Two-Phase Pareto Local Search to Solve the Biobjective Set Covering Problem

2013 ◽  
Author(s):  
Thibaut Lust ◽  
Daniel Tuyttens
Keyword(s):  
Local Search ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Two Phase ◽  
Pareto Local Search

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.

scholarly journals Ant-Set: A Subset-Oriented Ant Colony Optimization Algorithm for the Set Covering Problem

2020 ◽  
Vol 26 (2) ◽  
pp. 293-316
Author(s):  
Murilo Falleiros Lemos Schmitt ◽  
Mauro Mulati ◽  
Ademir Constantino ◽  
Fábio Hernandes ◽  
Tony Hild
Keyword(s):  
Local Search ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Ant Colony Optimization Algorithm ◽  
The Impact

This paper proposes an algorithm for the set covering problem based on the metaheuristic Ant Colony Optimization (ACO) called Ant-Set, which uses a lineoriented approach and a novelty pheromone manipulation based on the connections between components of the construction graph, while also applying a local search. The algorithm is compared with other ACO-based approaches. The results obtained show the effectiveness of the algorithm and the impact of the pheromone manipulation.

Two-phase method and Lagrangian relaxation to solve the Bi-Objective Set Covering Problem

2006 ◽  
Vol 147 (1) ◽  
pp. 23-41 ◽  
Author(s):  
Christian Prins ◽  
Caroline Prodhon ◽  
Roberto Wolfler Calvo
Keyword(s):  
Lagrangian Relaxation ◽  
Set Covering ◽  
Covering Problem ◽  
Phase Method ◽  
Set Covering Problem ◽  
Two Phase

A 3-flip neighborhood local search for the set covering problem

2006 ◽  
Vol 172 (2) ◽  
pp. 472-499 ◽  
Author(s):  
Mutsunori Yagiura ◽  
Masahiro Kishida ◽  
Toshihide Ibaraki
Keyword(s):  
Local Search ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem
Sign in / Sign up

Export Citation Format

Share Document