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5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)

作者： Song, Qun Fong, Simon Univ Macau Dept Comp & Informat Sci Macau Macau Sar Peoples R China

ISBN: (纸本)9781467389853

metaheuristic algorithms have been a very important topic in computer science since the start of evolutionary computing the Genetic algorithms 1950s. By now these metaheuristic algorithms have become a very large family with successful applications in industry. A challenge which is always pondered on, is finding the suitable metaheuristic algorithm for a certain problem. The choice sometimes may have to be made after trying through many experiments or by the experiences of human experts. As each of the algorithms have their own strengths in solving different kinds of problems, in this paper we propose a framework of metaheuristic brick-up system. The flexibility of brick-up (like Lego) offers users to pick a collection of fundamental functions of metaheuristic algorithms that were known to perform well in the past. In order to verify this brick-up concept, in this paper we propose to use the Monte Carlo method with upper confidence bounds applied to a decision tree in selecting appropriate functional pieces. This paper validates the basic concept and discusses the further works.

关键词： metaheuristic algorithms Brick-up system AI UCT Monte Carlo tree

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Integrating Local Search Methods in metaheuristic algorithms for Combinatorial Optimization: The Traveling Salesman Problem and its Variants 4

Integrating Local Search Methods in Metaheuristic Algorithms...

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4th IEEE-Nigeria-Section International Conference on Disruptive Technologies for Sustainable Development (IEEE NIGERCON)

作者： Jeremiah, Isuwa Abdullahi, Mohammed Yusuf, Sahabi Ali Idris, Muhammad Nuruddeen Garko, Baffa Shuaibu Haruna, Muhammad Yusuf Fed Univ Kashere Comp Sci Dept Gombe Nigeria Ahmadu Bello Univ Comp Sci Dept Zaria Nigeria Fed Univ Gusau Comp Sci Dept Zamfara Nigeria

ISBN: (纸本)9781665479783

metaheuristic algorithms (MAs) have demonstrated exceptional competence in solving Combinatorial Optimization Problems (COP) such as the Minimum Spanning Tree Problem (MSTP), the Features Selection Problem (FSP) with the most popular and oldest being the Traveling Salesman Problem (TSP). However, this class of algorithms many a time suffers from local optima stagnation leading to sub-optimal performance. Thus, there is a need for such algorithms to be supported with specific Local Search (LS) procedures either as an inner component or as a post-processing mechanism to enhance the search process for better performance. This paper presents a comprehensive review of the integration of LS methods in metaheuristic algorithms for solving single, multi, and many-objective COP with a focus on the TSP and its variants. The LS methods reviewed in this study were classified into one-way and two-way based on their mode of operation. In addition, practical suggestions were discussed and possible future directions were pointed out.

关键词： combinatorial optimization metaheuristic algorithms traveling salesman problem local search single multi and many-objective

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Design and Comparative Analysis of Synchronous Reference Frame based Enhanced Power Quality Management with metaheuristic algorithms 13

Design and Comparative Analysis of Synchronous Reference Fra...

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13th International Conference on Renewable Energy Research and Applications

作者： Tepe, Izviye Fatimanur Kaplan, Orhan Irmak, Erdal Bayindir, Ramazan Gazi Univ Grad Sch Nat & Appl Sci Dept Elect & Elect Engn Ankara Turkiye Gazi Univ Fac Technol Dept Elect & Elect Engn Ankara Turkiye

ISBN: (纸本)9798350375596;9798350375589

Harmonic distortion is a significant challenge in power systems that degrades energy quality and shortens the lifespan of equipment. Therefore, effectively managing and minimizing these distortions is crucial for the reliability and efficiency of modern power grids. This paper has been presented a shunt active power filter (SAPF) designed for a three-phase power system to address issues such as harmonic distortion and reactive power caused by nonlinear loads. A design incorporating synchronous reference frame (SRF) theory and hysteresis control methods to provide effective compensation and to supply pure and balanced current to the grid have been proposed in this study. Furthermore, a metaheuristic-based PI control has been used to regulate the DC bus voltage, which contributes to reducing the total harmonic distortion (THD) and improving energy efficiency. A comparison of different metaheuristic algorithms, such as particle swarm optimization and gray wolf algorithm, have been conducted to assess their efficacy in stabilization of DC bus voltage of SAPF. The results demonstrated that the proposed system effectively addresses the challenges posed by nonlinear loads, with particle swarm optimization exhibiting the most favourable performance for voltage regulation. This work contributes to the advancement of power quality, system stability, and the demonstration of the benefits of metaheuristic-based PI control in an active power filter.

关键词： shunt active power filter (PAF) synchronous reference frame (SRF) d-q control nonlinear loads harmonic compensation metaheuristic algorithms PSO GWO

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Forward and backward modeling of direct metal deposition using metaheuristic algorithms tuned artificial neural network and extreme gradient boost

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PROGRESS IN ADDITIVE MANUFACTURING 2022年第4期7卷 627-641页

作者： Dhar, Ananda Rabi Gupta, Dhrubajyoti Roy, Shibendu Shekhar Lohar, Aditya Kumar Natl Inst Technol Durgapur Dept Mech Engn Durgapur 713209 W Bengal India Cent Mech Engn Res Inst Surface Engn & Tribol Dept Durgapur 713209 W Bengal India

To enhance the automation in additive manufacturing technology, establishment of accurate relationship between the process parameters and responses is extremely important. Direct metal deposition (DMD) or laser metal deposition (LMD) is an ever-emerging field in additive manufacturing spectrum because of its higher build-up rate with flexibility at multiple scales and reduced material wastage. In this work, a feed-forward neural network-based model is proposed for predicting the clad height and capture efficiency while building austenitic steel part through DMD process with variations in input parameters, namely laser power, scan speed, and powder flow rate. With an aim to enhance the prediction performance, the model is hybridized with ancient metaheuristic algorithms, namely genetic algorithm and particle swarm optimization as well as some rare and new metaheuristic algorithms, namely firefly algorithm, bio-geography-based optimization, flower pollination algorithm, dragonfly algorithm, and gray wolf optimization. The backward mapping model is also established along similar lines using the same hybridization schema and all the approaches are comparatively studied. The bi-directional models are further investigated by applying extreme gradient boost (XGBoost), a new and emerging paradigm in the field of machine learning. The comparison is further emphasized by employing a statistical test known as 'technique for order of preference by similarity to ideal solution (TOPSIS).' The novelty of the present study lies in utilizing the aforementioned rare and new metaheuristic algorithms for training artificial neural networks in order to develop predictive models in the domain of DMD as well as application of XGBoost for the same. The results clearly recommend the application of hybridized computational intelligence-based approaches in case of the forward mapping model and XGBoost in case of the backward mapping model.

关键词： Additive manufacturing direct metal deposition artificial neural network metaheuristic algorithms forward mapping backward mapping extreme gradient boost

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A Novel Intelligent Technique Based on metaheuristic algorithms and Artificial Neural Networks: Application on a Photovoltaic Panel 2

A Novel Intelligent Technique Based on Metaheuristic Algorit...

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2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)

作者： Ncir, Noamane Sebbane, Saliha El Akchioui, Nabil Univ Abdelmalek Essaadi Fac Sci & Technol Phys Dept Tetouan Morocco

ISBN: (纸本)9781665422093

This article presents a novel methodology of optimization based on metaheuristic algorithms for optimization including Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and Imperialist Competitive Algorithm (ICA), and Artificial Neural Networks. The optimization method is mainly based on reducing the percentage of error before training the created neural network. For that, after the collection of dataset of the chosen system, those algorithms identifies the best configuration of weights and bias to train the Artificial Neural Network (ANN). However, metaheuristic methods work in a different way than classical methods, i.e. the mathematical modeling of these algorithms takes into consideration stochastic parameters and decisions. In this paper, all algorithms are validated by simulation using MATLAB software.

关键词： Artificial Neural Network Bayesian Regularization metaheuristic algorithms Optimization Particle Swarm Optimization Grey Wolf Optimization Whale Optimization Algorithm Imperialist Competitive Algorithm Photovoltaic Panel

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Feature Selection Using metaheuristic algorithms: Concept, Applications and Population Based Comparison

Feature Selection Using Metaheuristic Algorithms: Concept, A...

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International Conference on Computational Performance Evaluation (ComPE)

作者： Hans, Rahul Kaur, Harjot Guru Nanak Dev Univ Dept Comp Sci & Engn Reg Campus Gurdaspur Punjab India

ISBN: (纸本)9781728166445

Technologies like machine learning in the current times, have emerged as capable domains of research in Computer Science. In Machine learning, the system is trained on the basis of the available dataset;the dataset may contain many redundant and irrelevant features which may require more memory for storage and also increases the cost of computation. Selection of best features enhances the accuracy of data classification along with working on smallest amount of features is considered as an optimization problem. metaheuristic algorithms in current times have been used far and wide unravel various optimization problems. In this context, this study aims to discuss the solution of feature selection problem using metaheuristic algorithms and presents a population based comparison of four metaheuristic algorithms for extracting smallest feature subset with utmost accuracy.

关键词： Machine Learning Feature Selection Optimization metaheuristic algorithms K-NN

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A Gene Expression Programming Framework for Evolutionary Design of metaheuristic algorithms

A Gene Expression Programming Framework for Evolutionary Des...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Rahati, Amin Rakhshani, Hojjat Univ Sistan & Baluchestan Dept Comp Sci Zahedan Iran

ISBN: (纸本)9781509006229

metaheuristic algorithms have successfully tackled many difficult and ill-conditioned optimization problems. Nevertheless, performance of these methods is subjected to the complexity and fitness landscape of the problem at hand. Accordingly, designing metaheuristic algorithms that work well on a variety of optimization problems is not a trivial task. In this study, we introduce a novel framework for improving generalization capability of the metaheuristic algorithms based on the notion of gene expression programming (GEP). The proposed framework introduces a modified GEP (MGEP) in order to adaptively design search operators of a metaheuristic algorithm. During evolution process, a multi-criteria procedure determines the search operators that are preferable and can obtain high accuracy results. Performance of the proposed approach is empirically evaluated on CEC 2013 test suite. The obtained results confirm that the evolved metaheuristic algorithms by this framework perform similarly to or better than the standard versions.

关键词： Optimization metaheuristic algorithms Genetic programming Gene Expression Programming

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Optimizing Feature Selection and Oversampling Using metaheuristic algorithms for Binary Fraud Detection Classification 19th

Optimizing Feature Selection and Oversampling Using Metaheur...

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19th International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Biltawi, Mariam M. Qaddoura, Raneem Faris, Hossam Al Hussein Tech Univ Amman 11831 Jordan Univ Granada Res Ctr Informat & Commun Technol CITIC UGR Granada Spain Univ Jordan King Abdullah II Sch Informat Technol Amman 11942 Jordan

ISBN: (纸本)9783031341137;9783031341113;9783031341106

Identifying fraudulent transactions and preventing unauthorized individuals from revealing credit card information are essential tasks for different financial entities. Fraud detection systems are used to apply this task by identifying the fraudulent transactions from the normal ones. Usually, the data used for fraud detection is imbalanced, containing many more instances of normal transactions than fraudulent ones. This causes diminished classification task results because it is hard to train a classifier that distinguishes between them. Another problem is caused by many features under study for the fraud detection task. This paper utilizes different metaheuristic algorithms for feature selection to solve the problem of unneeded features and uses the Synthetic Minority Oversampling TEchnique (SMOTE) to solve the imbalance problem of the data using different classification algorithms. The meta-heuristic algorithms include Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), and A MultiVerse Optimizer (MVO), whereas the classification algorithms include Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB) algorithms. The results show that applying the oversampling technique generated better results for the G-Mean and Recall values, while the feature selection process enhanced the results of almost all the classification algorithms.

关键词： Fraud Detection SMOTE Oversampling Classification metaheuristic algorithms Optimization

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Analog Filter Group Delay Optimization using metaheuristic algorithms: A Comparative Study

Analog Filter Group Delay Optimization using Metaheuristic A...

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International Conference on Artificial Intelligence and Data Processing (IDAP)

作者： Kuyu, Yigit Cagatay Vatansever, Fahri Uludag Univ Elect Elect Engn Dept Bursa Turkey

ISBN: (纸本)9781538668788

Analog filters have been successfully used in many real-life applications Minimizing group delay ripple can be very challenging so metaheuristic algorithms are often needed to deal with this problem. In this paper, four different metaheuristic algorithms, namely backtracking search, grey wolf optimizer, lightning search and multi-verse optimizer algorithms, have been applied for optimization of the analog filter group delay. The evaluations of the algorithms performances are tested using the fifth-order Chebyshev low-pass filter as a base filter, then the second, third and fourth order all-pass filter structures are connected to it in cascade form respectively. Afterwards, group delay of the filter is minimized for the each cascaded all-pass filter structure. The group delay responses and the optimal parameters of the optimized filters have been determined. Comparisons have been made with the metaheuristic algorithms as well as other design techniques published in the literature to analyze which method works more efficiently on which filter structure.

关键词： Analog filter group delay optimization metaheuristic algorithms

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BIM-based Applications of metaheuristic algorithms to Support the Decision-making Process: Uses in the Planning of Construction Site Layout

BIM-based Applications of Metaheuristic Algorithms to Suppor...

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6th Creative Construction Conference (CCC)

作者： Amiri, Roya Sardroud, Javad Majrouhi de Soto, Borja Garcia Islamic Azad Univ Cent Tehran Branch Dept Civil & Construct Engn Tehran Iran Swiss Fed Inst Technol Inst Construct & Infrastruct Management Zurich Switzerland

The use of Building Information Modeling (BIM) is affecting the way in which construction projects are planned, designed, executed and operated. One of the main goals of BIM is to provide, based on a 3D model, an accurate information model in a digital format to give different project participants better tools when evaluating different options to support their decisions about the project at a given phase. With this in mind, BIM provides a suitable framework to support the decision-makings process by aggregating the necessary information at the right time, and clarifying details and existing conditions;however, the different elements required to make an optimized decision need additional consideration. To address that, this paper explores the value of metaheuristic algorithms for reaching an optimized solution. The use of metaheuristic algorithms is well known in various aspects of knowledge optimization. This paper provides an overall review of the applications of metaheuristic algorithms in BIM-based optimized decisions in the construction industry and focuses on applications to the planning of construction site layout. Based on the findings from this paper, research gaps in this area have been identified and suggestions for future research to address those gaps are suggested. (c) 2017 The Authors. Published by Elsevier Ltd.

关键词： Building Information Modeling metaheuristic algorithms Optimization Site Layout

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