scholarly journals An ILS algorithm with RVND for the green vehicle routing problems with time-varying speeds

2018 ◽  
Author(s):  
Darci José Mendes Júnior ◽  
Luciana Brugiolo Gonçalves ◽  
Stênio Sã R. F. Soares
Keyword(s):  
Variable Neighborhood Search ◽  
Search Algorithm ◽  
Random Variable ◽  
Human Action ◽  
Iterated Local Search ◽  
Neighborhood Search ◽  
Time Varying ◽  
Search Procedure ◽  
Local Search Algorithm ◽  
Randomized Search

The environmental impacts of human action have led several countries to create stricter laws and tax breaks to reduce this damage. Thereby, the Green Logistic has been increasingly sought to meet the requirements and needs for a more sustainable development. This work presents an ILS (Iterated Local Search) algorithm combined with RVND (Random Variable Neighborhood Search) and compare it with a GRASP (Greed Randomized Search Procedure) algorithm where each one has two variations: minimize distance and minimize emission. The results show the effectiveness of the ILS approach and heuristics that minimize the total distance covered do not present themselves as good solutions in terms of sustainability.

scholarly journals Reactive GRASP for the prize-collecting covering tour problem

2021 ◽  
Author(s):  
glaubos climaco ◽  
Isabel Rosseti ◽  
Rogério Da Silva ◽  
Marcos Guerine
Keyword(s):  
Processing Time ◽  
Variable Neighborhood Search ◽  
State Of The Art ◽  
Random Variable ◽  
Neighborhood Search ◽  
Search Procedure ◽  
Adaptive Search ◽  
Hybrid Heuristic ◽  
Prize Collecting ◽  
Covering Tour Problem

This paper presents a greedy randomized adaptive search procedure (GRASP) for the prize-collecting covering tour problem, which is the problem of finding a route for traveling teams that provide services to communities geographically distant from large urban locations. We devised a novel hybrid heuristic by combining a reactive extension of the GRASP with Random Variable Neighborhood Search (VND) meta-heuristic for the purpose of solving the PCCTP. Computational experiments were conducted on a PCCTP benchmark from the literature, and the results demonstrate our approach provides a significant improvement in solving PCCTP and comparable with the state-of-the-art, mainly regarding the computational processing time.

A variable neighborhood search algorithm for optimal protection coordination of power systems

2021 ◽  
Author(s):  
H. R. E. H. Bouchekara ◽  
M. S. Shahriar ◽  
M. S. Javaid ◽  
Y. A. Sha’aban ◽  
M. Zellagui ◽  
...  
Keyword(s):  
Power Systems ◽  
Variable Neighborhood Search ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Neighborhood Search Algorithm ◽  
Optimal Protection

scholarly journals DOA Estimation in Non-Uniform Noise Based on Subspace Maximum Likelihood Using MPSO

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1429
Author(s):  
Jui-Chung Hung
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Doa Estimation ◽  
Iterated Local Search ◽  
Local Search Algorithm ◽  
Noise Variance ◽  
Swarm Optimization ◽  
Noise Effects ◽  
Low Snr ◽  
Uniform Noise

In general, the performance of a direction of arrival (DOA) estimator may decay under a non-uniform noise and low signal-to-noise ratio (SNR) environment. In this paper, a memetic particle swarm optimization (MPSO) algorithm combined with a noise variance estimator is proposed, in order to address this issue. The MPSO incorporates re-estimation of the noise variance and iterated local search algorithms into the particle swarm optimization (PSO) algorithm, resulting in higher efficiency and a reduction in non-uniform noise effects under a low SNR. The MPSO procedure is as follows: PSO is initially utilized to evaluate the signal DOA using a subspace maximum-likelihood (SML) method. Next, the best position of the swarm to estimate the noise variance is determined and the iterated local search algorithm to reduce the non-uniform noise effect is built. The proposed method uses the SML criterion to rebuild the noise variance for the iterated local search algorithm, in order to reduce non-uniform noise effects. Simulation experiments confirm that the DOA estimation methods are valid in a high SNR environment, but in a low SNR and non-uniform noise environment, the performance becomes poor because of the confusion between noise and signal sources. The proposed method incorporates the re-estimation of noise variance and an iterated local search algorithm in the PSO. This method is effectively improved by the ability to reduce estimation deviation in low SNR and non-uniform environments.

scholarly journals Two meta-heuristics for solving the multi-vehicle multi-covering tour problem with a constraint on the number of vertices

2020 ◽  
pp. 14-14
Author(s):  
Manel Kammoun ◽  
Houda Derbel ◽  
Bassem Jarboui
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Variable Neighborhood Search ◽  
Search Algorithm ◽  
Descent Method ◽  
Computational Experiments ◽  
Neighborhood Search ◽  
Variable Neighborhood Descent ◽  
Covering Tour Problem ◽  
Neighborhood Search Algorithm

In this work we deal with a generalized variant of the multi-vehicle covering tour problem (m-CTP). The m-CTP consists of minimizing the total routing cost and satisfying the entire demand of all customers, without the restriction of visiting them all, so that each customer not included in any route is covered. In the m-CTP, only a subset of customers is visited to fulfill the total demand, but a restriction is put on the length of each route and the number of vertices that it contains. This paper tackles a generalized variant of the m-CTP, called the multi-vehicle multi-covering Tour Problem (mm-CTP), where a vertex must be covered several times instead of once. We study a particular case of the mm-CTP considering only the restriction on the number of vertices in each route and relaxing the constraint on the length (mm-CTP-p). A hybrid metaheuristic is developet by combining Genetic Algorithm (GA), Variable Neighborhood Descent method (VND), and a General Variable Neighborhood Search algorithm (GVNS) to solve the problem. Computational experiments show that our approaches are competitive with the Evolutionary Local Search (ELS) and Genetic Algorithm (GA), the methods proposed in the literature.

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