Finding Solutions of the Set Covering Problem with an Artificial Fish Swarm Algorithm Optimization

2016 ◽  
pp. 166-181
Author(s):  
Broderick Crawford ◽  
Ricardo Soto ◽  
Eduardo Olguín ◽  
Sanjay Misra ◽  
Sebastián Mansilla Villablanca ◽  
...  
Keyword(s):  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Algorithm Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

An Artificial Fish Swarm Optimization Algorithm to Solve Set Covering Problem

2016 ◽  
pp. 892-903
Author(s):  
Broderick Crawford ◽  
Ricardo Soto ◽  
Eduardo Olguín ◽  
Sebastián Mansilla Villablanca ◽  
Álvaro Gómez Rubio ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Swarm Optimization ◽  
Artificial Fish Swarm

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

PERFORMANCE EVALUATION OF CULTURAL ARTIFICIAL BEE COLONY AND CULTURAL ARTIFICIAL FISH SWARM ALGORITHM

2019 ◽  
Vol 6 (4) ◽  
pp. 43
Author(s):  
HADIR ADEBIYI BUSAYO ◽  
TIJANI SALAWUDEEN AHMED ◽  
FOLASHADE O. ADEBIYI RISIKAT ◽  
◽  
◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Artificial Bee Colony ◽  
Artificial Fish Swarm Algorithm ◽  
Bee Colony ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

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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