Quantum-inspired genetic algorithms applied to ordering combinatorial optimization problems

2012 ◽  
Author(s):  
Luciano R. Silveira ◽  
Ricardo Tanscheit ◽  
Marley Vellasco
Keyword(s):  
Genetic Algorithms ◽  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Combinatorial Optimization Problems

scholarly journals Focusing on the Golden Ball Metaheuristic: An Extended Study on a Wider Set of Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
E. Osaba ◽  
F. Diaz ◽  
R. Carballedo ◽  
E. Onieva ◽  
A. Perallos
Keyword(s):  
Genetic Algorithms ◽  
Combinatorial Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problems ◽  
Traveling Salesman ◽  
Routing Problem ◽  
Combinatorial Optimization Problems ◽  
Golden Ball ◽  
The One

Nowadays, the development of new metaheuristics for solving optimization problems is a topic of interest in the scientific community. In the literature, a large number of techniques of this kind can be found. Anyway, there are many recently proposed techniques, such as the artificial bee colony and imperialist competitive algorithm. This paper is focused on one recently published technique, the one called Golden Ball (GB). The GB is a multiple-population metaheuristic based on soccer concepts. Although it was designed to solve combinatorial optimization problems, until now, it has only been tested with two simple routing problems: the traveling salesman problem and the capacitated vehicle routing problem. In this paper, the GB is applied to four different combinatorial optimization problems. Two of them are routing problems, which are more complex than the previously used ones: the asymmetric traveling salesman problem and the vehicle routing problem with backhauls. Additionally, one constraint satisfaction problem (the n-queen problem) and one combinatorial design problem (the one-dimensional bin packing problem) have also been used. The outcomes obtained by GB are compared with the ones got by two different genetic algorithms and two distributed genetic algorithms. Additionally, two statistical tests are conducted to compare these results.

A GA-BASED APPROACH TO OPTIMIZING COMBINATIONAL QUERIES IN SCDL

2008 ◽  
Vol 02 (02) ◽  
pp. 273-289 ◽  
Author(s):  
WEIQIN YING ◽  
YUANXIANG LI ◽  
PHILLIP C.-Y. SHEU
Keyword(s):  
Genetic Algorithms ◽  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Description Language ◽  
Combinatorial Optimization Problems ◽  
One To One

The semantic capability description language (SCDL) allows users to describe combinatorial optimization problems declaratively. We employ genetic algorithms (GA) and penalty techniques to process unconstrained SCDL queries and singly constrained SCDL queries, and determine a "one-to-one" mapping between queries and algorithms. Based on such, we develop a GA-based "one-to-many" mapping to process and integrate multi-constrained SCDL queries.

scholarly journals Statistical Design of Genetic Algorithms for Combinatorial Optimization Problems

2011 ◽  
Vol 2011 ◽  
pp. 1-17 ◽  
Author(s):  
Moslem Shahsavar ◽  
Amir Abbas Najafi ◽  
Seyed Taghi Akhavan Niaki
Keyword(s):  
Genetic Algorithms ◽  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Statistical Design ◽  
Fine Tuning ◽  
Combinatorial Optimization Problems ◽  
Crossover Probability ◽  
Project Scheduling Problem ◽  
Np Complete ◽  
Parameter Values

Many genetic algorithms (GA) have been applied to solve different NP-complete combinatorial optimization problems so far. The striking point of using GA refers to selecting a combination of appropriate patterns in crossover, mutation, and and so forth and fine tuning of some parameters such as crossover probability, mutation probability, and and so forth. One way to design a robust GA is to select an optimal pattern and then to search for its parameter values using a tuning procedure. This paper addresses a methodology to both optimal pattern selection and the tuning phases by taking advantage of design of experiments and response surface methodology. To show the performances of the proposed procedure and demonstrate its applications, it is employed to design a robust GA to solve a project scheduling problem. Through the statistical comparison analyses between the performances of the proposed method and an existing GA, the effectiveness of the methodology is shown.

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