A Brief Survey on Intelligent Swarm-Based Algorithms for Solving Optimization Problems

This chapter presents an overview of optimization techniques followed by a brief survey on several swarm-based natural inspired algorithms which were introduced in the last decade. These techniques were inspired by the natural processes of plants, foraging behaviors of insects and social behaviors of animals. These swam intelligent methods have been tested on various standard benchmark problems and are capable in solving a wide range of optimization issues including stochastic, robust and dynamic problems

Feature Selection for Document Classification : Case Study of Meta-heuristic Intelligence and Traditional Approaches

Doctor of Philosophy (Computer Engineering), 2020Nowadays, the culture for accessing news around the world is changed from paper to electronic format and the rate of publication for newspapers and magazines on website are increased dramatically. Meanwhile, text feature selection for the automatic document classification (ADC) is becoming a big challenge because of the unstructured nature of text feature, which is called “multi-dimension feature problem”. On the other hand, various powerful schemes dealing with text feature selection are being developed continuously nowadays, but there still exists a research gap for “optimization of feature selection problem (OFSP)”, which can be looked for the global optimal features. Meanwhile, the capacity of meta-heuristic intelligence for knowledge discovery process (KDP) is also become the critical role to overcome NP-hard problem of OFSP by providing effective performance and efficient computation time. Therefore, the idea of meta-heuristic based approach for optimization of feature selection is proposed in this research to search the global optimal features for ADC. In this thesis, case study of meta-heuristic intelligence and traditional approaches for feature selection optimization process in document classification is observed. It includes eleven meta-heuristic algorithms such as Ant Colony search, Artificial Bee Colony search, Bat search, Cuckoo search, Evolutionary search, Elephant search, Firefly search, Flower search, Genetic search, Rhinoceros search, and Wolf search, for searching the optimal feature subset for document classification. Then, the results of proposed model are compared with three traditional search algorithms like Best First search (BFS), Greedy Stepwise (GS), and Ranker search (RS). In addition, the framework of data mining is applied. It involves data preprocessing, feature engineering, building learning model and evaluating the performance of proposed meta-heuristic intelligence-based feature selection using various performance and computation complexity evaluation schemes. In data processing, tokenization, stop-words handling, stemming and lemmatizing, and normalization are applied. In feature engineering process, n-gram TF-IDF feature extraction is used for implementing feature vector and both filter and wrapper approach are applied for observing different cases. In addition, three different classifiers like J48, Naïve Bayes, and Support Vector Machine, are used for building the document classification model. According to the results, the proposed system can reduce the number of selected features dramatically that can deteriorate learning model performance. In addition, the selected global subset features can yield better performance than traditional search according to single objective function of proposed model

DEEP LEARNING FOR IMAGE RESTORATION AND ROBOTIC VISION

Traditional model-based approach requires the formulation of mathematical model, and the model often has limited performance. The quality of an image may degrade due to a variety of reasons: It could be the context of scene is affected by weather conditions such as haze, rain, and snow; It\u27s also possible that there is some noise generated during image processing/transmission (e.g., artifacts generated during compression.). The goal of image restoration is to restore the image back to desirable quality both subjectively and objectively. Agricultural robotics is gaining interest these days since most agricultural works are lengthy and repetitive. Computer vision is crucial to robots especially the autonomous ones. However, it is challenging to have a precise mathematical model to describe the aforementioned problems. Compared with traditional approach, learning-based approach has an edge since it does not require any model to describe the problem. Moreover, learning-based approach now has the best-in-class performance on most of the vision problems such as image dehazing, super-resolution, and image recognition. In this dissertation, we address the problem of image restoration and robotic vision with deep learning. These two problems are highly related with each other from a unique network architecture perspective: It is essential to select appropriate networks when dealing with different problems. Specifically, we solve the problems of single image dehazing, High Efficiency Video Coding (HEVC) loop filtering and super-resolution, and computer vision for an autonomous robot. Our technical contributions are threefold: First, we propose to reformulate haze as a signal-dependent noise which allows us to uncover it by learning a structural residual. Based on our novel reformulation, we solve dehazing with recursive deep residual network and generative adversarial network which emphasizes on objective and perceptual quality, respectively. Second, we replace traditional filters in HEVC with a Convolutional Neural Network (CNN) filter. We show that our CNN filter could achieve 7% BD-rate saving when compared with traditional filters such as bilateral and deblocking filter. We also propose to incorporate a multi-scale CNN super-resolution module into HEVC. Such post-processing module could improve visual quality under extremely low bandwidth. Third, a transfer learning technique is implemented to support vision and autonomous decision making of a precision pollination robot. Good experimental results are reported with real-world data

A hybrid kidney algorithm strategy for combinatorial interaction testing problem

Combinatorial Interaction Testing (CIT) generates a sampled test case set (Final Test Suite (FTS)) instead of all possible test cases. Generating the FTS with the optimum size is a computational optimization problem (COP) as well as a Non-deterministic Polynomial hard (NP-hard) problem. Recent studies have implemented hybrid metaheuristic algorithms as the basis for CIT strategy. However, the existing hybrid metaheuristic-based CIT strategies generate a competitive FTS size, there is no single CIT strategy can overcome others existing in all cases. In addition, the hybrid metaheuristic-based CIT strategies require more execution time than their own original algorithm-based strategies. Kidney Algorithm (KA) is a recent metaheuristic algorithm and has high efficiency and performance in solving different optimization problems against most of the state-of-the-art of metaheuristic algorithms. However, KA has limitations in the exploitation and exploration processes as well as the balancing control process is needed to be improved. These shortages cause KA to fail easily into the local optimum. This study proposes a low-level hybridization of KA with the mutation operator and improve the filtration process in KA to form a recently Hybrid Kidney Algorithm (HKA). HKA addresses the limitations in KA by improving the algorithm's exploration and exploitation processes by hybridizing KA with mutation operator, and improve the balancing control process by enhancing the filtration process in KA. HKA improves the efficiency in terms of generating an optimum FTS size and enhances the performance in terms of the execution time. HKA has been adopted into the CIT strategy as HKA based CIT Strategy (HKAS) to generate the most optimum FTS size. The results of HKAS shows that HKAS can generate the optimum FTS size in more than 67% of the benchmarking experiments as well as contributes by 34 new optimum size of FTS. HKAS also has better efficiency and performance than KAS. HKAS is the first hybrid metaheuristic-based CIT strategy that generates an optimum FTS size with less execution time than the original algorithm-based CIT strategy. Apart from supporting different CIT features: uniform/VS CIT, IOR CIT as well as the interaction strength up to 6, this study also introduces another recently variant of KA which are Improved KA (IKA) and Mutation KA (MKA) as well as new CIT strategies which are IKA-based (IKAS) and MKA-based (MKAS)

An efficient scanning algorithm for photovoltaic systems under partial shading

This paper proposes a new technique of maximum power point tracking (MPPT) for a photovoltaic (PV) system connected to three phase grids under partial shading condition (PSC), based on a new combined perturb and observe (P&O) with scanning algorithm. This new algorithm main advantages are the high-speed tracking compared to existing algorithms, high accuracy and simplicity which makes it ideal for hardware implementation. Simulation was carried on MATLAB/Simulink. Results showed the effectiveness in speed and accuracy of our algorithm over the existing ones either during standard condition (STC) or PSC. Furthermore, conventional direct power control (DPC) was applied to synchronize successfully the injected power with the grid, which makes our algorithm global and works efficiently under severe conditions

In vitro embryo rescue and plant regeneration following self-pollination with irradiated pollen in cassava (Manihot esculenta Crantz)

Cassava is a highly heterozygous species; hence, current methods used in classical cassava breedingcannot match the urgent need to high yielding varieties. Recently, progress was made through androgenesis and gynogenesis as pathways for raising doubled cassava haploid lines to overcome problems associated with cassava’s inherent reproductive biology, but these efforts were limited (nocandidate cassava plantlets were regenerated). For the first time, this study shows that pollen irradiation coupled with self-pollination and embryo rescue regenerated 62 candidate cassava plantlets. Plants of an elite cassava variety, Nase14, served as a mother plant and as the pollen donor for the irradiation. Irradiation dosages of 50 to 250 Gray studied across five pollination events and 300 or 500 Gray in one pollination event caused a reduction in pollen germination up to 67.0%. By 15 days after pollination (DAP) with irradiated pollen, up to 89.7% of the pollinated flowers had aborted. By embryo rescue time (42 DAP), significant differences were observed in number of fruits, seeds and embryos generated, with the non-irradiated pollen treatments having significantly higher numbers. Sixteen (16) heterozygous SSR markers in the parent and ploidy analysis showed that none of the regenerated plants was haploid or homozygous. However, the plantlets resulting from pollination with non-irradiated pollen had 56.2% homozygous loci, while progeny derived from irradiated treatments had frequencies of homozygous loci between 28.1 and 55.0%. This is the first time to use irradiated pollen in cassava as a pathway to generate candidate plantlets as an initial step in double haploid production.Key words: Cassava, doubled haploids, embryo rescue, plant regeneration, pollen germination, pollenirradiation

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization

Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

INCUBATION OF METAHEURISTIC SEARCH ALGORITHMS INTO NOVEL APPLICATION FIELDS

Several optimization algorithms have been developed to handle various optimization issues in many fields, capturing the attention of many researchers. Algorithm optimizations are commonly inspired by nature or involve the modification of existing algorithms. So far, the new algorithms are set up and focusing on achieving the desired optimization goal. While this can be useful and efficient in the short term, in the long run, this is not enough as it needs to repeat for any new problem that occurs and maybe in specific difficulties, therefore one algorithm cannot be used for all real-world problems. This dissertation provides three approaches for implementing metaheuristic search (MHS) algorithms in fields that do not directly solve optimization issues. The first approach is to study parametric studies on MHS algorithms that attempt to understand how parameters work in MHS algorithms. In this first direction, we choose the Jaya algorithm, a relatively recent MHS algorithm defined as a method that does not require algorithm-specific control parameters. In this work, we incorporate weights as an extra parameter to test if Jaya’s approach is actually "parameter-free." This algorithm’s performance is evaluated by implementing 12 unconstrained benchmark functions. The results will demonstrate the direct impact of parameter adjustments on algorithm performance. The second approach is to embed the MHS algorithm on the Blockchain Proof of Work (PoW) to deal with the issue of excessive energy consumption, particularly in using bitcoin. This study uses an iterative optimization algorithm to solve the Traveling Salesperson Problem (TSP) as a model problem, which has the same concept as PoW and requires extending the Blockchain with additional blocks. The basic idea behind this research is to increase the tour cost for the best tour found for n blocks, extended by adding one more city as a requirement to include a new block in the Blockchain. The results reveal that the proposed concept can improve the way the current system solves complicated cryptographic problems Furthermore, MHS are implemented in the third direction approach to solving agricultural problems, especially the cocoa flowers pollination. We chose the problem in pollination in cacao flowers since they are distinctive and different from other flowers due to their small size and lack of odor, allowing just a few pollinators to successfully pollinate them, most notably a tiny midge called Forcipomyia Inornatipennis (FP). This concept was then adapted and implemented into an Idle-Metaheuristic for simulating the pollination of cocoa flowers. We analyze how MHS algorithms derived from three well-known methods perform when used to flower pollination problems. Swarm Intelligence Algorithms, Individual Random Search, and Multi-Agent Systems Search are the three methodologies studied here. The results shows that the Multi-Agent System search performs better than other methods. The findings of the three approaches reveal that adopting an MHS algorithms can solve the problem in this study by indirectly solving the optimization problem using the same problem model concept. Furthermore, the researchers concluded that parameter settings in the MHS algorithms are not so difficult to use, and each parameter can be adjusted to solve the real-world issue. This study is expected to encourage other researchers to improve and develop the performance of MHS algorithms used to deal with multiple real-world problems.九州工業大学博士学位論文 学位記番号: 情工博甲第367号 学位授与年月日: 令和4年3月25日1 Introduction|2 Traditional Metaheuristic Search Optimization|3 Parametric Study of Metaheuristic Search Algorithms|4 Embedded Metaheuristic Search Algorithms for Blockchain Proof-of-Work|5 Idle-Metaheuristic for Flower Pollination Simulation|6 Conclusion and Future Works九州工業大学令和3年

Intelligent instance selection techniques for support vector machine speed optimization with application to e-fraud detection.

Doctor of Philosophy in Computer Science. University of KwaZulu-Natal, Durban 2017.Decision-making is a very important aspect of many businesses. There are grievous penalties involved in wrong decisions, including financial loss, damage of company reputation and reduction in company productivity. Hence, it is of dire importance that managers make the right decisions. Machine Learning (ML) simplifies the process of decision making: it helps to discover useful patterns from historical data, which can be used for meaningful decision-making. The ability to make strategic and meaningful decisions is dependent on the reliability of data. Currently, many organizations are overwhelmed with vast amounts of data, and unfortunately, ML algorithms cannot effectively handle large datasets. This thesis therefore proposes seven filter-based and five wrapper-based intelligent instance selection techniques for optimizing the speed and predictive accuracy of ML algorithms, with a particular focus on Support Vector Machine (SVM). Also, this thesis proposes a novel fitness function for instance selection. The primary difference between the filter-based and wrapper-based technique is in their method of selection. The filter-based techniques utilizes the proposed fitness function for selection, while the wrapper-based technique utilizes SVM algorithm for selection. The proposed techniques are obtained by fusing SVM algorithm with the following Nature Inspired algorithms: flower pollination algorithm, social spider algorithm, firefly algorithm, cuckoo search algorithm and bat algorithm. Also, two of the filter-based techniques are boundary detection algorithms, inspired by edge detection in image processing and edge selection in ant colony optimization. Two different sets of experiments were performed in order to evaluate the performance of the proposed techniques (wrapper-based and filter-based). All experiments were performed on four datasets containing three popular e-fraud types: credit card fraud, email spam and phishing email. In addition, experiments were performed on 20 datasets provided by the well-known UCI data repository. The results show that the proposed filter-based techniques excellently improved SVM training speed in 100% (24 out of 24) of the datasets used for evaluation, without significantly affecting SVM classification quality. Moreover, experimental results also show that the wrapper-based techniques consistently improved SVM predictive accuracy in 78% (18 out of 23) of the datasets used for evaluation and simultaneously improved SVM training speed in all cases. Furthermore, two different statistical tests were conducted to further validate the credibility of the results: Freidman’s test and Holm’s post-hoc test. The statistical test results reveal that the proposed filter-based and wrapper-based techniques are significantly faster, compared to standard SVM and some existing instance selection techniques, in all cases. Moreover, statistical test results also reveal that Cuckoo Search Instance Selection Algorithm outperform all the proposed techniques, in terms of speed. Overall, the proposed techniques have proven to be fast and accurate ML-based e-fraud detection techniques, with improved training speed, predictive accuracy and storage reduction. In real life application, such as video surveillance and intrusion detection systems, that require a classifier to be trained very quickly for speedy classification of new target concepts, the filter-based techniques provide the best solutions; while the wrapper-based techniques are better suited for applications, such as email filters, that are very sensitive to slight changes in predictive accuracy