scholarly journals A Hybrid Framework Combining Genetic Algorithm with Iterated Local Search for the Dominating Tree Problem

Mathematics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 359 ◽  
Author(s):  
Shuli Hu ◽  
Huan Liu ◽  
Xiaoli Wu ◽  
Ruizhi Li ◽  
Junping Zhou ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Weighted Graph ◽  
Iterated Local Search ◽  
Edge Weight ◽  
Score Functions ◽  
Hybrid Framework ◽  
Dominating Tree ◽  
Main Components ◽  
Better Than

Given an undirected, connected and edge-weighted graph, the dominating tree problem consists of finding a tree with minimum total edge weight such that for each vertex is either in the tree or adjacent to a vertex in the tree. In this paper, we propose a hybrid framework combining genetic algorithm with iterated local search (GAITLS) for solving the dominating tree problem. The main components of our framework are as follows: (1) the score functions D s c o r e and W s c o r e applied in the initialization and local search phase; (2) the initialization procedure with restricted candidate list (RCL) by controlling the parameter to balance the greediness and randomness; (3) the iterated local search with three phases, which is used to intensify the individuals; (4) the mutation with high diversity proposed to perturb the population. The experimental results on the classical instances show that our method performs much better than the-state-of-art algorithms.

Application of Tabu-Genetic Algorithm in Reactive Power Optimization

2014 ◽  
Vol 1006-1007 ◽  
pp. 1021-1025
Author(s):  
Song Tao Zhang ◽  
Gong Bao Wang ◽  
Hui Bo Wang
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Local Search ◽  
Reactive Power ◽  
Power Optimization ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Reactive Power Optimization ◽  
Simple Genetic Algorithm ◽  
Better Than

By using tabu search algorithm which has strong local search ability as mutation operator of genetic algorithm, the tabu-genetic algorithm is designed for reactive power optimization in this paper, the strong global search ability of genetic algorithm and strong local search ability of tabu search algorithm is combined, the disadvantage of weak local search ability of genetic algorithm is conquered. Otherwise, the over limit of population is recorded and filtered, to ensure the final individual is under limit and effective. The tabu-genetic algorithm and simple genetic algorithm are used for simulation of IEEE 14-bus system 500 times, the results indicate that the performance of the tabu-genetic algorithm is much better than the simple genetic algorithm, its local search ability is improved obviously, and the active power loss is reduced more.

scholarly journals A Novel Hybrid Algorithm for Minimum Total Dominating Set Problem

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 222 ◽  
Author(s):  
Fuyu Yuan ◽  
Chenxi Li ◽  
Xin Gao ◽  
Minghao Yin ◽  
Yiyuan Wang
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Evolutionary Algorithm ◽  
Hybrid Algorithm ◽  
Dominating Set ◽  
Experimental Results ◽  
Total Dominating Set ◽  
Dominating Set Problem ◽  
Algorithm Search ◽  
Better Than

The minimum total dominating set (MTDS) problem is a variant of the classical dominating set problem. In this paper, we propose a hybrid evolutionary algorithm, which combines local search and genetic algorithm to solve MTDS. Firstly, a novel scoring heuristic is implemented to increase the searching effectiveness and thus get better solutions. Specially, a population including several initial solutions is created first to make the algorithm search more regions and then the local search phase further improves the initial solutions by swapping vertices effectively. Secondly, the repair-based crossover operation creates new solutions to make the algorithm search more feasible regions. Experiments on the classical benchmark DIMACS are carried out to test the performance of the proposed algorithm, and the experimental results show that our algorithm performs much better than its competitor on all instances.

A HYBRID GENETIC ALGORITHM FOR THE EARLY/TARDY SCHEDULING PROBLEM

2006 ◽  
Vol 23 (03) ◽  
pp. 393-405 ◽  
Author(s):  
JORGE M. S. VALENTE ◽  
JOSÉ FERNANDO GONÇALVES ◽  
RUI A. F. S. ALVES
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Hybrid Genetic Algorithm ◽  
Scheduling Problem ◽  
Solution Quality ◽  
New Approach ◽  
Random Keys ◽  
Chromosome Representation ◽  
Local Search Procedure ◽  
Better Than

In this paper, we present a hybrid genetic algorithm for a version of the early/tardy scheduling problem in which no unforced idle time may be inserted in a sequence. The chromosome representation of the problem is based on random keys. The genetic algorithm is used to establish the order in which the jobs are initially scheduled, and a local search procedure is subsequently applied to detect possible improvements. The approach is tested on a set of randomly generated problems and compared with existing efficient heuristic procedures based on dispatch rules and local search. The computational results show that this new approach, although requiring slightly longer computational times, is better than the previous algorithms in terms of solution quality.

Genetic algorithm with iterated local search for solving a location-routing problem

2012 ◽  
Vol 39 (3) ◽  
pp. 2865-2871 ◽  
Author(s):  
Houda Derbel ◽  
Bassem Jarboui ◽  
Saïd Hanafi ◽  
Habib Chabchoub
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Iterated Local Search ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing

scholarly journals Multi-start iterated local search, exact and matheuristic approaches for minimum capacitated dominating set problem

2021 ◽  
Vol 108 ◽  
pp. 107437
Author(s):  
Mallikarjun Rao Nakkala ◽  
Alok Singh ◽  
André Rossi
Keyword(s):  
Local Search ◽  
Dominating Set ◽  
Iterated Local Search ◽  
Dominating Set Problem

scholarly journals Combining Heuristics with Simulation and Fuzzy Logic to Solve a Flexible-Size Location Routing Problem under Uncertainty

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 45
Author(s):  
Rafael D. Tordecilla ◽  
Pedro J. Copado-Méndez ◽  
Javier Panadero ◽  
Carlos L. Quintero-Araujo ◽  
Jairo R. Montoya-Torres ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Local Search ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Iterated Local Search ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Routing ◽  
Fuzzy Pattern ◽  
Reasonable Computing

The location routing problem integrates both a facility location and a vehicle routing problem. Each of these problems are NP-hard in nature, which justifies the use of heuristic-based algorithms when dealing with large-scale instances that need to be solved in reasonable computing times. This paper discusses a realistic variant of the problem that considers facilities of different sizes and two types of uncertainty conditions. In particular, we assume that some customers’ demands are stochastic, while others follow a fuzzy pattern. An iterated local search metaheuristic is integrated with simulation and fuzzy logic to solve the aforementioned problem, and a series of computational experiments are run to illustrate the potential of the proposed algorithm.

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