• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3126-3145
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• 2014

Cloud computing is a computing paradigm in which users can rent computing resources from service providers according to their requirements. A spot instance in cloud computing helps a user to obtain resources at a lower cost. However, a crucial weakness of spot instances is that the resources can be unreliable anytime due to the fluctuation of instance prices, resulting in increasing the failure time of users' job. In this paper, we propose a Genetic Algorithm (GA)-based workflow scheduling scheme that can find the optimal task size of each instance in a spot instance-based cloud computing environment without increasing users' budgets. Our scheme reduces total task execution time even if an out-of-bid situation occurs in an instance. The simulation results, based on a before-and-after GA comparison, reveal that our scheme achieves performance improvements in terms of reducing the task execution time on average by 7.06%. Additionally, the cost in our scheme is similar to that when GA is not applied. Therefore, our scheme can achieve better performance than the existing scheme, by optimizing the task size allocated to each available instance throughout the evolutionary process of GA.

• Structural Engineering and Mechanics
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• v.72 no.2
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• pp.275-291
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• 2019

In a super-large underground with "large span and high side wall", it is buried in mountains with uneven lithology, complicated geostress field and developed geological structure. These surrounding rocks are more susceptible to stability issues during the construction period. This paper takes the left bank of Baihetan hydropower station (span is 34m) as a case study example, wherein the deformation mechanism of surrounding rock appears prominent. Through analysis of geological, geophysical, construction and monitoring data, the deformation characteristics and factors are concluded. The failure mechanism, spatial distribution characteristics, and evolution mechanism are also discussed, where rock mechanics theory, $FLAC^{3D}$ numerical simulation, rock creep theory, and the theory of center point are combined. In general, huge underground cavern stability issues has arisen with respect to huge-scale and adverse geological conditions since settling these issues will have milestone significance based on the evolutionary pattern of the surrounding rock and the correlation analyses, the rational structure of the factors, and the method of nonlinear regression modeling with regard to the construction and development of hydropower engineering projects among the worldwide.

• Earthquakes and Structures
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• v.17 no.3
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• pp.307-315
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• 2019

Building prediction and control theory have been drawing the attention of many scientists over the past few years because design and control efficiency consumes the most financial and energy. In the literature, many methods have been proposed to achieve this goal by trying different control theorems, but all of these methods face some problems in correctly solving the problem. The Evolutionary Bat (EB) Algorithm is one of the recently introduced optimization methods and providing researchers to solve different types of optimization problems. This paper applies EB to the optimization of building control design. The optimized parameter is the input to the fuzzy controller, which gives the status response as an output, which in turn changes the state of the associated actuator. The novel control criterion for guarantee of the stability of the system is also derived for the demonstration in the analysis. This systematic and simplified controller design approach is the contribution for solving complex dynamic engineering system subjected to external disturbances. The experimental results show that the method achieves effective results in the design of closed-loop system. Therefore, by establishing the stability of the closed-loop system, the behavior of the closed-loop building system can be precisely predicted and stabilized.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.335-352
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• 2023

When solving multi-objective optimization problems, the blindness of the evolution direction of the population gradually emerges with the increase in the number of objectives, and there are also problems of convergence and diversity that are difficult to balance. The many- objective optimization problem makes some classic multi-objective optimization algorithms face challenges due to the huge objective space. The sine cosine algorithm is a new type of natural simulation optimization algorithm, which uses the sine and cosine mathematical model to solve the optimization problem. In this paper, a knee-driven many-objective sine-cosine algorithm (MaSCA-KD) is proposed. First, the Latin hypercube population initialization strategy is used to generate the initial population, in order to ensure that the population is evenly distributed in the decision space. Secondly, special points in the population, such as nadir point and knee points, are adopted to increase selection pressure and guide population evolution. In the process of environmental selection, the diversity of the population is promoted through diversity criteria. Through the above strategies, the balance of population convergence and diversity is achieved. Experimental research on the WFG series of benchmark problems shows that the MaSCA-KD algorithm has a certain degree of competitiveness compared with the existing algorithms. The algorithm has good performance and can be used as an alternative tool for many-objective optimization problems.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.47-52
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• 2023

With the growing data size and the increased computing load in machine learning, energy-efficient resource planning in IoT systems is becoming increasingly important. In this paper, we suggest a new resource planning policy for real-time workloads that can be fluctuated over time in IoT systems. To handle such situations, we categorize real-time tasks into fixed tasks and variable tasks, and optimize the resource planning for various workload conditions. Based on this, we initiate the IoT system with the configuration for the fixed tasks, and when variable tasks are activated, we update the resource planning promptly for the situation. Simulation experiments show that the proposed policy saves the processor and memory energy significantly.

• Steel and Composite Structures
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• v.42 no.5
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• pp.591-598
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• 2022

The article deals with the possibilities of vibration stimulation. Based on the stability analysis, a multi-scale approach with a modified whole-building model is implemented. The motion equation is configured for a controlled bridge with a MDOF (multiple dynamic degrees of freedom) Tuned Mass Damper (M-TMD) system, and a combination of welding, excitation, and control effects is used with its advanced packages and commercial software submodel. Because the design of high-performance and efficient structural systems has been of interest to practical engineers, systematic methods of structural and functional synthesis of control systems must be used in many applications. The smart method can be stabilized by properly controlling the high frequency injection limits. The simulation results illustrate that the multiple modeling method used is consistent with the accuracy and high computational efficiency. The M-TMD system, even with moderate reductions in critical pressure, can significantly suppress overall feedback on an unregulated design.

• Advances in Computational Design
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• v.8 no.4
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• pp.327-351
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• 2023

This article explores how multi-objective optimization techniques can be used to design cost-effective and structurally optimal spatial steel structures, highlighting that optimizing performance can be as important as minimizing costs in real-world engineering problems. The study includes the minimization of maximum horizontal displacement, the maximization of the first natural frequency of vibration, the maximization of the critical load factor concerning the first global buckling mode of the structure, and weight minimization as the objectives. Additionally, it outlines a systematic approach to selecting the best design by employing four different evolutionary algorithms based on differential evolution and a multi-criteria decision-making methodology. The paper's contribution lies in its comprehensive consideration of multiple conflicting objectives and its novel approach to simultaneous consideration of bracing system, column orientation, and commercial profiles as design variables.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.890-903
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• 2017

The problem of determining a smooth and collision-free path with maximum possible speed for a Mobile Robot (MR) which is chasing a moving target in a dynamic environment is addressed in this paper. Genetic Network Programming with Reinforcement Learning (GNP-RL) has several important features over other evolutionary algorithms such as it combines offline and online learning on the one hand, and it combines diversified and intensified search on the other hand, but it was used in solving the problem of MR navigation in static environment only. This paper presents GNP-RL based on predicting collision positions as a first attempt to apply it for MR navigation in dynamic environment. The combination between features of the proposed collision prediction and that of GNP-RL provides safe navigation (effective obstacle avoidance) in dynamic environment, smooth movement, and reducing the obstacle avoidance latency time. Simulation in dynamic environment is used to evaluate the performance of collision prediction based GNP-RL compared with that of two state-of-the art navigation approaches, namely, Q-Learning (QL) and Artificial Potential Field (APF). The simulation results show that the proposed GNP-RL outperforms both QL and APF in terms of smooth movement and safer navigation. In addition, it outperforms APF in terms of preserving maximum possible speed during obstacle avoidance.

• Computers and Concrete
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• v.8 no.6
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• pp.647-675
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• 2011

Our ability to predict hydration behavior is becoming increasingly relevant to the concrete community as modelers begin to link material performance to the dynamics of material properties and chemistry. At early ages, the properties of concrete are changing rapidly due to chemical transformations that affect mechanical, thermal and transport responses of the composite. At later ages, the resulting, nano-, micro-, meso- and macroscopic structure generated by hydration will control the life-cycle performance of the material in the field. Ultimately, creep, shrinkage, chemical and physical durability, and all manner of mechanical response are linked to hydration. As a way to enable the modeling community to better understand hydration, a review of hydration models is presented offering insights into their mathematical origins and relationships one-to-the-other. The quest for a universal model begins in the 1920's and continues to the present, and is marked by a number of critical milestones. Unfortunately, the origins and physical interpretation of many of the most commonly used models have been lost in their overuse and the trail of citations that vaguely lead to the original manuscripts. To help restore some organization, models were sorted into four categories based primarily on their mathematical and theoretical basis: (1) mass continuity-based, (2) nucleation-based, (3) particle ensembles, and (4) complex multi-physical and simulation environments. This review provides a concise catalogue of models and in most cases enough detail to derive their mathematical form. Furthermore, classes of models are unified by linking them to their theoretical origins, thereby making their derivations and physical interpretations more transparent. Models are also used to fit experimental data so that their characteristics and ability to predict hydration calorimetry curves can be compared. A sort of evolutionary tree showing the progression of models is given along with some insights into the nature of future work yet needed to develop the next generation of cement hydration models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4357-4378
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• 2017

In cooperative multi-relay networks, the relay nodes which are selected are very important to the system performance. How to choose the best cooperative relay nodes is an optimization problem. In this paper, multi-relay selection schemes which consider either single objective or multi-objective are proposed based on evolutionary algorithms. Firstly, the single objective optimization problems of multi-relay selection considering signal to noise ratio (SNR) or power efficiency maximization are solved based on the quantum bee colony optimization (QBCO). Then the multi-objective optimization problems of multi-relay selection considering SNR maximization and power consumption minimization (two contradictive objectives) or SNR maximization and power efficiency maximization (also two contradictive objectives) are solved based on non-dominated sorting quantum bee colony optimization (NSQBCO), which can obtain the Pareto front solutions considering two contradictive objectives simultaneously. Simulation results show that QBCO based multi-relay selection schemes have the ability to search global optimal solution compared with other multi-relay selection schemes in literature, while NSQBCO based multi-relay selection schemes can obtain the same Pareto front solutions as exhaustive search when the number of relays is not very large. When the number of relays is very large, exhaustive search cannot be used due to complexity but NSQBCO based multi-relay selection schemes can still be used to solve the problems. All simulation results demonstrate the effectiveness of the proposed schemes.