Studying the effect of multisource Darwinian particle swarm optimization in search and rescue missions

Robotic Swarm Intelligence is considered one of the hottest topics within the robotics research eld nowadays, for its major contributions to di erent elds of life from hobbyists, makers and expanding to military applications. It has also proven to be more effective and effcient than other robotic approaches targeting the same problem. Within this research, we targeted to test the hypothesis that using more than a single starting/ seeding point for a swarm to explore an unknown environment will yield better solutions, routes and cover more area of the search space within context of Search and Rescue applications domain. We tested such hypothesis via extending existing Particle swarm optimization techniques for search and rescue operations (i.e. Robotic Darwinian Particle Swarm Optimization and we split the swarm into smaller groups that start exploration from di erent seed positions, then took the convergence time average for di erent runs of simulations and recorded the results for quanti cation. The results presented in this work con rms the hypothesis we started with, and gives insight to how the number of robots contributing in the experiments a ect the quality of the results. This work also shows a direct correlation between the swarm size and the search space

Hyperspectral Image Classification based on Dimensionality Reduction and Swarm Optimization

Hyperspectral images have high dimensions, making it difficult to determine accurate and efficient image segmentation algorithms. Dimension reduction data is done to overcome these problems. In this paper we use Discriminant independent component analysis (DICA). The accuracy and efficiency of the segmentation algorithm used will affect final results of image classification. In this paper a new method of multilevel thresholding is introduced for segmentation of hyperspectral images. A method of swarm optimization approach, namely Darwinian Particle Swarm Optimization (DPSO) is used to find n-1 optimal m-level threshold on a given image. A new classification image approach based on Darwinian particle swarm optimization (DPSO) and support vector machine (SVM) is used in this paper. The method introduced in this paper is compared to existing approach. The results showed that the proposed method was better than the standard SVM in terms of classification accuracy namely average accuracy (AA), overall accuracy (OA and Kappa index (K)

New Trends in Artificial Intelligence: Applications of Particle Swarm Optimization in Biomedical Problems

Optimization is a process to discover the most effective element or solution from a set of all possible resources or solutions. Currently, there are various biological problems such as extending from biomolecule structure prediction to drug discovery that can be elevated by opting standard protocol for optimization. Particle swarm optimization (PSO) process, purposed by Dr. Eberhart and Dr. Kennedy in 1995, is solely based on population stochastic optimization technique. This method was designed by the researchers after inspired by social behavior of flocking bird or schooling fishes. This method shares numerous resemblances with the evolutionary computation procedures such as genetic algorithms (GA). Since, PSO algorithms is easy process to subject with minor adjustment of a few restrictions, it has gained more attention or advantages over other population based algorithms. Hence, PSO algorithms is widely used in various research fields like ranging from artificial neural network training to other areas where GA can be used in the system

A WEB PERSONALIZATION ARTIFACT FOR UTILITY-SENSITIVE REVIEW ANALYSIS

Online customer reviews are web content voluntarily posted by the users of a product (e.g. camera) or service (e.g. hotel) to express their opinions about the product or service. Online reviews are important resources for businesses and consumers. This dissertation focuses on the important consumer concern of review utility, i.e., the helpfulness or usefulness of online reviews to inform consumer purchase decisions. Review utility concerns consumers since not all online reviews are useful or helpful. And, the quantity of the online reviews of a product/service tends to be very large. Manual assessment of review utility is not only time consuming but also information overloading. To address this issue, review helpfulness research (RHR) has become a very active research stream dedicated to study utility-sensitive review analysis (USRA) techniques for automating review utility assessment. Unfortunately, prior RHR solution is inadequate. RHR researchers call for more suitable USRA approaches. Our current research responds to this urgent call by addressing the research problem: What is an adequate USRA approach? We address this problem by offering novel Design Science (DS) artifacts for personalized USRA (PUSRA). Our proposed solution extends not only RHR research but also web personalization research (WPR), which studies web-based solutions for personalized web provision. We have evaluated the proposed solution by applying three evaluation methods: analytical, descriptive, and experimental. The evaluations corroborate the practical efficacy of our proposed solution. This research contributes what we believe (1) the first DS artifacts to the knowledge body of RHR and WPR, and (2) the first PUSRA contribution to USRA practice. Moreover, we consider our evaluations of the proposed solution the first comprehensive assessment of USRA solutions. In addition, this research contributes to the advancement of decision support research and practice. The proposed solution is a web-based decision support artifact with the capability to substantially improve accurate personalized webpage provision. Also, website designers can apply our research solution to transform their works fundamentally. Such transformation can add substantial value to businesses

Performance and robustness of regional image segmentation driven by selected evolutionary and genetic algorithms: Study on MR articular cartilage images

The analysis and segmentation of articular cartilage magnetic resonance (MR) images belongs to one of the most commonly routine tasks in diagnostics of the musculoskeletal system of the knee area. Conventional regional segmentation methods, which are based either on the histogram partitioning (e.g., Otsu method) or clustering methods (e.g., K-means), have been frequently used for the task of regional segmentation. Such methods are well known as fast and well working in the environment, where cartilage image features are reliably recognizable. The well-known fact is that the performance of these methods is prone to the image noise and artefacts. In this context, regional segmentation strategies, driven by either genetic algorithms or selected evolutionary computing strategies, have the potential to overcome these traditional methods such as Otsu thresholding or K-means in the context of their performance. These optimization strategies consecutively generate a pyramid of a possible set of histogram thresholds, of which the quality is evaluated by using the fitness function based on Kapur's entropy maximization to find the most optimal combination of thresholds for articular cartilage segmentation. On the other hand, such optimization strategies are often computationally demanding, which is a limitation of using such methods for a stack of MR images. In this study, we publish a comprehensive analysis of the optimization methods based on fuzzy soft segmentation, driven by artificial bee colony (ABC), particle swarm optimization (PSO), Darwinian particle swarm optimization (DPSO), and a genetic algorithm for an optimal thresholding selection against the routine segmentations Otsu and K-means for analysis and the features extraction of articular cartilage from MR images. This study objectively analyzes the performance of the segmentation strategies upon variable noise with dynamic intensities to report a segmentation's robustness in various image conditions for a various number of segmentation classes (4, 7, and 10), cartilage features (area, perimeter, and skeleton) extraction preciseness against the routine segmentation strategies, and lastly the computing time, which represents an important factor of segmentation performance. We use the same settings on individual optimization strategies: 100 iterations and 50 population. This study suggests that the combination of fuzzy thresholding with an ABC algorithm gives the best performance in the comparison with other methods as from the view of the segmentation influence of additive dynamic noise influence, also for cartilage features extraction. On the other hand, using genetic algorithms for cartilage segmentation in some cases does not give a good performance. In most cases, the analyzed optimization strategies significantly overcome the routine segmentation methods except for the computing time, which is normally lower for the routine algorithms. We also publish statistical tests of significance, showing differences in the performance of individual optimization strategies against Otsu and K-means method. Lastly, as a part of this study, we publish a software environment, integrating all the methods from this study.Web of Science2217art. no. 633

Bold:Bio-inspired optimized leader election for multiple drones

Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applications. In certain applications like surveillance and emergency rescue operations, multiple drones work as a network to achieve the target in which any one of the drones will act as the master or coordinator to communicate, monitor, and control other drones. Hence, drones are energy-constrained; there is a need for effective coordination among them in terms of decision making and communication between drones and base stations during these critical situations. This paper focuses on providing an efficient approach for the election of the cluster head dynamically, which heads the other drones in the network. The main objective of the paper is to provide an effective solution to elect the cluster head among multi drones at different periods based on the various physical constraints of drones. The elected cluster head acts as the decision-maker and assigns tasks to other drones. In a case where the cluster head fails, then the next eligible drone is re-elected as the leader. Hence, an optimally distributed solution proposed is called Bio-Inspired Optimized Leader Election for Multiple Drones (BOLD), which is based on two AI-based optimization techniques. The simulation results of BOLD compared with the existing Particle Swarm Optimization-Cluster head election (PSO-C) in terms of network lifetime and energy consumption, and from the results, it has been proven that the lifetime of drones with the BOLD algorithm is 15% higher than the drones with PSO-C algorithm

Effect of nano black rice husk ash on the chemical and physical properties of porous concrete pavement

Black rice husk is a waste from this agriculture industry. It has been found that majority inorganic element in rice husk is silica. In this study, the effect of Nano from black rice husk ash (BRHA) on the chemical and physical properties of concrete pavement was investigated. The BRHA produced from uncontrolled burning at rice factory was taken. It was then been ground using laboratory mill with steel balls and steel rods. Four different grinding grades of BRHA were examined. A rice husk ash dosage of 10% by weight of binder was used throughout the experiments. The chemical and physical properties of the Nano BRHA mixtures were evaluated using fineness test, X-ray Fluorescence spectrometer (XRF) and X-ray diffraction (XRD). In addition, the compressive strength test was used to evaluate the performance of porous concrete pavement. Generally, the results show that the optimum grinding time was 63 hours. The result also indicated that the use of Nano black rice husk ash ground for 63hours produced concrete with good strengt

Adapting heterogeneous ensembles with particle swarm optimization for video face recognition

In video-based face recognition applications, matching is typically performed by comparing query samples against biometric models (i.e., an individual’s facial model) that is designed with reference samples captured during an enrollment process. Although statistical and neural pattern classifiers may represent a flexible solution to this kind of problem, their performance depends heavily on the availability of representative reference data. With operators involved in the data acquisition process, collection and analysis of reference data is often expensive and time consuming. However, although a limited amount of data is initially available during enrollment, new reference data may be acquired and labeled by an operator over time. Still, due to a limited control over changing operational conditions and personal physiology, classification systems used for video-based face recognition are confronted to complex and changing pattern recognition environments. This thesis concerns adaptive multiclassifier systems (AMCSs) for incremental learning of new data during enrollment and update of biometric models. To avoid knowledge (facial models) corruption over time, the proposed AMCS uses a supervised incremental learning strategy based on dynamic particle swarm optimization (DPSO) to evolve a swarm of fuzzy ARTMAP (FAM) neural networks in response to new data. As each particle in a FAM hyperparameter search space corresponds to a FAM network, the learning strategy adapts learning dynamics by co-optimizing all their parameters – hyperparameters, weights, and architecture – in order to maximize accuracy, while minimizing computational cost and memory resources. To achieve this, the relationship between the classification and optimization environments is studied and characterized, leading to these additional contributions. An initial version of this DPSO-based incremental learning strategy was applied to an adaptive classification system (ACS), where the accuracy of a single FAM neural network is maximized. It is shown that the original definition of a classification system capable of supervised incremental learning must be reconsidered in two ways. Not only must a classifier’s learning dynamics be adapted to maintain a high level of performance through time, but some previously acquired learning validation data must also be used during adaptation. It is empirically shown that adapting a FAM during incremental learning constitutes a type III dynamic optimization problem in the search space, where the local optima values and their corresponding position change in time. Results also illustrate the necessity of a long term memory (LTM) to store previously acquired data for unbiased validation and performance estimation. The DPSO-based incremental learning strategy was then modified to evolve the swarm (or pool) of FAM networks within an AMCS. A key element for the success of ensembles is tackled: classifier diversity. With several correlation and diversity indicators, it is shown that genoVIII type (i.e., hyperparameters) diversity in the optimization environment is correlated with classifier diversity in the classification environment. Following this result, properties of a DPSO algorithm that seeks to maintain genotype particle diversity to detect and follow local optima are exploited to generate and evolve diversified pools of FAMclassifiers. Furthermore, a greedy search algorithm is presented to perform an efficient ensemble selection based on accuracy and genotype diversity. This search algorithm allows for diversified ensembles without evaluating costly classifier diversity indicators, and selected ensembles also yield accuracy comparable to that of reference ensemble-based and batch learning techniques, with only a fraction of the resources. Finally, after studying the relationship between the classification environment and the search space, the objective space of the optimization environment is also considered. An aggregated dynamical niching particle swarm optimization (ADNPSO) algorithm is presented to guide the FAM networks according two objectives: FAM accuracy and computational cost. Instead of purely solving a multi-objective optimization problem to provide a Pareto-optimal front, the ADNPSO algorithm aims to generate pools of classifiers among which both genotype and phenotype (i.e., objectives) diversity are maximized. ADNPSO thus uses information in the search spaces to guide particles towards different local Pareto-optimal fronts in the objective space. A specialized archive is then used to categorize solutions according to FAMnetwork size and then capture locally non-dominated classifiers. These two components are then integrated to the AMCS through an ADNPSO-based incremental learning strategy. The AMCSs proposed in this thesis are promising since they create ensembles of classifiers designed with the ADNPSO-based incremental learning strategy and provide a high level of accuracy that is statistically comparable to that obtained through mono-objective optimization and reference batch learning techniques, and yet requires a fraction of the computational cost