Multi-objective Database Queries in Combined Knapsack and Set Covering Problem Domains

Database queries are one of the most important functions of a relational database. Users are interested in viewing a variety of data representations, and this may vary based on database purpose and the nature of the stored data. The Air Force Institute of Technology has approximately 100 data logs which will be converted to the standardized Scorpion Data Model format. A relational database is designed to house this data and its associated sensor and non-sensor metadata. Deterministic polynomial-time queries were used to test the performance of this schema against two other schemas, with databases of 100 and 1000 logs of repeated data and randomized metadata. Of these approaches, the one that had the best performance was chosen as AFIT’s database solution, and now more complex and useful queries need to be developed to enable filter research. To this end, consider the combined Multi-Objective Knapsack/Set Covering Database Query. Algorithms which address The Set Covering Problem or Knapsack Problem could be used individually to achieve useful results, but together they could offer additional power to a potential user. This paper explores the NP-Hard problem domain of the Multi-Objective KP/SCP, proposes Genetic and Hill Climber algorithms, implements these algorithms using Java, populates their data structures using SQL queries from two test databases, and finally compares how these algorithms perform

Un Enfoque de Meta-Optimización para Resolver el Problema de Cobertura de Conjunto

Context: In the industry the resources are increasingly scarce. For this reason, we must make a gooduse of it. Being the optimization tools, a good alternative that it is necessary to bear in mind. A realworldproblem is the facilities location being the Set Covering Problem, one of the most used models.Our interest, it is to find solution alternatives to this problem of the real-world using metaheuristics. Method: One of the main problems which we turn out to be faced on having used metaheuristic is thedifficulty of realizing a correct parametrization with the purpose to find good solutions. This is not aneasy task, for which our proposal is to use a metaheuristic that allows to provide good parameters toanother metaheuristics that will be responsible for resolving the Set Covering Problem. Results: To prove our proposal, we use the set of 65 instances of OR-Library which also was comparedwith other recent algorithms, used to solve the Set Covering Problem. Conclusions: Our proposal has proved to be very effective able to produce solutions of good qualityavoiding also have to invest large amounts of time in the parametrization of the metaheuristic responsiblefor resolving the problem.Contexto: En la industria los recursos son cada vez más escasos. Por esta razón debemos hacer un buen uso de ellos.Siendo las herramientas de optimización una buena alternativa que se debe tener presente. Un problema del mundo real lo contituye la ubicación de instalaciones siendo el Problema de Cobertura de Conjuntos uno de los modelos más utilizados. Nuestro interés, es encontrar alternativas de solución a este problema de la vida-real utilizando metaheuristicas. Método: Uno de los principales problemas a que nos vemos enfrentados al utilizar metaheurísticas es la dificultad de realizar una correcta parametrización con el objetivo de encontrar buenas soluciones. Esta no es una tarea fácil, para lo cual nuestra propuesta es utilizar una metaheurística que permita proporcionar buenos parametros a otra metaheurstica que será la encargada de resolver el Problema de Cobertura de Conjuntos. Resultados: Para probar nuestra propuesta, utilizamos el set de 65 instancias de OR-Library el cual además fue comparado con otros recientes algoritmos utilizados para resolver el Problema de Cobertura de Conjuntos. Conclusiones: Nuestra propuesta a demostrado ser muy efectiva logrando producir soluciones de buena calidad evitando además que se tenga que invertir gran cantidad de tiempo en la parametrización de la metaheurística encargada de resolver el problema

A Meta-Optimization Approach to Solve the Set Covering Problem

Context: In the industry the resources are increasingly scarce. For this reason, we must make a good use of it. Being the optimization tools, a good alternative that it is necessary to bear in mind. A realworld problem is the facilities location being the Set Covering Problem, one of the most used models. Our interest, it is to find solution alternatives to this problem of the real-world using metaheuristics. Method: One of the main problems which we turn out to be faced on having used metaheuristic is the difficulty of realizing a correct parametrization with the purpose to find good solutions. This is not an easy task, for which our proposal is to use a metaheuristic that allows to provide good parameters to another metaheuristics that will be responsible for resolving the Set Covering Problem. Results: To prove our proposal, we use the set of 65 instances of OR-Library which also was compared with other recent algorithms, used to solve the Set Covering Problem. Conclusions: Our proposal has proved to be very effective able to produce solutions of good quality avoiding also have to invest large amounts of time in the parametrization of the metaheuristic responsible for resolving the problem

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

Sine Cosine Algorithm for Optimization

This open access book serves as a compact source of information on sine cosine algorithm (SCA) and a foundation for developing and advancing SCA and its applications. SCA is an easy, user-friendly, and strong candidate in the field of metaheuristics algorithms. Despite being a relatively new metaheuristic algorithm, it has achieved widespread acceptance among researchers due to its easy implementation and robust optimization capabilities. Its effectiveness and advantages have been demonstrated in various applications ranging from machine learning, engineering design, and wireless sensor network to environmental modeling. The book provides a comprehensive account of the SCA, including details of the underlying ideas, the modified versions, various applications, and a working MATLAB code for the basic SCA