasm screening Key wavelengths metaheuristic algorithms NIRS, Machine learning Rice bean

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