scholarly journals Adaptive Black Hole Algorithm for Solving the Set Covering Problem

2018 ◽  
Vol 2018 ◽  
pp. 1-23 ◽  
Author(s):  
Ricardo Soto ◽  
Broderick Crawford ◽  
Rodrigo Olivares ◽  
Carla Taramasco ◽  
Ignacio Figueroa ◽  
...  
Keyword(s):  
Black Hole ◽  
Optimization Problems ◽  
Main Idea ◽  
Industrial Applications ◽  
Set Covering ◽  
Covering Problem ◽  
Problem Solver ◽  
Set Covering Problem ◽  
A New Technique ◽  
Black Hole Algorithm

Evolutionary algorithms have been used to solve several optimization problems, showing an efficient performance. Nevertheless, when these algorithms are applied they present the difficulty to decide on the appropriate values of their parameters. Typically, parameters are specified before the algorithm is run and include population size, selection rate, and operator probabilities. This process is known as offline control and is even considered as an optimization problem in itself. On the other hand, parameter settings or control online is a variation of the algorithm original version. The main idea is to vary the parameters so that the algorithm of interest can provide the best convergence rate and thus may achieve the best performance. In this paper, we propose an adaptive black hole algorithm able to dynamically adapt its population according to solving performance. For that, we use autonomous search which appeared as a new technique that enables the problem solver to control and adapt its own parameters and heuristics during solving in order to be more efficient without the knowledge of an expert user. In order to test this approach, we resolve the set covering problem which is a classical optimization benchmark with many industrial applications such as line balancing production, crew scheduling, service installation, and databases, among several others. We illustrate encouraging experimental results, where the proposed approach is able to reach various global optimums for a well-known instance set from Beasley’s OR-Library, while improving various modern metaheuristics.

An Binary Black Hole Algorithm to Solve Set Covering Problem

2016 ◽  
pp. 873-883 ◽  
Author(s):  
Álvaro Gómez Rubio ◽  
Broderick Crawford ◽  
Ricardo Soto ◽  
Adrián Jaramillo ◽  
Sebastián Mansilla Villablanca ◽  
...  
Keyword(s):  
Black Hole ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Binary Black Hole ◽  
Black Hole Algorithm

The set covering problem solved by the Black Hole Algorithm

2016 ◽  
Author(s):  
Ricardo Soto ◽  
Broderick Crawford ◽  
Ignacio Figueroa ◽  
Rodrigo Olivares ◽  
Eduardo Olguin
Keyword(s):  
Black Hole ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Black Hole Algorithm

An binary black hole algorithm to solve the set covering problem

2016 ◽  
Author(s):  
Alvaro Gomez ◽  
Broderick Crawford ◽  
Ricardo Soto ◽  
Adrian Jaramillo ◽  
Sebastian Mansilla ◽  
...  
Keyword(s):  
Black Hole ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Binary Black Hole ◽  
Black Hole Algorithm

A Black Hole Algorithm for Solving the Set Covering Problem

2016 ◽  
pp. 855-861
Author(s):  
Ricardo Soto ◽  
Broderick Crawford ◽  
Ignacio Figueroa ◽  
Stefanie Niklander ◽  
Eduardo Olguín
Keyword(s):  
Black Hole ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Black Hole Algorithm

A self-adaptive biogeography-based algorithm to solve the set covering problem

2019 ◽  
Vol 53 (3) ◽  
pp. 1033-1059
Author(s):  
Broderick Crawford ◽  
Ricardo Soto ◽  
Rodrigo Olivares ◽  
Luis Riquelme ◽  
Gino Astorga ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Industrial Applications ◽  
Original Version ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Control Parameters ◽  
Production Crew ◽  
Classical Optimization ◽  
Self Adaptive

Using the approximate algorithms, we are faced with the problem of determining the appropriate values of their input parameters, which is always a complex task and is considered an optimization problem. In this context, incorporating online control parameters is a very interesting issue. The aim is to vary the parameters during the run so that the studied algorithm can provide the best convergence rate and, thus, achieve the best performance. In this paper, we compare the performance of a self-adaptive approach for the biogeography-based optimization algorithm using the mutation rate parameter with respect to its original version and other heuristics. This work proposes altering some parameters of the metaheuristic according to its exhibited efficiency. To test this approach, we solve the set covering problem, which is a classical optimization benchmark with many industrial applications such as line balancing production, crew scheduling, service installation, databases, among several others. We illustrate encouraging experimental results, where the proposed approach is capable of reaching various global optimums for a well-known instance set taken from the Beasleys OR-Library, and sometimes, it improves the results obtained by the original version of the algorithm.

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 OR Practitioner's Solution Approach for the Set Covering Problem

2015 ◽  
Vol 6 (4) ◽  
pp. 1-13 ◽  
Author(s):  
Yun Lu ◽  
Francis J. Vasko
Keyword(s):  
Industrial Applications ◽  
Fine Tuning ◽  
Set Covering ◽  
Covering Problem ◽  
Initial Population ◽  
Optimization Approach ◽  
Set Covering Problem ◽  
Two Phase ◽  
Solution Approach ◽  
Phase Approach

The set covering problem (SCP) is an NP-complete problem that has many important industrial applications. Since industrial applications are typically large in scale, exact solution algorithms are not feasible for operations research (OR) practitioners to use when called on to solve real-world problems involving SCPs. However, the best performing heuristics for the SCP reported in the literature are not usually straightforward to implement. Additionally, these heuristics usually require the fine-tuning of several parameters. In contrast, simple greedy or even randomized greedy heuristics typically do not give as good results as the more sophisticated heuristics. In this paper, the authors present a compromise; a straightforward to implement, population-based solution approach for the SCP. It uses a randomized greedy approach to generate an initial population and then uses a genetic-based two phase approach to improve the population solutions. This two-phase approach uses transformation equations based on a Teaching-Learning based optimization approach developed by Rao, Savsani and Vakharia (2011, 2012) for continuous nonlinear optimization problems. Empirical results using set covering problems from Beasley's OR-library demonstrate the competitiveness of this approach both in terms of solution quality and execution time. The advantage to this approach is its relative simplicity for the practitioner to implement.

Comparing the Black Hole and the Soccer League Competition Algorithms Solving the Set Covering Problem

Polibits ◽  
2018 ◽  
Vol 57 ◽  
pp. 5-17
Author(s):  
Adrián Jaramillo ◽  
Álvaro Gómez ◽  
Broderick Crawford ◽  
Ricardo Soto ◽  
Fernando Paredes ◽  
...  
Keyword(s):  
Black Hole ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem

scholarly journals Using machine learning to predict the number of alternative solutions to a minimum cardinality set covering problem

2021 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Brooks Emerick ◽  
Yun Lu ◽  
Francis J. Vasko
Keyword(s):  
Machine Learning ◽  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Regression Trees ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Minimum Cardinality ◽  
Combinatorial Optimization Problems ◽  
Secondary Objective

Although the characterization of alternative optimal solutions for linear programming problems is well known, such characterizations for combinatorial optimization problems are essentially non-existent. This is the first article to qualitatively predict the number of alternative optima for a classic NP-hard combinatorial optimization problem, namely, the minimum cardinality (also called unicost) set covering problem (MCSCP). For the MCSCP, a set must be covered by a minimum number of subsets selected from a specified collection of subsets of the given set. The MCSCP has numerous industrial applications that require that a secondary objective is optimized once the size of a minimum cover has been determined. To optimize the secondary objective, the number of MCSCP solutions is optimized. In this article, for the first time, a machine learning methodology is presented to generate categorical regression trees to predict, qualitatively (extra-small, small, medium, large, or extra-large), the number of solutions to an MCSCP. Within the machine learning toolbox of MATLAB®, 600,000 unique random MCSCPs were generated and used to construct regression trees. The prediction quality of these regression trees was tested on 5000 different MCSCPs. For the 5-output model, the average accuracy of being at most one off from the predicted category was 94.2%. 

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