Deep Learning-Driven Particle Swarm Optimisation for Additive Manufacturing Energy Optimisation
بهینه سازی ازدحام ذرات با محوریت یادگیری عمیق برای بهینه سازی انرژی تولید افزودنی-2019
The additive manufacturing (AM) process is characterised as a high energy-consuming process, which has a significant impact on the environment and sustainability. The topic of AM energy consumption modelling, prediction, and optimisation has then become a research focus in both industry and academia. This issue involves many relevant features, such as material condition, process operation, part and process design, working environment, and so on. While existing studies reveal that AM energy consumption modelling largely depends on the design-relevant features in practice, it has not been given sufficient attention. Therefore, in this study, design-relevant features are firstly examined with respect to energy modelling. These features are typically determined by part designers and process operators before production. The AM energy consumption knowledge, hidden in the design-relevant features, is exploited for prediction modelling through a design-relevant data analytics approach. Based on the new modelling approach, a novel deep learning-driven particle swarm optimisation (DLD-PSO) method is proposed to optimise the energy utility. Deep learning is introduced to address several issues, in terms of increasing the search speed and enhancing the global best of PSO. Finally, using the design-relevant data collected from a real-world AM system in production, a case study is presented to validate the proposed modelling approach, and the results reveal its merits. Meanwhile, optimisation has also been carried out to guide part designers and process operators to revise their designs and decisions in order to reduce the energy consumption of the designated AM system under study.
Keywords: Additive Manufacturing | Energy Consumption Modelling | Prediction and Optimisation | Deep Learning | Particle Swarm Optimisation
Study of LED array fill light based on parallel particle swarm optimization in greenhouse planting
مطالعه نور پر کننده آرایه LED بر اساس بهینه سازی ازدحام ذرات موازی در کاشت گلخانه ای-2019
Agricultural productivity is crucial to the economy. The output and quality of crops have a direct impact on people’s daily lives and a country’s food and clothing. Therefore, harvesting high-quality crops efficiently and maximizing yield per unit area are the most important goals pursued by farmers. As an important parameter of plant growth, light intensity is one of the important factors that affects plant growth and development, morphological establishment and accumulation of functional chemical substances. When light intensity cannot meet the plant’s needs, the optimal light intensity or uneven light distribution will have a greater impact on plant growth and development. This paper aims to address the optimal plant light intensity problem. The paper presents an expert system technology database storing the empirical value of real-time light intensity values and compares it with a parallel particle swarm optimization algorithm to identify the optimal locations where LED lights need to be turned on and where drive circuit lit LED arrays need to be situated, to identify the number of LED fill lights and to solve light intensity optimization problems.
Keywords: Particle swarm optimization | Greenhouse | Fill light | Expert system | LED
Swarm intelligence techniques in recommender systems - A review of recent research
تکنیک های هوش ازدحام در سیستم های توصیه کننده - مروری بر تحقیقات اخیر-2019
One of the main current applications of Intelligent Systems are Recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS. One of them are Swarm Intelligence (SI) techniques, which are an emerging trend with various application areas. Although the interest in using Computational Intelligence in web personalization and information retrieval fostered the publication of some survey papers, these surveys so far focused on different application domains, e.g., clustering, or were too broadly focused and incorporated only a handful of SI approaches. This study provides a comprehensive review of 77 research publications applying SI in RS. The study focus on five aspects we consider relevant for such: the recommendation technique used, the datasets and the evaluation methods adopted in their experimental parts, the baselines employed in the experimental comparison of proposed approaches and the reproducibility of the reported results. At the end of this review, we discuss negative and positive aspects of these papers, as well as point out opportunities, challenges and possible future research directions. To the best of our knowledge, this survey is the most comprehensive review of approaches using SI in RS. Therefore, we believe this review will be a relevant material for researchers interested in either of the domains.
Keywords: Swarm intelligence | Particle swarm optimization | Ant colony optimization | Invasive weed optimization | Artificial bee colony | Recommender systems | Personalization
Clustering of multi-view relational data based on particle swarm optimization
خوشه بندی داده های رابطه ای چند منظوره بر اساس بهینه سازی ازدحام ذرات-2019
Clustering of multi-view data has received increasing attention since it explores multiple views of data sets aiming at improving clustering accuracy. Particle Swarm Optimization (PSO) is a well-known population-based meta-heuristic successfully used in cluster analysis. This paper introduces two hybrid clustering methods for multi-view relational data. These hybrid methods combine PSO and hard clus- tering algorithms based on multiple dissimilarity matrices. These methods take advantage of the global convergence ability of PSO and the local exploitation of hard clustering algorithms in the position up- date step, aiming to improve the balance between exploitation and exploration processes. Moreover, the paper provides adapted versions of 11 fitness functions suitable for vector data aiming at dealing with multi-view relational data. Two performance criteria were used to evaluate the clustering quality using the two proposed methods over eleven real-world data sets including image and document data sets. Among new findings, it was observed that the top three fitness functions are Silhouette index, Xu index and Intra-cluster homogeneity. The performance of the proposed algorithms was compared with previ- ous single and multi-view relational clustering algorithms. The results show that the proposed methods significantly outperformed the other algorithms in the majority of cases. The results reinforce the im- portance of the application of techniques such as PSO-based clustering algorithms in the field of expert systems and machine learning. Such application enhances classification accuracy and cluster compactness. Besides, the proposed algorithms can be useful tools in content-based image retrieval systems, providing good categorizations and automatically learning relevance weights for each cluster of images and sets of views.
Keywords: PSO | Cluster analysis | Multi-view clustering | Relational data
Hierarchical differential evolution algorithm combined with multi-cross operation
الگوریتم تکامل تفاضلی سلسله مراتبی همراه با عمل چند تقابلی-2019
In expert systems, complex optimization problems are always characterized by nonlinearity, nonconvex- ity, multi-modality, discontinuity, and high dimensionality. Although classical optimization algorithms are mature, they readily fall into a local optimum. The differential evolution (DE) algorithm has been suc- cessfully applied to solve numerous problems with expert systems. However, balancing the global and local search capabilities of the DE algorithm remains an open issue and has attracted significant research attention. Thus, a hierarchical heterogeneous DE algorithm that incorporates multi-cross operation (MCO) is proposed in this article. In the proposed algorithm, success-history-based adaptive DE (SHADE) is im- plemented in the bottom layer, while MCO is implemented in the top layer. The MCO search is based on the SHADE results, but its search results do not affect the bottom layer. First-order stability analyses con- ducted for the presented MCO showed that the individual positions are expected to converge at a fixed point in the search space. The accuracy and convergence speed of the proposed algorithm were also ex- perimentally compared with those of eight other advanced particle swarm optimization techniques and DE variants using benchmark functions from CEC2017. The proposed algorithm yielded better solution ac- curacy for 30- and 50-dimensional problems than the other variants, and although it did not provide the fastest convergence for all of the functions, it ranked among the top three for the unimodal and simple multimodal functions and achieved fast convergence for the other functions.
Keywords: Differential evolution | Particle swarm optimization | Hierarchical structure | Multi-cross operation
Dependence structure of Gabor wavelets based on copula for face recognition
ساختار وابستگی Gabor wavelets بر اساس کوپول برای تشخیص چهره-2019
Low resolution, difficult illumination and noise are the important factors that affect the performance of face recognition system. In order to counteract these adverse factors, in this paper we propose copula probability models based on Gabor wavelets for face recognition. Gabor wavelets have robust performance under lighting and noise conditions. The strong dependencies exist in the domain of Gabor wavelets due to their non-orthogonal property. In the light of the structure characteristic of Gabor wavelet sub- bands, the proposed methods use copula to capture the dependencies to represent the face image. Three probability-model-based methods CF-GW (Copula Function of Gabor Wavelets), LCM-GW (Lightweight Copula Model of Gabor Wavelets) and LCM-GW-PSO (Lightweight Copula Model of Gabor Wavelets with Particle Swarm Optimization) are proposed for face recognition. Experiments of face recognition show our proposed methods are more robust under the conditions of low resolution, lighting and noise than the popular methods such as the LBP-based methods and other Gabor-based methods. The face features extracted by our methods belong to the Riemannian manifold which is different to Euclidean space. In order to deal the issue of face recognition in complex environment, we can combine the face features in Riemannian manifold with the face features in Euclidean space to obtain the more robust face recognition system by using expert system technologies such as reasoning model and multi-classifier fusion.
Keywords: Face recognition | Gabor wavelets | Gaussian copula | Covariance matrix | Particle swarm optimization
الگوریتم بهینه سازی ازدحام ذرات با کنترل هوشمند تعداد ذرات برای طراحی بهینه ماشین های الکتریکی
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 23
در این مقاله، یک الگوریتم بهینه سازی ازدحام ذرات (PSO) اصلاح شده پیشنهاد می شود که نسخه ارتقاء یافته الگوریتم PSO معمولی است. برای بهبود دادن عملکرد الگوریتم PSO ، یک روش جدید برای کنترل کردن هوشمندانه تعداد ذرات به کار برده شده است. این روش جدید، مقدار هزینه بهترین جهانی (gbest) در تکرار فعلی نسبت به gbest در تکرار قبلی را با یکدیگر مقایسه می کند. اگر بین دو مقدار هزینه اختلافی وجود داشته باشد، آنگاه الگوریتم پیشنهادی در مرحله اکتشاف عمل می کند و تعداد ذرات را حفظ می کند. اما، وقتی که اختلاف در مقادیر هزینه نسبت به مقادیر تحمل تخصیص یافته توسط کاربر کوچکتر باشد، این الگوریتم پیشنهادی در مرحله استخراج عمل می کند و تعداد ذرات را کاهش می دهد. علاوه بر این، این الگوریتم ، نزدیکترین ذره به بهترین ذره را حذف می کند تا از تصادفی بودنش بر حسب فاصله ی اقلیدسی اطمینان حاصل کند. الگوریتم پیشنهادی با استفاده از پنج تابع آزمون عددی اعتبارسنجی می شود، که تعداد فراخوانی های تابع تا اندازه ای نسبت به PSO معمولی کاهش می یابد. بعد از اعتبار سنجی الگوریتم ، برای طراحی بهینه موتور سنکرون مغناطیس دائم درونی (IPMSM) به کار برده می شود تا اعوجاج هارمونیک کل (THD) نیروی ضد محرکه الکتریکی (back-EMF) کاهش یابد. با در نظر گرفتن شرط عملکرد، طراحی بهینه به دست می آید که back-EMF THD را کاهش داده و مقدار back-EMF را برآورده می کند. نهایتا، یک مدل آزمایشگاهی را ایجاد کرده و آزمایش می کنیم. برای اعتبارسنجی عملکرد طراحی بهینه و الگوریتم بهینه سازی ، یک آزمایش بدون بار انجام می شود. بر اساس نتایج آزمایشگاهی، اثربخشی الگوریتم پیشنهادی بر روی طراحی بهینه یک ماشین الکتریکی تایید می شود.
کلمات کلیدی: طراحی بهینه | الگوریتم بهینه سازی | بهینه سازی ذرات ذرات | ماشین الکتریکی | موتور همگام مگنت دائمی.
|مقاله ترجمه شده|
Link based BPSO for feature selection in big data text clustering
BPSO مبتنی بر لینک برای انتخاب ویژگی در خوشه بندی متون داده های بزرگ-2018
Feature selection is a significant task in data mining and machine learning applications which eliminates irrelevant and redundant features and improves learning performance. This paper proposes a new feature selection method for unsupervised text clustering named link based particle swarm optimization (LBPSO). This method introduces a new neighbour selection strategy in BPSO to select prominent features. The performance of traditional particle swarm optimization(PSO)is limited by using global best updating mechanism for feature selection. Instead of using global best, LBPSO particles are updated based on neighbour best position to enhance the exploitation and exploration capability. These prominent features are then tested using k-means clustering algorithm to improve the performance and reduce the cost of computational time of the proposed algorithm. The performance of LBPSO are investigated on three published datasets, namely Reuter 21578, TDT2 and Tr11. Our results based on evaluation measures show that the performance of LBPSO is superior than other PSO based algorithms.
Keywords: Big data ، Text clustering ، Particle swarm optimization ، Scale free network ، k-means ، Feature selection
A hybrid model of Internet of Things and cloud computing to manage big data in health services applications
یک مدل ترکیبی از اینترنت اشیا و محاسبات ابری برای مدیریت داده های بزرگ در برنامه های خدمات بهداشتی-2018
Over the last decade, there has been an increasing interest in big data research, especially for health services applications. The adoption of the cloud computing and the Internet of Things (IoT) paradigm in the healthcare field can bring several opportunities to medical IT, and experts believe that it can significantly improve healthcare services and contribute to its continuous and systematic innovation in a big data environment such as Industry 4.0 applications. However, the required resources to manage such data in a cloud-IoT environment are still a big challenge. Accordingly, this paper proposes a new model to optimize virtual machines selection (VMs) in cloud-IoT health services applications to efficiently manage a big amount of data in integrated industry 4.0. Industry 4.0 applications require to process and analyze big data, which come from different sources such as sensor data, without human intervention. The proposed model aims to enhance the performance of the healthcare systems by reducing the stakeholders’ request execution time, optimizing the required storage of patients’ big data and providing a real-time data retrieval mechanism for those applications. The architecture of the proposed hybrid cloud-IoT consists of four main components: stakeholders’ devices, stakeholders’ requests (tasks), cloud broker and network administrator. To optimize the VMs selection, three different well-known optimizers (Genetic Algorithm (GA), Particle swarm optimizer (PSO) and Parallel Particle swarm optimization (PPSO) are used to build the proposed model. To calculate the execution time of stakeholders’ requests, the proposed fitness function is a composition of three important criteria which are CPU utilization, turn-around time and waiting time. A set of experiments were conducted to provide a comparative study between those three optimizers regarding the execution time, the data processing speed, and the system efficiency. The proposed model is tested against the state-of-the-art method to evaluate its effectiveness. The results show that the proposed model outperforms on the state-of-the-art models in total execution time the rate of 50%. Also, the system efficiency regarding real-time data retrieve is significantly improved by 5.2%.
Keywords: Big data ، Industry 4.0 ، Cloud computing ، Internet of Things ، Health services ، Genetic Algorithm ، Particle swarm optimization
A robust hybrid approach based on particle swarm optimization and genetic algorithm to minimize the total machine load on unrelated parallel machines
یک روش ترکیبی قوی بر اساس بهینه سازی ازدحام ذرات و الگوریتم ژنتیک برای به حداقل رساندن بار دستگاه ها بر روی ماشین های موازی غیر مرتبط-2016
This paper dealt with an unrelated parallel machines scheduling problem with past-sequence-dependentsetup times, release dates, deteriorating jobs and learning effects, in which the actual processing timeof a job on each machine is given as a function of its starting time, release time and position on thecorresponding machine. In addition, the setup time of a job on each machine is proportional to theactual processing times of the already processed jobs on the corresponding machine, i.e., the setup timesare past-sequence-dependent (p-s-d). The objective is to determine jointly the jobs assigned to eachmachine and the order of jobs such that the total machine load is minimized. Since the problem is NP-hard, optimal solution for the instances of realistic size cannot be obtained within a reasonable amountof computational time using exact solution approaches. Hence, an efficient method based on the hybridparticle swarm optimization (PSO) and genetic algorithm (GA), denoted by HPSOGA, is proposed to solvethe given problem. In view of the fact that efficiency of the meta-heuristic algorithms is significantlydepends on the appropriate design of parameters, the Taguchi method is employed to calibrate and selectthe optimal levels of parameters. The performance of the proposed method is appraised by comparingits results with GA and PSO with and without local search through computational experiments. Thecomputational results for small sized problems show that the mentioned algorithms are fully effectiveand viable to generate optimal/near optimal solutions, but when the size of the problem is increased, theHPSOGA obtains better results in comparison with other algorithms.
Keywords:Unrelated parallel machines scheduling | Deteriorating jobs | Learning effect | Release dates | P-S-D setup times | Meta-heuristic algorithms