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metaheuristic algorithms to allocate and schedule of the rescue units in the natural disaster with fatigue effect

NEURAL COMPUTING & APPLICATIONS 2019年第11期31卷 7517-7537页

作者： Nayeri, Sina Asadi-Gangraj, Ebrahim Emami, Saeed Babol Noshirvani Univ Technol Dept Ind Engn Babol Sar Iran

During each year, natural disasters like floods, hurricanes, tornadoes, earthquakes, and mass movements cause enormous damages to the people and infrastructure. Designing an effective decision support model to allocate and schedule of the rescue units can reduce economic losses and casualties in the natural disasters. By assuming the incidents as jobs and rescue units as machines, we can formulate the research problem as an unrelated parallel machine scheduling problem. In this paper, a mixed integer linear programming model is proposed to minimize the sum of the weighted completion times and delays at the start of relief operations. After relieving several incidents, rescuers will become tired and then need more time to relieve the remaining incidents which were assigned to them;therefore, we consider this phenomenon as fatigue effect in this research. The rescue units also have different capabilities, and each incident just can be allocated to a rescue unit that is able to do it. Due to NP-hardness of the research problem, three metaheuristic algorithms, namely simulated annealing (SA) algorithm, particle swarm optimization (PSO) algorithm, and a method based on hybrid SA and PSO (SA-PSO), are developed to solve the research problem. Finally, the developed metaheuristic algorithms are ranked by applying the technique for order of preference by similarity to ideal solution. The experimental results illustrate that the SA algorithm and the hybrid SA-PSO are better than others in terms of CPU time and solution quality, respectively.

关键词： Natural disaster management Fatigue effect Unrelated parallel machines scheduling metaheuristic algorithms TOPSIS

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metaheuristic algorithms For Portfolio Selection Problem: An Overview Including Crypto-Assets

Metaheuristic Algorithms For Portfolio Selection Problem: An...

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International Conference on Decision Aid Sciences and Applications (DASA)

作者： Alaya, Houda Youssef, Meriem Univ Tunis Tunis Business Sch Business Analyt & Decis Making Lab Tunis Tunisia IHE Paris Logist & Innovat Technol Res Ctr Paris France Univ Sousse Higher Inst Finance & Taxat ISFF Sousse Tunisia LaREMFiQ IHEC BP 40 Route Ceinture Sahloul 3 Sousse 4054 Tunisia

ISBN: (纸本)9781665495011

Due to the NP-hard nature of the portfolio optimization problem, a plethora of techniques, such as metaheuristic algorithms have been reported and used in the literature. Their capacity to produce top-quality solutions in reduced computing times makes them a seductive tool for decision-makers (investors, portfolio managers, policymakers ... ) by supplying real-time solutions. This paper provides an overview of the literature on the use of metaheuristics for solving NP-hard versions of portfolio selection optimization and focuses on their readiness to provide high-quality solutions. In addition, the existing literature including crypto-assets is highlighted. A major finding of this paper is that studies conducted in this specific framework are scarce.

关键词： Portfolio Selection metaheuristic algorithms Crypto-assets

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metaheuristic algorithms IN EXTREME LEARNING MACHINE FOR SELECTION OF PARAMETERS IN ACTIVATION FUNCTION 37

METAHEURISTIC ALGORITHMS IN EXTREME LEARNING MACHINE FOR SEL...

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37th Annual European Simulation and Modelling Conference (ESM)

作者： Struniawski, Karol Konopka, Aleksandra Kozera, Ryszard Warsaw Univ Life Sci SGGW Inst Informat Technol Ul Nowoursynowska 166 PL-02776 Warsaw Poland Univ Western Australia Sch Phys Math & Comp 35 Stirling Highway Perth WA Australia

ISBN: (纸本)9789492859280

This research investigates the integration of metaheuristic algorithms (MAs) with the Extreme Learning Machine (ELM) model to optimize parameters of activation function. While MAs have traditionally been employed for weights selection, a methodology that utilizes MA for the selection of activation function parameters was proposed. The performance of 24 distinctive activation functions was evaluated on diverse and widespread benchmark datasets: Ionosphere, Breast Cancer, Australian Credits, Musk and Banana. The results demonstrate a strong dependence on selecting an optimal activation function for each task, with variations in accuracy ranging up to 60 percentage points. The MA-ELM approach shows promising results, providing improved accuracy and reducing the number of required neurons in certain cases. The approach offers an efficient alternative to the typical MA-ELM method, requiring evaluation of only a few parameter values compared to the optimization of hundreds or thousands of weights. This approach enhances the generalization abilities of core ELM method and reduces computational time in comparison to typical MA-ELM. These findings validate the effectiveness of the proposed MA-ELM approach, contributing to the understanding of integrating MA with activation functions in ELM and offering insights for enhancing model performance in various applications.

关键词： Machine Learning metaheuristic algorithms Extreme Learning Machine Activation Function Optimization Computational Efficiency

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metaheuristic algorithms and Polynomial Turing Reductions: A Case Study Based on Ant Colony Optimization

Metaheuristic Algorithms and Polynomial Turing Reductions: A...

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International Conference on Information and Communication Technologies (ICICT)

作者： Prakasam, Anandkumar Savarimuthu, Nickolas Natl Inst Technol Dept Comp Applicat Tiruchirappalli Tamil Nadu India

Nowadays, there is an increasing dependence on metaheuristic algorithms for solving combinatorial optimization problems. This paper discusses various metaheuristic algorithms, their similarities and differences and how Ant Colony Optimization algorithm is found to be much more suitable for providing a generic implementation. We start with the solution for Travelling Salesman Problem using Ant Colony Optimization (ACO) and show how Polynomial Turing Reduction helps us solve Job Shop Scheduling and Knapsack Problems without making considerable changes in the implementation. The probabilistic nature of metaheuristic algorithms, especially ACO helps us to a greater extent in avoiding parameter fine-tuning. Through Sensitivity analysis we find that ACO exhibits better resilience to changes in parameter values in comparison to other metaheuristic algorithms. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： Ant Colony Optimization Turing Reductions metaheuristic algorithms Travelling Salesman Problem Job Shop Scheduling Knapsack Problem

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metaheuristic algorithms for Enhancing Multicepstral Representation in Voice Spoofing Detection: An Experimental Approach 15th

Metaheuristic Algorithms for Enhancing Multicepstral Represe...

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15th International Conference on Advances in Swarm Intelligence (ICSI)

作者： Contreras, Rodrigo Colnago Heck, Gustavo Luiz Viana, Monique Simplicio dos Santos Bongarti, Marcelo Adriano Zamani, Hoda Guido, Rodrigo Capobianco Sao Paulo State Univ UNESP Inst Biosci Letters & Exact Sci Sao Jose Do Rio Preto SP Brazil Univ Sao Paulo Sao Carlos SP Brazil Univ Fed Sao Carlos Sao Carlos SP Brazil Weierstr Inst Berlin Germany Islamic Azad Univ Fac Comp Engn Najafabad Iran Islamic Azad Univ Najafabad Branch Big Data Res Ctr Najafabad Iran

ISBN: (纸本)9789819771806;9789819771813

The problem of voice spoofing detection is critical for identity authentication within biometric systems. Among the existing countermeasures, those based on soft computing have received attention from researchers in the last few years. However, it is known that spoofing representation is only effective when many features are used, which limits its applicability due to the curse of dimensionality. Accordingly, we focus on strategies to reduce the dimensionality of multicepstral features while maintaining reasonable accuracy in distinguishing between real and spoofed voices. Given the complexity of voice data, identifying and prioritizing the features with the highest information content is of utmost relevance. The study utilized four metaheuristic algorithms-GA, DA, PSO, and GWO for dimension reduction. The findings indicate that all algorithms, particularly GWO, exceed baseline performance levels. This demonstrates their efficacy in detecting voice spoofing. Moreover, it was found that certain combinations of cepstral coefficients when applied with principal component analysis projection, notably enhanced the model's performance of voice spoofing detection.

关键词： Spoofing Detection Cepstral Features Dimensionality Reduction metaheuristic algorithms

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metaheuristic algorithms for Circle Packing Problem: A Comprehensive Review 9th

Metaheuristic Algorithms for Circle Packing Problem: A Compr...

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9th International Conference on metaheuristics and Nature Inspired Computing (META)

作者： Kumar, Yogesh Deep, Kusum Indian Inst Technol Roorkee Dept Math Roorkee Uttar Pradesh India

ISBN: (纸本)9783031692567;9783031692574

The Circle Packing Problem (CPP) is a well-known combinatorial optimization problem with a wide range of applications such as logistics, cable allocation and many more. It is difficult to identify exact solutions for the CPP since it is a NP-hard problem. As a result, metaheuristic algorithms are efficient approach to address this challenging problem. This paper presents an extensive literature review of the role and effectiveness of metaheuristic algorithms in solving the CPP. The objective of this paper is to examine the potential of metaheuristic algorithms in solving the CPP and application advancements. Moreover, case studies and real-world examples of how metaheuristics have been used to address the CPP. Finally, some suggested possible future areas are also highlighted for the researchers who want to utilize metaheuristic algorithms for solving the CPP and the real world problem which can be modeled as CPP.

关键词： metaheuristic algorithms Combinatorial Optimization Circle Packing Problem Sphere Packing Problem

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A new mesh stiffness determination method in a straight bevel gear system through experimental modal analysis and metaheuristic algorithms

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MEASUREMENT 2024年 238卷

作者： Talakesh, Alireza Jazi, Shahram Hadian Ariaei, Alireza Poursina, Mehrdad Univ Isfahan Dept Mech Engn Esfahan *** Iran

The mesh stiffness plays a crucial role in influencing the performance and behavior of straight bevel gear (SBG) systems. Precisely determining the mesh stiffness enables to assess the SBG system's dynamic behavior more accurately and anticipate potential concerns such as crack identification and noise reduction. A novel experimental method is developed, employing experimental modal analysis associated with metaheuristic algorithms and an innovative setup. This method effectively determines the mesh stiffness in healthy and cracked systems. Additionally, an analytical method based on the potential energy associated with crack modeling is proposed. Both methods are implemented on SBG systems with varying crack depths. The results obtained from the experimental method are compared with those from the analytical method, revealing good agreement between them. This demonstrates that the newly proposed experimental method effectively considers all parts of mesh stiffness and is appropriate for determining the mesh stiffness in healthy and cracked SBG systems.

关键词： Dynamic model Experimental modal analysis Mesh stiffness determination metaheuristic algorithms Optimization problem Straight bevel gear system

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Stability prediction of hard rock pillar using support vector machine optimized by three metaheuristic algorithms

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International Journal of Mining Science and Technology 2023年第8期33卷 1019-1036页

作者： Chuanqi Li Jian Zhou Kun Du Daniel Dias School of Resources and Safety Engineering Central South UniversityChangsha 410083China Laboratory 3SR CNRS UMR 5521Grenoble Alpes UniversityGrenoble 38000France

Hard rock pillar is one of the important structures in engineering design and excavation in underground *** and convenient prediction of pillar stability is of great significance for underground space *** paper aims to develop hybrid support vector machine(SVM)models improved by three metaheuristic algorithms known as grey wolf optimizer(GWO),whale optimization algorithm(WOA)and sparrow search algorithm(SSA)for predicting the hard rock pillar *** integrated dataset containing 306 hard rock pillars was established to generate hybrid SVM *** parameters including pillar height,pillar width,ratio of pillar width to height,uniaxial compressive strength and pillar stress were set as input *** global indices,three local indices and the receiver operating characteristic(ROC)curve with the area under the ROC curve(AUC)were utilized to evaluate all hybrid models’*** results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local ***,the performance of the SSASVM model for predicting the unstable pillar(AUC:0.899)is not as good as those for stable(AUC:0.975)and failed pillars(AUC:0.990).To verify the effectiveness of the proposed models,5 field cases were investigated in a metal mine and other 5 cases were collected from several published *** validation results indicated that the SSA-SVM model obtained a considerable accuracy,which means that the combination of SVM and metaheuristic algorithms is a feasible approach to predict the pillar stability.

关键词： Underground pillar stability Hard rock Support vector machine metaheuristic algorithms

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CHAOS THEORY, ADVANCED metaheuristic algorithms AND THEIR NEWFANGLED DEEP LEARNING ARCHITECTURE OPTIMIZATION APPLICATIONS: A REVIEW

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FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2024年第3期32卷 2430001-2430001页

作者： Akgul, Akif Karaca, Yell'z Pala, Muhammed Ali Cimen, Murat Erhan Boz, Ali Fuat Yildiz, Mustafa Zahid Hitit Univ Fac Engn Dept Comp Engn Corum Turkiye Univ Massachusetts Chan Med Sch Worcester MA 01655 USA Sakarya Univ Appl Sci Fac Technol Dept Elect & Elect Engn Sakarya Turkiye

metaheuristic techniques are capable of representing optimization frames with their specific theories as well as objective functions owing to their being adjustable and effective in various applications. Through the optimization of deep learning models, metaheuristic algorithms inspired by nature, imitating the behavior of living and non-living beings, have been used for about four decades to solve challenging, complex, and chaotic problems. These algorithms can be categorized as evolution-based, swarm-based, nature-based, human-based, hybrid, or chaos-based. Chaos theory, as a useful approach to understanding neural network optimization, has the basic idea of viewing the neural network optimization as a dynamical system in which the equation schemes are utilized from the space pertaining to learnable parameters, namely optimization trajectory, to itself, which enables the description of the evolution of the system by understanding the training behavior, which is to say the number of iterations over time. The examination of the recent studies reveals the importance of chaos theory, which is sensitive to initial conditions with randomness and dynamical properties that are principally emerging on the complex multimodal landscape. Chaotic optimization, in this regard, accelerates the speed of the algorithm while also enhancing the variety of movement patterns. The significance of hybrid algorithms developed through their applications in different domains concerning real-world phenomena and well-known benchmark problems in the literature is also evident. metaheuristic optimization algorithms have also been applied to deep learning or deep neural networks (DNNs), a branch of machine learning. In this respect, the basic features of deep learning and DNNs and the extensive use of metaheuristic algorithms are overviewed and explained. Accordingly, the current review aims at providing new insights into the studies that deal with metaheuristic algorithms, hybrid-based metaheu

关键词： metaheuristic algorithms Chaos-based metaheuristic algorithms Evolution-based metaheuristic algorithms Swarm-based metaheuristic algorithms Nature-based metaheuristic algorithms Human-based metaheuristic algorithms Hybrid metaheuristic algorithms Architecture Optimization Deep Learning Engineering Applications Natural Mathematics Engineering Mathematics Applicability-based optimization Chaos Theory

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Active structural control via metaheuristic algorithms considering soil-structure interaction

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STRUCTURAL ENGINEERING AND MECHANICS 2020年第2期75卷 175-191页

作者： Ulusoy, Serdar Bekdas, Gebrail Nigdeli, Sinan Melih Yeditepe Univ Dept Civil Engn TR-34755 Istanbul Turkey Istanbul Univ Cerrahpasa Dept Civil Engn TR-34320 Istanbul Turkey

In this study, multi-story structures are actively controlled using metaheuristic algorithms. The soil conditions such as dense, normal and soft soil are considered under near-fault ground motions consisting of two types of impulsive motions called directivity effect (fault normal component) and the flint step (fault parallel component). In the active tendon-controlled structure, Proportional-Integral-Derivative (PID) type controller optimized by the proposed algorithms was used to achieve a control signal and to produce a corresponding control force. As the novelty of the study, the parameters of PID controller were determined by different metaheuristic algorithms to find the best one for seismic structures. These algorithms are flower pollination algorithm (FPA), teaching learning based optimization (TLBO) and Jaya Algorithm (JA). Furthermore, since the influence of time delay on the structural responses is an important issue for active control systems, it should be considered in the optimization process and time domain analyses. The proposed method was applied for a 15-story structural model and the feasible results were found by limiting the maximum control force for the near-fault records defined in FEMA P-695. Finally, it was determined that the active control using metaheuristic algorithms optimally reduced the structural responses and can be applied for the buildings with the soil-structure interaction (SSI).

关键词： active tendon control PID control metaheuristic algorithms soil-structure interaction near-fault ground motions

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