Seamless Vertical Handoff using Invasive Weed Optimization (IWO) algorithm for heterogeneous wireless networks

AbstractHeterogeneous wireless networks are an integration of two different networks. For better performance, connections are to be exchanged among the different networks using seamless Vertical Handoff. The evolutionary algorithm of invasive weed optimization algorithm popularly known as the IWO has been used in this paper, to solve the Vertical Handoff (VHO) and Horizontal Handoff (HHO) problems. This integer coded algorithm is based on the colonizing behavior of weed plants and has been developed to optimize the system load and reduce the battery power consumption of the Mobile Node (MN). Constraints such as Receiver Signal Strength (RSS), battery lifetime, mobility, load and so on are taken into account. Individual as well as a combination of a number of factors are considered during decision process to make it more effective. This paper brings out the novel method of IWO algorithm for decision making during Vertical Handoff. Therefore the proposed VHO decision making algorithm is compared with the existing SSF and OPTG methods

Recommendation with User Trust and Item Rating

Recommender systems is becomes widespread and utilized in several fields for gathering the knowledge supported the user necessities. It�s in the main wont to facilitate the user for accessing the method supported the relevant data. Several framework for recommendation systems supported the various algorithms area unit revolve round the idea of accuracy solely however alternative necessary feature like diversity of the recommendations area unit neglected. The main idea of these works is that not only incorporating demographic information of users in profile matching process of CF-based algorithms is important weighting should be assigned to these features including rating feature the motivation behind this idea is that �different users place different importance or priority on each feature of the user � profile. For example if a male user prefers to be given recommendations based on the opinions of the other men then his feature weight for gender would be higher than other features�. Here we apply improved invasive weed optimization (IIWO) algorithm for the same purpose with some little changes in selecting the potential similar users as described in the previous sub section and in the evaluation criteria. After the optima weights have been found the two profiles are compared according to equation based on the Euclidean distance of the two profiles

Clustering in Recommendation Systems Using Swarm Intelligence

Ένα σύστημα συστάσεων είναι μία εφαρμογή που εκμεταλλεύεται πληροφορίες για να βοηθήσει τους χρήστες στη λήψη αποφάσεων προτείνοντας αντικείμενα που μπορεί να τους αρέσουν. Ένα σύστημα συστάσεων που βασίζεται στην τεχνική του συνεργατικού φιλτραρίσματος (collaborative filtering) δημιουργεί συστάσεις στους χρήστες με βάση τις προτιμήσεις παρόμοιων χρηστών. Ωστόσο, αυτός ο τύπος συστήματος συστάσεων δεν είναι τόσο αποτελεσματικός όταν τα δεδομένα αυξάνονται σε μεγάλο βαθμό (scalability) ή όταν δεν υπάρχει αρκετή πληροφορία (sparsity), καθώς δεν ομαδοποιούνται σωστά οι παρόμοιοι χρήστες. Αυτή η διπλωματική εργασία προτείνει τρείς υβριδικούς αλγορίθμους που ο καθένας συνδυάζει τον αλγόριθμο k-means με έναν αλγόριθμο ευφυΐας σμήνους για να βελτιώσει την ομαδοποίηση των χρηστών, και κατ’ επέκταση την ποιότητα των συστάσεων. Οι αλγόριθμοι ευφυΐας σμήνους που χρησιμοποιούνται είναι o αλγόριθμος τεχνητής κοινωνίας μελισσών (artificial bee colony), ο αλγόριθμος βελτιστοποίησης αναζήτησης κούκων (cuckoo search optimization) και ο αλγόριθμος βελτιστοποίησης γκρίζων λύκων (grey-wolf optimization). Οι προτεινόμενες μέθοδοι αξιολογήθηκαν χρησιμοποιώντας ένα σύνολο δεδομένων του MovieLens. Η αξιολόγηση δείχνει πως τα προτεινόμενα συστήματα συστάσεων αποδίδουν καλύτερα σε σύγκριση με τις ήδη υπάρχουσες τεχνικές όσον αφορά τις μετρικές του μέσου απόλυτου σφάλματος (mean absolute error - MAE), της ακρίβειας (precision), του αθροίσματος των τετραγωνικών σφαλμάτων (sum of squared errors - SSE) και της ανάκλησης (recall). Επιπλέον, τα αποτελέσματα της αξιολόγησης δείχνουν πως ο υβριδικός αλγόριθμος που χρησιμοποιεί την μέθοδο της τεχνητής κοινωνίας μελισσών αποδίδει ελαφρώς καλύτερα από τους άλλους δύο προτεινόμενους αλγορίθμους.A recommender system (RS) is an application that exploits information to help users in decision making by suggesting items they might like. A collaborative recommender system generates recommendations to users based on their similar neighbor’s preferences. However, this type of recommender system faces the data sparsity and scalability problems making the neighborhood selection a challenging task. This thesis proposes three hybrid collaborative recommender systems that each one combines the k-means algorithm with a different bio-inspired technique to enhance the clustering task, and therefore to improve the recommendation quality. The used bio-inspired techniques are artificial bee colony (ABC), cuckoo search optimization (CSO), and grey-wolf optimizer (GWO). The proposed approaches were evaluated over a MovieLens dataset. The evaluation shows that the proposed recommender systems perform better compared to already existing techniques in terms of mean absolute error (MAE), precision, sum of squared errors (SSE), and recall. Moreover, the experimental results indicate that the hybrid recommender system that uses the ABC method performs slightly better than the other two proposed hybrid algorithms

A New Optimization via Invasive Weeds Algorithm for Dynamic Facility Layout Problem

Abstract-The dynamic facility layout problem (DFLP) is the problem of finding positions of departments o

Automatic Generation Control by Hybrid Invasive Weed Optimization and Pattern Search Tuned 2-DOF PID Controller

A hybrid invasive weed optimization and pattern search (hIWO-PS) technique is proposed in this paper to design 2 degree of freedom proportionalintegral- derivative (2-DOF-PID) controllers for automatic generation control (AGC) of interconnected power systems. Firstly, the proposed approach is tested in an interconnected two-area thermal power system and the advantage of the proposed approach has been established by comparing the results with recently published methods like conventional Ziegler Nichols (ZN), differential evolution (DE), bacteria foraging optimization algorithm (BFOA), genetic algorithm (GA), particle swarm optimization (PSO), hybrid BFOA-PSO, hybrid PSO-PS and non-dominated shorting GA-II (NSGA-II) based controllers for the identical interconnected power system. Further, sensitivity investigation is executed to demonstrate the robustness of the proposed approach by changing the parameters of the system, operating loading conditions, locations as well as size of the disturbance. Additionally, the methodology is applied to a three area hydro thermal interconnected system with appropriate generation rate constraints (GRC). The superiority of the presented methodology is demonstrated by presenting comparative results of adaptive neuro fuzzy inference system (ANFIS), hybrid hBFOA-PSO as well as hybrid hPSO-PS based controllers for the identical system

Непрерывная вертикальная передача обслуживания в гетерогенных беспроводных сетях с использованием алгоритма модифицированной оптимизации удаления ненужных данных

Полный текст доступен на сайте издания по подписке: http://radio.kpi.ua/article/view/S0021347017100028В глобальном контексте существует большое количество сетей с беспроводным доступом. В гетерогенных беспроводных сетях используются различные типы реализаций, включая работу в реальном масштабе времени, работу в нереальном масштабе времени и работу с высокой пропускной способностью, поэтому провайдер услуг связи должен обеспечить поддержку соответствующих соединений. Для улучшения рабочих характеристик, соединения должны коммутироваться между различными сетями с использованием непрерывной вертикальной передачи обслуживания VHO (vertical handoff). Предложенный алгоритм обеспечивает улучшение, обусловленное методом оптимизации, который включает процесс решения о коммутации с использованием запуска VHO и выбора сети. Запуск VHO инициируется путем использования уровня принятого сигнала RSS (received signal stregth). В случае использования традиционного метода, запуск VHO инициируется только с помощью RSS. Предложенный метод, представляющий собой алгоритм модифицированной оптимизации удаления ненужных данных M-WO (modified weed optimization), сокращает ненужные VHO путем учета как RSS, так и скорости мобильного узла при запуске VHO. Для достижения эффективности процесса выбора сети необходимо учитывать индивидуально или совместно следующие параметры: срок службы аккумуляторной батареи, процент сорванных звонков при передаче обслуживания, нагрузку, адаптацию по методу динамических весовых коэффициентов и т.п. В этой работе показан новый эффект алгоритма M-WO для принятия решения при VHO. Усилия авторов направлены на существенную оптимизацию нагрузки системы с тем, чтобы уменьшить процент сорванных звонков при VHO и потребление энергии аккумуляторной батареей мобильного узла MN (mobile node). Вес каждого показателя QoS подстраивается в соответствии с изменяющимися условиями работы сетей для отслеживания M-WO. Таким образом, предложенный алгоритм принятия решения при VHO превосходит существующие методы SSF и OPTG. Результаты моделирования показали, что эффективность алгоритма M-WO выше эффективности методов SSF и OPTG в отношении нагрузки, процента сорванных звонков при передаче обслуживания и срока службы батареи MN

BAS-ADAM: an ADAM based approach to improve the performance of beetle antennae search optimizer

In this paper, we propose enhancements to Beetle Antennae search ( BAS ) algorithm, called BAS-ADAM, to smoothen the convergence behavior and avoid trapping in local-minima for a highly non-convex objective function. We achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation ( ADAM ) update rule. The proposed algorithm also increases the convergence rate in a narrow valley. A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size. Since ADAM is traditionally used with gradient-based optimization algorithms, therefore we first propose a gradient estimation model without the need to differentiate the objective function. Resultantly, it demonstrates excellent performance and fast convergence rate in searching for the optimum of non-convex functions. The efficiency of the proposed algorithm was tested on three different benchmark problems, including the training of a high-dimensional neural network. The performance is compared with particle swarm optimizer ( PSO ) and the original BAS algorithm

Adaptive bio-inspired firefly and invasive weed algorithms for global optimisation with application to engineering problems

The focus of the research is to investigate and develop enhanced version of swarm intelligence firefly algorithm and ecology-based invasive weed algorithm to solve global optimisation problems and apply to practical engineering problems. The work presents two adaptive variants of firefly algorithm by introducing spread factor mechanism that exploits the fitness intensity during the search process. The spread factor mechanism is proposed to enhance the adaptive parameter terms of the firefly algorithm. The adaptive algorithms are formulated to avoid premature convergence and better optimum solution value. Two new adaptive variants of invasive weed algorithm are also developed seed spread factor mechanism introduced in the dispersal process of the algorithm. The working principles and structure of the adaptive firefly and invasive weed algorithms are described and discussed. Hybrid invasive weed-firefly algorithm and hybrid invasive weed-firefly algorithm with spread factor mechanism are also proposed. The new hybridization algorithms are developed by retaining their individual advantages to help overcome the shortcomings of the original algorithms. The performances of the proposed algorithms are investigated and assessed in single-objective, constrained and multi-objective optimisation problems. Well known benchmark functions as well as current CEC 2006 and CEC 2014 test functions are used in this research. A selection of performance measurement tools is also used to evaluate performances of the algorithms. The algorithms are further tested with practical engineering design problems and in modelling and control of dynamic systems. The systems considered comprise a twin rotor system, a single-link flexible manipulator system and assistive exoskeletons for upper and lower extremities. The performance results are evaluated in comparison to the original firefly and invasive weed algorithms. It is demonstrated that the proposed approaches are superior over the individual algorithms in terms of efficiency, convergence speed and quality of the optimal solution achieved