752 publications from this institution
Metaheuristic algorithms are effective for optimization with diverse applications in engineering. The optimum tuning of tuned mass dampers is very important for seismic structures excited by random vibrations, and optimization techniques have been used to obtain the best performance for optimally tuned mass dampers. In this study, a novel optimization approach employing the bat algorithm with several modifications for the tuned mass damper optimization problem is presented. In the proposed method, the design variables such as the mass, period and damping ratio of tuned mass damper are optimized and different earthquake records are considered during the optimization process. The method is then applied to a ten-story civil structure and the results are then compared with the analytical methods and other methods such as genetic algorithms, particle swarm optimization, and harmony search. The comparison shows that the proposed method is more effective than other compared methods. Additionally, the robustness of the optimum results was evaluated. The proposed approach for optimizating tuned mass dampers via the bat algorithm is a feasible and efficient approach.
The firefly algorithm has been proved to be effective to solve multimodal optimization problems. Over the past ten years, many variants have been developed and have been applied to a diverse range of applications. This chapter briefly outlines some of the recent variants and applications.
The integration we have discussed so far is for functions in the real domain. Integration can be extended to deal with the integrals of complex functions, and this chapter introduces some advanced techniques in calculus.
No abstract is provided for this article.
Plasma gas injection as one of the non-thermal plasma (NTP) technologies has a free-standing gas-phase plasma reactor outside the polluted target and can efficiently degrade contaminants by injecting plasma gas containing a large number of reactive species. This approach has been utilized in various fields of pollutants treatment, because of its structural advantage of indirect contact between pollutants and electrodes and its ability to produce strong oxidizing species. This article first expounds on the principle of the plasma gas injection and then progresses to a discussion of its application for pollutants degradation, mainly focusing on the treatment effect and degradation mechanisms. It is believed that plasma gas injection is considered to be a very practical, clean, efficient, and environmentally friendly method for pollution control.
This chapter introduces the finite element method from weak forms to shape functions. Differential operators are briefly reviewed, followed by the weak formulation with the application of both essential and natural boundary conditions. Various shape functions, such as linear, quadratic, and 2D shape functions, are given in detail. A worked example shows how the finite element method works. Finally, the formulation for time-dependent problems is also explained.
Clustering algorithms are an important component of data mining technology which has been applied widely in many applications including those that operate on Internet. Recently a new line of research namely Web Intelligence emerged that demands for advanced analytics and machine learning algorithms for supporting knowledge discovery mainly in the Web environment. The so called Web Intelligence data are known to be dynamic, loosely structured and consists of complex attributes. To deal with this challenge standard clustering algorithms are improved and evolved with optimization ability by swarm intelligence which is a branch of nature-inspired computing. Some examples are PSO Clustering (C-PSO) and Clustering with Ant Colony Optimization. The objective of this paper is to investigate the possibilities of applying other nature-inspired optimization algorithms (such as Fireflies, Cuckoos, Bats and Wolves) for performing clustering over Web Intelligence data. The efficacies of each new clustering algorithm are reported in this paper, and in general they outperformed C-PSO.
The firefly algorithm is a swarm intelligence-based algorithm, and its nonlinearity in search mechanisms can usually lead to subdivision and multiswarms, which means that it can be potentially more effective than single-swarm algorithms. This chapter introduces the main ideas of the firefly algorithm, followed by the introduction of the flower pollination algorithm. Both implementation details and examples will be presented to show how these algorithms work. Suggestions on modifications and multiobjective optimization will also be discussed.
Nature-inspired metaheuristic algorithms have become powerful and popular in computational intelligence and many applications. There are some important developments in recent years, and this special issue aims to provide a timely review of such developments, including ant colony optimization, bat algorithm, cuckoo search, particle swarm optimization, genetic algorithms, support vector machine, neural networks, and others. In addition, these algorithms have been applied in a diverse range of applications, and some of these latest applications are also summarized here. Computational intelligence and metaheuristic algorithms have become increasingly popular in computer science, artificial intelligence, machine learning, engineering design, data mining, image processing, and data-intensive applications. Most algorithms in computational intelligence and optimization are based on swarm intelligence (SI) [1, 2]. For example, both particle swarm optimization [1] and cuckoo search [3] have attracted much attention in science and engineering. They both can effectively deal with continuous problems [2] and combinatorial problems [4]. These algorithms are very different from the conventional evolutionary algorithms such as genetic algorithms and simulated annealing [5, 6] and other heuristics [7]. Many new optimization algorithms are based on the so-called swarm intelligence (SI) with diverse characteristics in mimicking natural systems [1, 2]. Consequently, different algorithms may have different features and thus may behave differently, even with different efficiencies. However, It still lacks in-depth understanding why these algorithms work well and exactly under what conditions, though there were some good studies that may provide insight into algorithms [2, 8]. This special issue focuses on the recent developments of SI-based metaheuristic algorithms and their diverse applications as well as theoretical studies. Therefore, this paper is organized as follows. Section 2 provides an introduction and comparison of the so-called infinite monkey theorem and metaheuristics, followed by the brief review of computational intelligence and metaheuristics in Section 3. Then, Section 4 touches briefly the state-of-the-art developments, and finally, Section 5 provides some open problems about some key issues concerning computational intelligence and metaheuristics.
Low latency is an important design goal for reliable data transmission protocols such as TCP and QUIC. However, timeout-based loss recovery can unnecessarily increase end-to-end latency. Previous work in reducing timeout-based loss recovery latency either duplicates every packet to avoid loss or focuses on fine-tuning the timeout timers to shorten the timeout latency without causing spurious packet retransmissions. In this work, we propose a new mechanism called Selective Loss Prevention (SLP) to reduce the loss recovery latency of a reliable transport protocol. Through extensive trace analysis, we find that not all lost packets are equal. The loss of packets with certain flags, such as SYN and PSH, is more likely to cause timeouts than other packets. Based on this observation, we propose to selectively duplicate an "important" packet whose loss is likely to increase a connection's latency. We design an algorithm to determine when to duplicate a lost packet proactively and incorporate it into TCP's congestion control algorithm so that duplicate packets will not congest the network. We incorporate SLP into Linux's kernel and evaluate its performance. Our results show that SLP can reduce timeout-based latency caused by the loss of important packets in a connection, and its overhead is low.
No abstract is provided for this article.
Medical image registration represents a pivotal element in the field of disease image analysis, acting as the essential precursor for a multitude of sophisticated analytical tasks. In recent years, traditional methodologies have encountered significant challenges in meeting the evolving demands of clinical practice. In contrast, deep learning-based strategies have emerged as powerful alternatives, showcasing their ability to facilitate more rapid and accurate registration processes, thereby exerting a profound impact on clinical applications. Within the specialized domain of medical imaging, the intricate level of expertise required for domain knowledge imposes rigorous standards on annotators, which in turn leads to increased annotation costs. As a result, the efficacy of supervised learning approaches compared to unsupervised learning methodologies can exhibit substantial variability in real-world applications. This paper systematically utilizes a diverse array of medical imaging datasets to rigorously assess the performance outcomes of both supervised and unsupervised learning techniques, specifically in relation to their practical applications in the medical imaging landscape.
No abstract is provided for this article.
No abstract is provided for this article.
This paper proposes an elegant optimization framework consisting of a mix of linear-matrix-inequality and second-order-cone constraints. The proposed framework generalizes the semidefinite relaxation (SDR) enabled solution to the typical transmit beamforming problems presented in the form of quadratically constrained quadratic programs (QCQPs) in the literature. It is proved that the optimization problems subsumed under the framework always admit a rank-one optimal solution when they are feasible and their optimal solutions are not trivial. This finding indicates that the relaxation is tight as the optimal solution of the original beamforming QCQP can be straightforwardly obtained from that of the SDR counterpart without any loss of optimality. Four representative examples of transmit beamforming, i.e., transmit beamforming with perfect channel state information (CSI), transmit beamforming with imperfect CSI, chance-constraint approach for imperfect CSI, and reconfigurable-intelligent-surface (RIS) aided beamforming, are shown to demonstrate how the proposed optimization framework can be realized in deriving the SDR counterparts for different beamforming designs.
