Implementation of a personalized food recommendation system based on collaborative filtering and knapsack method

Food recommendation system is one of the most interesting recommendation problems since it provides data for decision-making to users on selection of foods that meets individual preference of each user. Personalized recommender system has been used to recommend foods or menus to respond to requirements and restrictions of each user in a better way. This research study aimed to develop a personalized healthy food recommendation system based on collaborative filtering and knapsack method. Assessment results found that users were satisfied with the personalized healthy food recommendation system based on collaborative filtering and knapsack problem algorithm which included ability of operating system, screen design, and efficiency of operating system. The average satisfaction score overall was 4.20 implying that users had an excellent level of satisfaction

“Cooperation Greedy Monkey Algorithm”: Algoritmo paralelo para resolver la clase fuertemente correlacionada del problema de la mochila 0-1

Se presenta la paralelización del Cooperation Greedy Monkey Algorithm y el ajuste de parámetros para resolver el problema KP 0-1 (0-1 Knapsack Problem). Los problemas resueltos son tomados de la literatura especializada hasta las instancias establecidas por Pisinger, las no correlacionadas, las débilmente correlacionadas y las fuertemente correlacionadas. Se amplía la capacidad de solución del algoritmo para resolver instancias con diferentes porcentajes del 25% y 50% de la suma de los pesos de los elementos, y no únicamente el 75% como está diseñado el algoritmo originalmente. Se utilizó un modelo maestro-esclavo para su implementación paralela en un cluster de 5 servidores. Los resultados son alentadores y en algunas ocasiones se calcula la solución óptima

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

A GUI Driven Platform for Implementing Evolutionary Algorithms in Java

CodeMonkey-GA (CM) is a GUI driven software development platform that enables non-experts and experts alike to turn an evolutionary algorithm design into a working Java program, with a minimum amount of manual coding. CM is provided as a framework and plug-in application for the Eclipse platform for non-commercial uses. We compare some of the most popular frameworks and platforms for evolutionary computation. We discuss their shortfalls and justify the need for still another platform. Hence, we present CodeMonkey-GA: its concept, internal architecture and design. We provide an overview of the graphical user interface (GUI) of the platform followed by examples of evolutionary algorithm applications, all generated using CodeMonkey’s Eclipse application. Through several examples we demonstrate the ease of use and (to some degree) the applicability of the CM application. The Ackley function is a well-known test function for optimization; the Traveling Salesman Problem is a famous example of NP-Complete problems; the Knapsack problem is an example of combinatorial optimization. In all three cases, CM is used to develop working Java programs that provided satisfactory solutions, which are as good as, or better than the given solutions. Critically, in all cases, not a line of code was entered or altered – bar the fitness function – by the user

End-to-end security in service-oriented architecture

A service-oriented architecture (SOA)-based application is composed of a number of distributed and loosely-coupled web services, which are orchestrated to accomplish a more complex functionality. Any of these web services is able to invoke other web services to offload part of its functionality. The main security challenge in SOA is that we cannot trust the participating web services in a service composition to behave as expected all the time. In addition, the chain of services involved in an end-to-end service invocation may not be visible to the clients. As a result, any violation of client’s policies could remain undetected. To address these challenges in SOA, we proposed the following contributions. First, we devised two composite trust schemes by using graph abstraction to quantitatively maintain the trust levels of different services. The composite trust values are based on feedbacks from the actual execution of services, and the structure of the SOA application. To maintain the dynamic trust, we designed the trust manager, which is a trusted-third party service. Second, we developed an end-to-end inter-service policy monitoring and enforcement framework (PME framework), which is able to dynamically inspect the interactions between services at runtime and react to the potentially malicious activities according to the client’s policies. Third, we designed an intra-service policy monitoring and enforcement framework based on taint analysis mechanism to monitor the information flow within services and prevent information disclosure incidents. Fourth, we proposed an adaptive and secure service composition engine (ASSC), which takes advantage of an efficient heuristic algorithm to generate optimal service compositions in SOA. The service compositions generated by ASSC maximize the trustworthiness of the selected services while meeting the predefined QoS constraints. Finally, we have extensively studied the correctness and performance of the proposed security measures based on a realistic SOA case study. All experimental studies validated the practicality and effectiveness of the presented solutions

Demand Side Management In Smart Grid Optimization Using Artificial Fish Swarm Algorithm

The demand side management and their response including peak shaving approaches and motivations with shiftable load scheduling strategies advantages are the main focus of this paper. A recent real-time pricing model for regulating energy demand is proposed after a survey of literature-based demand side management techniques. Lack of user’s resources needed to change their energy consumption for the system's overall benefit. The recommended strategy involves modern system identification and administration that would enable user side load control. This might assist in balancing the demand and supply sides more effectively while also lowering peak demand and enhancing system efficiency. The AFSA and BFO algorithms are combined in this study to handle the optimization of difficult problems in a range of industries. Although the BFO will be used to exploit the search space and converge to the optimum solution, the AFSA will be used to explore the search space and retain variation. In terms of reduction of peak demand, energy consumption, and user satisfaction, the AFSA-BFO hybrid algorithm outperforms previous techniques in the field of demand side management in a smart grid context, using an AFSA. According to simulation results, the genetic algorithm successfully reduces PAR and power consumption expenses