Cheapest Insertion Constructive Heuristic based on Two Combination Seed Customer Criterion for the Capacitated Vehicle Routing Problem

The heuristic method is a well-known constructive method for initialize trail quality solutions in capacitated vehicle routing problem. Cheapest insertion heuristic is a popular construction heuristic known for being fast, producing decent solutions, simple to implement and easy to extend handling complicated constraints. However, in previous work, there was less focus on diverse initial quality solutions. Therefore, this study proposed an extension to the cheapest insertion heuristic which consider various combinations of seed customer criteria (the first customer inserted on a route) to preserve solutions diversification. Three seed customer criteria proposed which based on the combination of two criteria based on (farthest, nearest and random criteria). The best performing criteria selected and tested on benchmark dataset, later compared with Clarke and Wright saving heuristic. The results shown that the combination of (farthest and random) criteria obtained the best initial solution which preserve balance between the quality and diversity, with less time when compared to Clarke and wright saving heuristic. This approach is for generating diverse and quality starting solutions for the capacitated vehicle routing problem

Grammatical evolution hyper-heuristic for combinatorial optimization problems

Designing generic problem solvers that perform well across a diverse set of problems is a challenging task. In this work, we propose a hyper-heuristic framework to automatically generate an effective and generic solution method by utilizing grammatical evolution. In the proposed framework, grammatical evolution is used as an online solver builder, which takes several heuristic components (e.g., different acceptance criteria and different neighborhood structures) as inputs and evolves templates of perturbation heuristics. The evolved templates are improvement heuristics, which represent a complete search method to solve the problem at hand. To test the generality and the performance of the proposed method, we consider two well-known combinatorial optimization problems: exam timetabling (Carter and ITC 2007 instances) and the capacitated vehicle routing problem (Christofides and Golden instances). We demonstrate that the proposed method is competitive, if not superior, when compared to state-of-the-art hyper-heuristics, as well as bespoke methods for these different problem domains. In order to further improve the performance of the proposed framework we utilize an adaptive memory mechanism, which contains a collection of both high quality and diverse solutions and is updated during the problem solving process. Experimental results show that the grammatical evolution hyper-heuristic, with an adaptive memory, performs better than the grammatical evolution hyper-heuristic without a memory. The improved framework also outperforms some bespoke methodologies, which have reported best known results for some instances in both problem domains

The automatic design of hyper-heuristic framework with gene expression programming for combinatorial optimization problems

Hyper-heuristic approaches aim to automate heuristic design in order to solve multiple problems instead of designing tailor-made methodologies for individual problems. Hyper-heuristics accomplish this through a high level heuristic (heuristic selection mechanism and an acceptance criterion). This automates heuristic selection, deciding whether to accept or reject the returned solution. The fact that different problems or even instances, have different landscape structures and complexity, the design of efficient high level heuristics can have a dramatic impact on hyper-heuristic performance. In this work, instead of using human knowledge to design the high level heuristic, we propose a gene expression programming algorithm to automatically generate, during the instance solving process, the high level heuristic of the hyper-heuristic framework. The generated heuristic takes information (such as the quality of the generated solution and the improvement made) from the current problem state as input and decides which low level heuristic should be selected and the acceptance or rejection of the resultant solution. The benefit of this framework is the ability to generate, for each instance, different high level heuristics during the problem solving process. Furthermore, in order to maintain solution diversity, we utilize a memory mechanism which contains a population of both high quality and diverse solutions that is updated during the problem solving process. The generality of the proposed hyper-heuristic is validated against six well known combinatorial optimization problem, with very different landscapes, provided by the HyFlex software. Empirical results comparing the proposed hyper-heuristic with state of the art hyper-heuristics, conclude that the proposed hyper-heuristic generalizes well across all domains and achieves competitive, if not superior, results for several instances on all domains

A Recent Trend in Individual Counting Approach Using Deep Network

In video surveillance scheme, counting individuals is regarded as a crucial task. Of all the individual counting techniques in existence, the regression technique can offer enhanced performance under overcrowded area. However, this technique is unable to specify the details of counting individual such that it fails in locating the individual. On contrary, the density map approach is very effective to overcome the counting problems in various situations such as heavy overlapping and low resolution. Nevertheless, this approach may break down in cases when only the heads of individuals appear in video scenes, and it is also restricted to the feature’s types. The popular technique to obtain the pertinent information automatically is Convolutional Neural Network (CNN). However, the CNN based counting scheme is unable to sufficiently tackle three difficulties, namely, distributions of non-uniform density, changes of scale and variation of drastic scale. In this study, we cater a review on current counting techniques which are in correlation with deep net in different applications of crowded scene. The goal of this work is to specify the effectiveness of CNN applied on popular individuals counting approaches for attaining higher precision results

Nonlinear regression in tax evasion with uncertainty: a variational approach

One of the major problems in today's economy is the phenomenon of tax evasion. The linear regression method is a solution to find a formula to investigate the effect of each variable in the final tax evasion rate. Since the tax evasion data in this study has a great degree of uncertainty and the relationship between variables is nonlinear, Bayesian method is used to address the uncertainty along with 6 nonlinear basis functions to tackle the nonlinearity problem. Furthermore, variational method is applied on Bayesian linear regression in tax evasion data to approximate the model evidence in Bayesian method. The dataset is collected from tax evasion in Malaysia in period from 1963 to 2013 with 8 input variables. Results from variational method are compared with Maximum Likelihood Estimation technique on Bayeisan linear regression and variational method provides more accurate prediction. This study suggests that, in order to reduce the tax evasion, Malaysian government should decrease direct tax and taxpayer income and increase indirect tax and government regulation variables by 5% in the small amount of changes (10%-30%) and reduce direct tax and income on taxpayer and increment indirect tax and government regulation variables by 90% in the large amount of changes (70%-90%) with respect to the current situation to reduce the final tax evasion rate

A dynamic multiarmed bandit-gene expression programming hyper-heuristic for combinatorial optimization problems

Hyper-heuristics are search methodologies that aim to provide high-quality solutions across a wide variety of problem domains, rather than developing tailor-made methodologies for each problem instance/domain. A traditional hyper-heuristic framework has two levels, namely, the high level strategy (heuristic selection mechanism and the acceptance criterion) and low level heuristics (a set of problem specific heuristics). Due to the different landscape structures of different problem instances, the high level strategy plays an important role in the design of a hyper-heuristic framework. In this paper, we propose a new high level strategy for a hyper-heuristic framework. The proposed high-level strategy utilizes a dynamic multiarmed bandit-extreme value-based reward as an online heuristic selection mechanism to select the appropriate heuristic to be applied at each iteration. In addition, we propose a gene expression programming framework to automatically generate the acceptance criterion for each problem instance, instead of using human-designed criteria. Two well-known, and very different, combinatorial optimization problems, one static (exam timetabling) and one dynamic (dynamic vehicle routing) are used to demonstrate the generality of the proposed framework. Compared with state-of-the-art hyper-heuristics and other bespoke methods, empirical results demonstrate that the proposed framework is able to generalize well across both domains. We obtain competitive, if not better results, when compared to the best known results obtained from other methods that have been presented in the scientific literature. We also compare our approach against the recently released hyper-heuristic competition test suite. We again demonstrate the generality of our approach when we compare against other methods that have utilized the same six benchmark datasets from this test suite

Rekabentuk sistem pemprosesan tesis secara automatik

Perpustakaan yang merupakan gedung ilmu yang penting perlu disediakan dengan kemudahan selaras dengan perkembangan ICT (Information Communication Technology). Ini adalah bertujuan untuk menggalakkan dan memudahkan orang ramai menggunakan perpustakaan. Gabungan teknologi digital dan rangkaian komunikasi ini membolehkan pengguna mengetahui koleksi sesebuah perpustakaan atau pusat sumber melalui Katalog Awam dalam talian atau OPAC (Online Public Access Catalogue) di mana sahaja mereka berada dan pada bila-bila masa. Kewujudan Katalog Awam Dalam Talian (OPAC)yang telah wujud semenjak awal tahun 80-an bukan sahaja berfungsi untuk memaklumkan kepada pengguna tentang bahan koleksi yang terdapat dalam perpustakaan malah turut menunjukkan status bahan yang dipesan tetapi belum di terima serta menyatakan bahan yang sudah dipinjam dan tarikh pemulangan. Pengguna juga boleh membuat tempahan dan membuat capaian koleksi perpustakaan lain (Ding 1998). Sistem pengurusan sumber maklumat yang lebih cekap dan baik membantu pengguna membuat capaian maklumat serta memudahkan pihak pengurusan menguruskan pusat sumber. Kemajuan teknologi ICT yang dibincangkan di atas turut di rasai oleh insitusi pengajian tinggi termasuk di pusat sumber, Fakulti Teknologi dan Sains Maklumat, Universiti Kebangsaan Malaysia

An IoT-based Prediction Technique for Efficient Energy Consumption in Buildings

Today, there is a crucial need for precise monitoring and prediction of energy consumption at the building level using the latest technologies including Internet of Things (IoT) and data analytics to determine and enhance energy usage. Data-driven models could be used for energy consumption prediction. However, due to high non-linearity between the inputs and outputs of energy consumption prediction models, these models need improvement in terms of accuracy and robustness. Therefore, this work aims to predict energy usage for the optimum outline of building-extensive energy distribution strategies based on a lightweight IoT monitoring framework. To calculate accurate energy consumption, an enhanced hybrid model was developed based on Auto-Regressive Integrated Moving Average (ARIMA) and Imperialist Competitive Algorithm (ICA). The parameters of the ARIMA model were optimized by adapting the ICA technique that improved fitting accuracy while preventing over-fitting on the acquired data. Then, Exponentially Weighted Moving Average (EWMA) was applied to monitor the predicted values. The proposed AIK-EWMA hybrid model was assessed based on the actual power consumption data and validated using mathematical tests. As compared to previous works, the findings revealed that the hybrid model could accurately predict power consumption for green building automation applications

Breast cancer diagnosis using the fast learning network algorithm

The use of machine learning (ML) and data mining algorithms in the diagnosis of breast cancer (BC) has recently received a lot of attention. The majority of these efforts, however, still require improvement since either they were not statistically evaluated or they were evaluated using insufficient assessment metrics, or both. One of the most recent and effective ML algorithms, fast learning network (FLN), may be seen as a reputable and efficient approach for classifying data; however, it has not been applied to the problem of BC diagnosis. Therefore, this study proposes the FLN algorithm in order to improve the accuracy of the BC diagnosis. The FLN algorithm has the capability to a) eliminate overfitting, b) solve the issues of both binary and multiclass classification, and c) perform like a kernel-based support vector machine with a structure of the neural network. In this study, two BC databases (Wisconsin Breast Cancer Database (WBCD) and Wisconsin Diagnostic Breast Cancer (WDBC)) were used to assess the performance of the FLN algorithm. The results of the experiment demonstrated the great performance of the suggested FLN method, which achieved an average of accuracy 98.37%, precision 95.94%, recall 99.40%, F-measure 97.64%, G-mean 97.65%, MCC 96.44%, and specificity 97.85% using the WBCD, as well as achieved an average of accuracy 96.88%, precision 94.84%, recall 96.81%, F-measure 95.80%, G-mean 95.81%, MCC 93.35%, and specificity 96.96% using the WDBC database. This suggests that the FLN algorithm is a reliable classifier for diagnosing BC and may be useful for resolving other application-related problems in the healthcare sector