An improved configuration checking-based algorithm for the unicost set covering problem

2021 ◽  
Author(s):  
Yiyuan Wang ◽  
Shiwei Pan ◽  
Sameh Al-Shihabi ◽  
Junping Zhou ◽  
Nan Yang ◽  
...  
Keyword(s):  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Configuration Checking ◽  
Unicost Set Covering Problem

scholarly journals A Binary Cat Swarm Optimization Algorithm for the Non-Unicost Set Covering Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Broderick Crawford ◽  
Ricardo Soto ◽  
Natalia Berríos ◽  
Franklin Johnson ◽  
Fernando Paredes ◽  
...  
Keyword(s):  
Moving Objects ◽  
Minimum Cost ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Swarm Optimization ◽  
Cat Swarm Optimization ◽  
The Matrix ◽  
Problem Instances ◽  
Unicost Set Covering Problem

The Set Covering Problem consists in finding a subset of columns in a zero-one matrix such that they cover all the rows of the matrix at a minimum cost. To solve the Set Covering Problem we use a metaheuristic called Binary Cat Swarm Optimization. This metaheuristic is a recent swarm metaheuristic technique based on the cat behavior. Domestic cats show the ability to hunt and are curious about moving objects. Based on this, the cats have two modes of behavior: seeking mode and tracing mode. We are the first ones to use this metaheuristic to solve this problem; our algorithm solves a set of 65 Set Covering Problem instances from OR-Library.

A GRASP algorithm to solve the unicost set covering problem

2007 ◽  
Vol 34 (10) ◽  
pp. 3162-3173 ◽  
Author(s):  
Joaquín Bautista ◽  
Jordi Pereira
Keyword(s):  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Unicost Set Covering Problem

scholarly journals Application of the Artificial Bee Colony Algorithm for Solving the Set Covering Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Broderick Crawford ◽  
Ricardo Soto ◽  
Rodrigo Cuesta ◽  
Fernando Paredes
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Formal Model ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Solution Quality ◽  
Bee Colony ◽  
Unicost Set Covering Problem

The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. Experimental results show that our artificial bee colony algorithm is competitive in terms of solution quality with other recent metaheuristic approaches for the set covering problem.

An electromagnetism metaheuristic for the unicost set covering problem

2010 ◽  
Vol 205 (2) ◽  
pp. 290-300 ◽  
Author(s):  
Zahra Naji-Azimi ◽  
Paolo Toth ◽  
Laura Galli
Keyword(s):  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Unicost Set Covering Problem

A Memetic Approach for the Unicost Set Covering Problem

2020 ◽  
pp. 233-248
Author(s):  
Maxime Pinard ◽  
Laurent Moalic ◽  
Mathieu Brévilliers ◽  
Julien Lepagnot ◽  
Lhassane Idoumghar
Keyword(s):  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Unicost Set Covering Problem

scholarly journals The Relationship between the Unicost Set Covering Problem and the Attribute Reduction Problem in Rough Set Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Qingyuan Xu ◽  
Jinjin Li
Keyword(s):  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Reduction Problem ◽  
Covering Model ◽  
The Relationship ◽  
Unicost Set Covering Problem

The unicost set covering problem and the attribute reduction problem are NP-complete problems. In this paper, the relationship between these two problems are discussed. Based on the transformability between attribute reductions and minimal solutions in unicost set covering models, two methods are provided. One is to induce an information table from a given unicost set covering model. With no doubt, it shows that the unicost set covering problem can be investigated by rough set theory. The other is to induce a unicost set covering model from a given information table. Similarly, it shows that the attribute reduction problem can be studied by set covering theory. As an application of the proposed theoretical results, a rough set heuristic algorithm is presented for the unicost set covering problem.

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