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%.

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.

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

scholarly journals A New Formulation of the Set Covering Problem for Metaheuristic Approaches

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Nehme Bilal ◽  
Philippe Galinier ◽  
Francois Guibault
Keyword(s):  
Optimization Problem ◽  
Set Covering ◽  
Covering Problem ◽  
Greedy Heuristic ◽  
Test Problems ◽  
Set Covering Problem ◽  
Computational Results ◽  
Unconstrained Optimization Problem ◽  
New Formulation ◽  
Neighbourhood Structure

Two difficulties arise when solving the set covering problem (SCP) with metaheuristic approaches: solution infeasibility and set redundancy. In this paper, we first present a review and analysis of the heuristic approaches that have been used in the literature to address these difficulties. We then present a new formulation that can be used to solve the SCP as an unconstrained optimization problem and that eliminates the need to address the infeasibility and set redundancy issues. We show that all local optimums with respect to the new formulation and a 1-flip neighbourhood structure are feasible and free of redundant sets. In addition, we adapt an existing greedy heuristic for the SCP to the new formulation and compare the adapted heuristic to the original heuristic using 88 known test problems for the SCP. Computational results show that the adapted heuristic finds better results than the original heuristic on most of the test problems in shorter computation times.

scholarly journals Incorporating multiple ecological criteria in classical zero one selection algorithms

Web Ecology ◽  
2002 ◽  
Vol 3 (1) ◽  
pp. 48-55 ◽  
Author(s):  
D. P. Memtsas ◽  
P. G. Dimitrakopoulos ◽  
A. Y. Troumbis
Keyword(s):  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
Optimal Solutions ◽  
Species List ◽  
Basic Set ◽  
Ecological Criteria ◽  
Conservation Values ◽  
Species Rarity ◽  
Selection Algorithms

Abstract. Avifauna on Greek wetland sites is used as a model for the implementation of the Set Covering Problem in selecting nature reserves. Three site conservation values, which depend on species presence, are used as selection criteria. Their calculation is based upon species richness, species rarity and species-danger status. The conservation values must be inserted in the linear programming problem’s objective function by the form of weighting factors. Optimal solutions according to the three ecological criteria are produced. These solutions belong to the set of alternative optimal solutions of the basic Set Covering Problem with no other criterion taken into account except that of the whole species-list coverage. The set of alternative optimal solutions is generated by the explicit exclusion method. The relative value of goal programming and weighing up-criteria methods in producing a unique solution based on the three criteria simultaneously is assessed. Both methods coincide with the same alternative solution that is thus regarded as the final optimal one incorporating all the three ecological criteria.

scholarly journals An Effective Chromosome Representation on Proportional Tuition Fees Assessment Using NSGA-II

2019 ◽  
Vol 4 (3) ◽  
pp. 291
Author(s):  
Farid Jauhari ◽  
Wayan Firdaus Mahmudy ◽  
Achmad Basuki
Keyword(s):  
Genetic Algorithm ◽  
Execution Time ◽  
Genetic Parameters ◽  
The Other ◽  
Optimal Solutions ◽  
Tuition Fees ◽  
Nsga Ii ◽  
Chromosome Representation ◽  
Time Required

Proportional tuition fees assessment is an optimization process to find a compromise point between student willingness to pay and institution income. Using a genetic algorithm to find optimal solutions requires effective chromosome representations, parameters, and operator genetic to obtain efficient search. This paper proposes a new chromosome representation and also finding efficient genetic parameters to solve the proportional tuition fees assessment problem. The results of applying the new chromosome representation are compared with another chromosome representation in the previous study. The evaluations show that the proposed chromosome representation obtains better results than the other in both execution time required and the quality of the solutions.

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