A 2-level Approach for the Set Covering Problem: Parameter Tuning of Artificial Bee Colony Algorithm by Using Genetic Algorithm

2014 ◽  
pp. 189-196 ◽  
Author(s):  
Broderick Crawford ◽  
Ricardo Soto ◽  
Wenceslao Palma ◽  
Franklin Johnson ◽  
Fernando Paredes ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Parameter Tuning ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Problem Parameter ◽  
Bee Colony

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.

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.

An effective binary artificial bee colony algorithm for maximum set k-covering problem

2020 ◽  
Vol 161 ◽  
pp. 113717 ◽  
Author(s):  
Geng Lin ◽  
Haiping Xu ◽  
Xiang Chen ◽  
Jian Guan
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Covering Problem ◽  
Bee Colony
Sign in / Sign up

Export Citation Format

Share Document