scholarly journals A NOVEL GREEDY GENETIC ALGORITHM TO SOLVE COMBINATORIAL OPTIMIZATION PROBLEM

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 117-120
Author(s):  
M. A. Basmassi ◽  
L. Benameur ◽  
J. A. Chentoufi
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
Sequential Algorithm ◽  
Solution Quality ◽  
Graph Coloring Problem ◽  
Benchmark Instances ◽  
Crossover And Mutation

Abstract. In this paper, a modified genetic algorithm based on greedy sequential algorithm is presented to solve combinatorial optimization problem. The algorithm proposed here is a hybrid of heuristic and computational intelligence algorithm where greedy sequential algorithm is used as operator inside genetic algorithm like crossover and mutation. The greedy sequential function is used to correct non realizable solution after crossover and mutation which contribute to increase the rate of convergence and upgrade the population by improving the quality of chromosomes toward the chromatic number. Experiments on a set of 6 well-known DIMACS benchmark instances of graph coloring problem to test this approach show that the proposed algorithm achieves competitive results in comparison with three states of art algorithms in terms of either success rate and solution quality.

scholarly journals Solving the Set Packing Problem via a Maximum Weighted Independent Set Heuristic

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Ruizhi Li ◽  
Yupan Wang ◽  
Shuli Hu ◽  
Jianhua Jiang ◽  
Dantong Ouyang ◽  
...  
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
State Of The Art ◽  
Independent Set ◽  
Packing Problem ◽  
Combinatorial Optimization Problem ◽  
Set Packing ◽  
Benchmark Instances ◽  
Set Packing Problem

The set packing problem (SPP) is a significant NP-hard combinatorial optimization problem with extensive applications. In this paper, we encode the set packing problem as the maximum weighted independent set (MWIS) problem and solve the encoded problem with an efficient algorithm designed to the MWIS problem. We compare the independent set-based method with the state-of-the-art algorithms for the set packing problem on the 64 standard benchmark instances. The experimental results show that the independent set-based method is superior to the existing algorithms in terms of the quality of the solutions and running time obtained the solutions.

scholarly journals An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Yong Deng ◽  
Yang Liu ◽  
Deyun Zhou
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
Traveling Salesman ◽  
Average Error ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Population Strategy ◽  
Series Of Experiments

A new initial population strategy has been developed to improve the genetic algorithm for solving the well-known combinatorial optimization problem, traveling salesman problem. Based on thek-means algorithm, we propose a strategy to restructure the traveling route by reconnecting each cluster. The clusters, which randomly disconnect a link to connect its neighbors, have been ranked in advance according to the distance among cluster centers, so that the initial population can be composed of the random traveling routes. This process isk-means initial population strategy. To test the performance of our strategy, a series of experiments on 14 different TSP examples selected from TSPLIB have been carried out. The results show that KIP can decrease best error value of random initial population strategy and greedy initial population strategy with the ratio of approximately between 29.15% and 37.87%, average error value between 25.16% and 34.39% in the same running time.

Research on the Application of College Automated Course Scheduling System Based on Genetic Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 1782-1785
Author(s):  
Xiu Ying Li ◽  
Dong Ju Du
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Teaching Quality ◽  
Combinatorial Optimization Problem ◽  
Scheduling System ◽  
Course Scheduling ◽  
Strong Ability

A reasonable curriculum contributes to the improvement of the training and teaching quality of college students. Using computer which is speed and strong ability to arrange curriculum automatically is imperative. Automatically curriculum arrangement is a constrained, multi-objective and intricate combinatorial optimization problem. Based on genetic algorithm of population search, it is suitable to process complex and nonlinear optimization problems which it difficult to solve for traditional search methods. In this paper solves complex automated course scheduling using genetic algorithms.

A Review and Comparison of Genetic Algorithms for the 0-1 Multidimensional Knapsack Problem

2015 ◽  
Vol 6 (2) ◽  
pp. 21-31 ◽  
Author(s):  
Bernhard Lienland ◽  
Li Zeng
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Combinatorial Optimization ◽  
Knapsack Problem ◽  
Optimization Problem ◽  
Real Life ◽  
Combinatorial Optimization Problem ◽  
Multidimensional Knapsack Problem ◽  
Multidimensional Knapsack ◽  
Problem Types

The 0-1 multidimensional knapsack problem (MKP) is a well-known combinatorial optimization problem with several real-life applications, for example, in project selection. Genetic algorithms (GA) are effective heuristics for solving the 0-1 MKP. Multiple individual GAs with specific characteristics have been proposed in literature. However, so far, these approaches have only been partially compared in multiple studies with unequal conditions. Therefore, to identify the “best” genetic algorithm, this article reviews and compares 11 existing GAs. The authors' tests provide detailed information on the GAs themselves as well as their performance. The authors validated fitness values and required computation times in varying problem types and environments. Results demonstrate the superiority of one GA.

scholarly journals Rectangular Bin-Packing Problem: a computational evaluation of 4 heuristics algorithms

2017 ◽  
Vol 1 (1) ◽  
pp. 35-49 ◽  
Author(s):  
Duarte Nuno Gonçalves Ferreira
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Bin Packing ◽  
Packing Problem ◽  
Combinatorial Optimization Problem ◽  
Two Dimensional ◽  
Computational Evaluation ◽  
Bin Packing Problem ◽  
Benchmark Instances ◽  
Minimum Number

The Rectangular Bin-packing Problem, also known as The Two-dimensional Bin-packing Problem (2DBPP), is a well-known combinatorial optimization problem which is the problem of orthogonally packing a given set of rectangles into a minimum number of two-dimensional rectangular bins. In this article we benchmark four heuristics: constructive, based on a First Fit Decreasing strategy, local search using a greedy packing First-Fit algorithm, Simulated Annealing with multiple cooling values and Genetic Algorithm. All implementations are written in Python, run using the Pypy environment and the new multiprocessing module. All implementations were tested using the Berkey and Wang and Martelo and Vigo Benchmark Instances.

scholarly journals Sustainability Assessment of Constructive Solutions for Urban Spain: A Multi-Objective Combinatorial Optimization Problem

2019 ◽  
Vol 11 (3) ◽  
pp. 839 ◽  
Author(s):  
Simón Martínez ◽  
Cristina González ◽  
Antonio Hospitaler ◽  
Vicente Albero
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Environmental Impacts ◽  
Co2 Emissions ◽  
Optimization Problem ◽  
Economic Cost ◽  
Sustainability Indicators ◽  
Combinatorial Optimization Problem ◽  
Nsga Ii ◽  
Multi Objective

Industrial areas are set up on plots of roads and associated infrastructure. These use materials and machinery that have environmental impacts, and thus require constructive solutions throughout their lifecycles. In turn, these solutions and their components cause environmental impacts that can be measured by sustainability indicators. The concept of sustainability is closely tied to sustainable development, which is defined as “development that meets the needs of the present, without compromising the ability of future generations to meet their own needs”. The large number of possible and available solutions means that identifying the best one for a given road section must employ a set of heuristic techniques, which conceptualize the issue as a combinatorial optimization problem that is purely discrete and non-differential. The system chosen can be based on a genetic algorithm method that differentiates individuals based on three sustainability indicators: CO2 emissions, embedded energy (also known as embodied energy, defined as the energy expended to manufacture a product), and economic cost. In this paper, we supplement traditional cost analyses using a three-objective multi-objective genetic algorithm that considers the aforementioned criteria, thus addressing sustainability in aggregate planning. The procedure is applied to three objective functions—CO2 emissions, economic cost and embedded energy—for each possible solution. We used the non-dominated sorting genetic algorithm (NSGA-II) to implement multi-objective optimization in MATLAB. Additional results for a random walk and multi-objective search algorithm are shown. This study involved 26 design variables, with different ranks of variation, and the application of the algorithm generates results for the defined Pareto fronts. Our method shows that the optimal approach effectively solves a real-world multi-objective project planning problem, as our solution is one of the Pareto-optimal solutions generated by the NSGA-II.

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