Solving the Set Covering Problem with a Shuffled Frog Leaping Algorithm

2015 ◽  
pp. 41-50 ◽  
Author(s):  
Broderick Crawford ◽  
Ricardo Soto ◽  
Cristian Peña ◽  
Wenceslao Palma ◽  
Franklin Johnson ◽  
...  
Keyword(s):  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Shuffled Frog Leaping Algorithm ◽  
Shuffled Frog Leaping

Binarization functions for Shuffled Frog Leaping Algorithm can solve the Set Covering Problem

2015 ◽  
Author(s):  
Broderick Crawford ◽  
Ricardo Soto ◽  
Cristian Pena ◽  
Marco Riquelme-Leiva ◽  
Claudio Torres-Rojas ◽  
...  
Keyword(s):  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Shuffled Frog Leaping Algorithm ◽  
Shuffled Frog Leaping

A new approach using a Binary Firefly Algorithm for the Set Covering Problem

2014 ◽  
Author(s):  
Broderick Crawford ◽  
Ricardo Soto ◽  
Miguel Olivares-Suárez ◽  
Fernando Paredes
Keyword(s):  
Firefly Algorithm ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
New Approach

scholarly journals Multi-objective database queries in combined knapsack and set covering problem domains

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sean A. Mochocki ◽  
Gary B. Lamont ◽  
Robert C. Leishman ◽  
Kyle J. Kauffman
Keyword(s):  
Relational Database ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Database Queries ◽  
Multi Objective ◽  
Data Representations ◽  
Np Hard Problem ◽  
Institute Of Technology ◽  
The One

AbstractDatabase 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.

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