Genetic Algorithm with Approximation Algorithm Based Initial Population for the Set Covering Problem

2021 ◽  
pp. 59-78
Author(s):  
Hajar Razip ◽  
Nordin Zakaria
Keyword(s):  
Genetic Algorithm ◽  
Approximation Algorithm ◽  
Set Covering ◽  
Covering Problem ◽  
Initial Population ◽  
Set Covering Problem

scholarly journals Model-based view planning for building inspection and surveillance using voxel dilation, Medial Objects, and Random-Key Genetic Algorithm

2017 ◽  
Vol 5 (3) ◽  
pp. 337-347 ◽  
Author(s):  
Wei Jing ◽  
Kenji Shimada
Keyword(s):  
Genetic Algorithm ◽  
Computational Method ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Greedy Search ◽  
View Planning ◽  
3D Environment ◽  
Optimal Set ◽  
Gaussian Sampling

Abstract Model-based view planning is to find a near-optimal set of viewpoints that cover the surface of a target geometric model. It has been applied to many building inspection and surveillance applications with Unmanned Aerial Vehicle (UAV). Previous approaches proposed in the past few decades suffer from several limitations: many of them work exclusively for 2D problems, generate only a sub-optimal set of views for target surfaces in 3D environment, and/or generate a set of views that cover only part of the target surfaces in 3D environment. This paper presents a novel two-step computational method for finding near-optimal views to cover the surface of a target set of buildings using voxel dilation, Medial Objects (MO), and Random-Key Genetic Algorithm (RKGA). In the first step, the proposed method inflates the building surfaces by voxel dilation to define a sub-volume around the buildings. The MO of this sub-volume is then calculated, and candidate viewpoints are sampled using Gaussian sampling around the MO surface. In the second step, an optimization problem is formulated as (partial) Set Covering Problem and solved by searching through the candidate viewpoints using RKGA and greedy search. The performance of the proposed two-step computational method was measured with several computational cases, and the performance was compared with two previously proposed methods: the optimal-scan-zone method and the randomized sampling-based method. The results demonstrate that the proposed method outperforms the previous methods by finding a better solution with fewer viewpoints and higher coverage ratio compared to the previous methods. Highlights A two-step “generate-test” view planning method is proposed. Voxel dilation, Medial Objects and Gaussian sampling are used to generate viewpoints. Random-Key GA and Greedy search are combined to solve the Set Covering Problem. The proposed method is benchmarked and outperforms two existing methods.

scholarly journals Greedy Genetic Algorithm for the Data Aggregator Positioning Problem in Smart Grids

2021 ◽  
Vol 24 (68) ◽  
pp. 123-137
Author(s):  
Sami Nasser Lauar ◽  
Mario Mestria
Keyword(s):  
Genetic Algorithm ◽  
Smart Grids ◽  
Greedy Algorithms ◽  
The Other ◽  
Set Covering ◽  
Covering Problem ◽  
Initial Population ◽  
Computational Results ◽  
Optimal Solutions ◽  
The Greedy Algorithm

In this work, we present a metaheuristic based on the genetic and greedy algorithms to solve an application of the set covering problem (SCP), the data aggregator positioning in smart grids. The GGH (Greedy Genetic Hybrid) is structured as a genetic algorithm, but it has many modifications compared to the classic version. At the mutation step, only columns included in the solution can suffer mutation and be removed. At the recombination step, only columns from the parent’s solutions are available to generate the offspring. Moreover, the greedy algorithm generates the initial population, reconstructs solutions after mutation, and generates new solutions from the recombination step. Computational results using OR-Library problems showed that the GGH reached optimal solutions for 40 instances in a total of 75 and, in the other instances, obtained good and promising values, presenting a medium gap of 1,761%.

A Genetic Algorithm for the Set Covering Problem

1996 ◽  
Vol 47 (5) ◽  
pp. 702 ◽  
Author(s):  
K. S. Al-Sultan ◽  
M. F. Hussain ◽  
J. S. Nizami
Keyword(s):  
Genetic Algorithm ◽  
Set Covering ◽  
Covering Problem ◽  
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.

A parallel genetic algorithm to solve the set-covering problem

2002 ◽  
Vol 29 (9) ◽  
pp. 1221-1235 ◽  
Author(s):  
Mauricio Solar ◽  
Vı́ctor Parada ◽  
Rodrigo Urrutia
Keyword(s):  
Genetic Algorithm ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Parallel Genetic Algorithm
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