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.

Based on Genetic Algorithm Research of Stocker Picking Shortest Path

2012 ◽  
Vol 155-156 ◽  
pp. 186-190
Author(s):  
Fu Cai Wan ◽  
Duo Chen ◽  
Yong Qiang Wu
Keyword(s):  
Genetic Algorithm ◽  
Shortest Path ◽  
Optimization Problem ◽  
Traveling Salesman ◽  
Path Optimization ◽  
Improved Genetic Algorithm ◽  
Operating Process ◽  
Path Distance ◽  
Simulation Results ◽  
The Mathematical Model

This paper analyzes characteristics of automated warehouse stocker picking operating process. Path optimization problem is considered as traveling salesman problem. The coordinates of picking points by calculating determine a stocker running route. The mathematical model of a path distance is built. And using the improved genetic algorithm solves the above problem. Finally, M-file program of stocker running path optimization is written and run in MATLAB. The simulation results that, in solving stocker path optimization problem, it can search for a shortest path by genetic algorithm. Thereby enhance the efficiency of automated warehouse system, increase greater benefits of the enterprise.

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 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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