scholarly journals A Hybrid of Differential Evolution and Genetic Algorithm for the Multiple Geographical Feature Label Placement Problem

2019 ◽  
Vol 8 (5) ◽  
pp. 237 ◽  
Author(s):  
Fuyu Lu ◽  
Jiqiu Deng ◽  
Shiyu Li ◽  
Hao Deng
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Difficult Problem ◽  
Placement Problem ◽  
Label Placement ◽  
Point Line ◽  
Discrete Differential Evolution ◽  
Quality Map ◽  
Geographical Feature

Label placement is a difficult problem in automated map production. Many methods have been proposed to automatically place labels for various types of maps. While the methods are designed to automatically and effectively generate labels for the point, line and area features, less attention has been paid to the problem of jointly labeling all the different types of geographical features. In this paper, we refer to the labeling of all the graphic features as the multiple geographical feature label placement (MGFLP) problem. In the MGFLP problem, the overlapping and occlusion among labels and corresponding features produces poorly arranged labels, and results in a low-quality map. To solve the problem, a hybrid algorithm combining discrete differential evolution and the genetic algorithm (DDEGA) is proposed to search for an optimized placement that resolves the MGFLP problem. The quality of the proposed solution was evaluated using a weighted metric regarding a number of cartographical rules. Experiments were carried out to validate the performance of the proposed method in a set of cartographic tasks. The resulting label placement demonstrates the feasibility and the effectiveness of our method.

A Novel Discrete Differential Evolution Algorithm for SVRPSPD

2013 ◽  
Vol 303-306 ◽  
pp. 2227-2230 ◽  
Author(s):  
Ling Juan Hou ◽  
Zhi Jiang Hou
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Vehicle Routing ◽  
Differential Evolution Algorithm ◽  
Computational Simulations ◽  
Special Design ◽  
Stochastic Vehicle Routing ◽  
Evolution Algorithm ◽  
Discrete Differential Evolution ◽  
Discrete Domain

Aiming at the stochastic vehicle routing problems with simultaneous pickups and deliveries, a novel discrete differential evolution algorithm is proposed for routes optimization. The algorithm can directly be used for the discrete domain by special design. Computational simulations and comparisons based on a medium-sized problem of SVRPSPD is provided. Results demonstrate that the proposed algorithm obtains better results than the basic differential evolution algorithm and the existing genetic algorithm.

Research on Optimal Configuration of the Filter in Distribution Network with Distributed Power

2014 ◽  
Vol 551 ◽  
pp. 589-593
Author(s):  
Ya Di Tang ◽  
Yong Hai Xu
Keyword(s):  
Genetic Algorithm ◽  
Energy Loss ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Optimal Configuration ◽  
Nonlinear Loads ◽  
Distributed Power ◽  
Evolution Algorithm ◽  
Harmonic Problem

As there are a lot of inverters existed in distributed power coupled with nonlinear loads, the quality of energy power has been greatly affected, especially the harmonic problem. This paper presents an optimal configuration method of the filter based on the combination of genetic algorithm and differential evolution algorithm. The proposed method takes filter investment and energy loss cost as objective function. Then, it combines the advantages of both algorithms for optimizing allocation, numbers and parameters of active filter (APF) and passive filter (PF). Simulations are provided showing the validity of the proposed method.

scholarly journals A Novel Parallel Algorithm with Map Segmentation for Multiple Geographical Feature Label Placement Problem

2021 ◽  
Vol 10 (12) ◽  
pp. 826
Author(s):  
Mohammad Naser Lessani ◽  
Jiqiu Deng ◽  
Zhiyong Guo
Keyword(s):  
Parallel Algorithm ◽  
Large Scale ◽  
Computational Time ◽  
Processing Unit ◽  
Placement Problem ◽  
Central Processing ◽  
Label Placement ◽  
Map Segmentation ◽  
Real World Datasets ◽  
Geographical Feature

Multiple geographical feature label placement (MGFLP) is an NP-hard problem that can negatively influence label position accuracy and the computational time of the algorithm. The complexity of such a problem is compounded as the number of features for labeling increases, causing the execution time of the algorithms to grow exponentially. Additionally, in large-scale solutions, the algorithm possibly gets trapped in local minima, which imposes significant challenges in automatic label placement. To address the mentioned challenges, this paper proposes a novel parallel algorithm with the concept of map segmentation which decomposes the problem of multiple geographical feature label placement (MGFLP) to achieve a more intuitive solution. Parallel computing is then utilized to handle each decomposed problem simultaneously on a separate central processing unit (CPU) to speed up the process of label placement. The optimization component of the proposed algorithm is designed based on the hybrid of discrete differential evolution and genetic algorithms. Our results based on real-world datasets confirm the usability and scalability of the algorithm and illustrate its excellent performance. Moreover, the algorithm gained superlinear speedup compared to the previous studies that applied this hybrid algorithm.

Urban stormwater: the analysis of the failure of the alternative techniques and the management of quality

1995 ◽  
Vol 32 (1) ◽  
pp. 33-39
Author(s):  
E. Alfakih ◽  
S. Barraud ◽  
Y. Azzout ◽  
B. Chocat
Keyword(s):  
Stormwater Management ◽  
Difficult Problem ◽  
Management Approach ◽  
Alternative Technique ◽  
Urban Stormwater ◽  
Urban Stormwater Management ◽  
Porous Pavement ◽  
Alternative Techniques ◽  
Design Construction

The implementation of alternative techniques in urban stormwater management is a difficult problem in terms of choice, design, construction, and operating. We applied a quality management approach to try and have a better understanding of these techniques. The quality of an alternative technique in urban stormwater management is defined; the factors that lead to failures were identified and analysed. In order to reduce these factors, tools were developed, and measures that allow the achievement of the necessary standard of quality are suggested. In this article, all the illustrations refer to the porous pavement technique.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

scholarly journals Genetic Algorithm for the Retailers’ Shelf Space Allocation Profit Maximization Problem

2021 ◽  
Vol 11 (14) ◽  
pp. 6401
Author(s):  
Kateryna Czerniachowska ◽  
Karina Sachpazidu-Wójcicka ◽  
Piotr Sulikowski ◽  
Marcin Hernes ◽  
Artur Rot
Keyword(s):  
Genetic Algorithm ◽  
Profit Maximization ◽  
Allocation Rule ◽  
Maximization Problem ◽  
Shelf Space ◽  
Allocation Model ◽  
Space Allocation ◽  
Shelf Space Allocation ◽  
To Receive

This paper discusses the problem of retailers’ profit maximization regarding displaying products on the planogram shelves, which may have different dimensions in each store but allocate the same product sets. We develop a mathematical model and a genetic algorithm for solving the shelf space allocation problem with the criteria of retailers’ profit maximization. The implemented program executes in a reasonable time. The quality of the genetic algorithm has been evaluated using the CPLEX solver. We determine four groups of constraints for the products that should be allocated on a shelf: shelf constraints, shelf type constraints, product constraints, and virtual segment constraints. The validity of the developed genetic algorithm has been checked on 25 retailing test cases. Computational results prove that the proposed approach allows for obtaining efficient results in short running time, and the developed complex shelf space allocation model, which considers multiple attributes of a shelf, segment, and product, as well as product capping and nesting allocation rule, is of high practical relevance. The proposed approach allows retailers to receive higher store profits with regard to the actual merchandising rules.

Multi-objective optimization of injection-molded plastic parts using entropy weight, random forest, and genetic algorithm methods

2020 ◽  
Vol 40 (4) ◽  
pp. 360-371
Author(s):  
Yanli Cao ◽  
Xiying Fan ◽  
Yonghuan Guo ◽  
Sai Li ◽  
Haiyue Huang
Keyword(s):  
Genetic Algorithm ◽  
Random Forest ◽  
Regression Model ◽  
Process Parameters ◽  
Multi Objective Optimization ◽  
Entropy Weight ◽  
Multi Objective ◽  
Injection Molded ◽  
Plastic Parts

AbstractThe qualities of injection-molded parts are affected by process parameters. Warpage and volume shrinkage are two typical defects. Moreover, insufficient or excessively large clamping force also affects the quality of parts and the cost of the process. An experiment based on the orthogonal design was conducted to minimize the above defects. Moldflow software was used to simulate the injection process of each experiment. The entropy weight was used to determine the weight of each index, the comprehensive evaluation value was calculated, and multi-objective optimization was transformed into single-objective optimization. A regression model was established by the random forest (RF) algorithm. To further illustrate the reliability and accuracy of the model, back-propagation neural network and kriging models were taken as comparative algorithms. The results showed that the error of RF was the smallest and its performance was the best. Finally, genetic algorithm was used to search for the minimum of the regression model established by RF. The optimal parameters were found to improve the quality of plastic parts and reduce the energy consumption. The plastic parts manufactured by the optimal process parameters showed good quality and met the requirements of production.

Sign in / Sign up

Export Citation Format

Share Document