A Satisfaction Differential Evolution Algorithm for Detailed Machine Layout in Assembly Plant

2014 ◽  
Vol 926-930 ◽  
pp. 3346-3349
Author(s):  
Wei Zeng ◽  
Cheng Long Liu ◽  
Xiao Jun Zheng
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Optimization Theory ◽  
Optimal Solution ◽  
Assembly Plant ◽  
Machine Layout ◽  
Practical Engineering ◽  
Evolution Algorithm ◽  
Evolution Algorithms ◽  
Differential Evolution Algorithms

We herein present a satisfaction differential evolution algorithm to deal with machine layout in assembly plant. This paper firstly provide the assumption for detailed machine layout problem, build a mathematical model of detailed machine layout, and replace the optimal solution with satisfactory solution, combine satisfactory optimization theory with differential evolution algorithms, employ satisfaction function as the objective function of differential evolution algorithm. The experimental results verified our methods with practical engineering examples.

Research and Improvement on K-Means Clustering Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 3231-3235
Author(s):  
Xue Mei Wang ◽  
Jin Bo Wang
Keyword(s):  
Differential Evolution ◽  
Clustering Algorithm ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Clustering Quality ◽  
Local Optimal Solution ◽  
Evolution Algorithm ◽  
Clustering Center ◽  
Searching Ability ◽  
Improved Differential Evolution Algorithm

According to the defects of classical k-means clustering algorithm such as sensitive to the initial clustering center selection, the poor global search ability, falling into the local optimal solution. A differential evolution algorithm which was a kind of a heuristic global optimization algorithm based on population was introduced in this article, then put forward an improved differential evolution algorithm combined with k-means clustering algorithm at the same time. The experiments showed that the method has solved initial centers optimization problem of k-means clustering algorithm well, had a better searching ability,and more effectively improved clustering quality and convergence speed.

scholarly journals Multiscale Cooperative Differential Evolution Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Yongzhao Du ◽  
Yuling Fan ◽  
Xiaofang Liu ◽  
Yanmin Luo ◽  
Jianeng Tang ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Early Stage ◽  
Differential Evolution Algorithm ◽  
Search Range ◽  
Adaptive Parameter ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Evolution Algorithms ◽  
The Individual

A multiscale cooperative differential evolution algorithm is proposed to solve the problems of narrow search range at the early stage and slow convergence at the later stage in the performance of the traditional differential evolution algorithms. Firstly, the population structure of multipopulation mechanism is adopted so that each subpopulation is combined with a corresponding mutation strategy to ensure the individual diversity during evolution. Then, the covariance learning among populations is developed to establish a suitable rotating coordinate system for cross operation. Meanwhile, an adaptive parameter adjustment strategy is introduced to balance the population survey and convergence. Finally, the proposed algorithm is tested on the CEC 2005 benchmark function and compared with other state-of-the-art evolutionary algorithms. The experiment results showed that the proposed algorithm has better performance in solving global optimization problems than other compared algorithms.

Research on the Optimal Design of Soccer Robot Based on the Mechanical Analysis

2013 ◽  
Vol 380-384 ◽  
pp. 3854-3857
Author(s):  
Jian Wen Han ◽  
Lei Hong
Keyword(s):  
Differential Evolution ◽  
Clustering Algorithm ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Mechanical Analysis ◽  
Clustering Quality ◽  
Local Optimal Solution ◽  
Evolution Algorithm ◽  
Searching Ability ◽  
Improved Differential Evolution Algorithm

According to the defects of classical k-means clustering algorithm such as sensitive to the initial clustering center selection, the poor global search ability, falling into the local optimal solution. A differential evolution algorithm which was a kind of a heuristic global optimization algorithm based on population was introduced in this article, then put forward an improved differential evolution algorithm combined with k-means clustering algorithm at the same time. The experiments showed that the method has solved initial centers optimization problem of k-means clustering algorithm well, had a better searching ability,and more effectively improved clustering quality and convergence speed

scholarly journals ON SEARCH CAPABILITIES OF THE DIFFERENTIAL EVOLUTION ALGORITHM

2001 ◽  
Vol 1 ◽  
pp. 288
Author(s):  
Anatoly Sukov
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Search Technique ◽  
Technical Character ◽  
Evolution Algorithm

This paper examines the algorithm of differential evolution that has appeared rather recently. This algorithm ascribed by its developers to a class of evolutionary algorithms is a comparatively non-complicated technique o f solution search as applied to multiparameter optimisation tasks. Nevertheless, there are two essential factors preventing from wide application of the considered solution search technique. One of them lies in the principle of coding vectors (variables) that constitute a population the algorithm works with. The second problem is of pure technical character: in the process of search, stagnation occurs, or impossibility to find new solutions, when there is no optimal solution in the population and the vectors available are not heterogeneous. Besides studying search possibilities (limitations) of the differential evolution, some ways to cope with the problem of stagnation so-as to raise the performance of the algorithm are also suggested.

scholarly journals A Novel Enhanced Differential Evolution Algorithm for Outbound Logistics of the Poultry Industry in Thailand

2022 ◽  
Vol 8 (1) ◽  
pp. 15
Author(s):  
Karn Moonsri ◽  
Kanchana Sethanan ◽  
Kongkidakhon Worasan
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Transportation Cost ◽  
Mixed Integer ◽  
Computational Time ◽  
Poultry Industry ◽  
Routing Problem ◽  
De Algorithm ◽  
Evolution Algorithm

Outbound logistics is a crucial field of logistics management. This study considers a planning distribution for the poultry industry in Thailand. The goal of the study is to minimize the transportation cost for the multi-depot vehicle-routing problem (MDVRP). A novel enhanced differential evolution algorithm (RI-DE) is developed based on a new re-initialization mutation formula and a local search function. A mixed-integer programming formulation is presented in order to measure the performance of a heuristic with GA, PSO, and DE for small-sized instances. For large-sized instances, RI-DE is compared to the traditional DE algorithm for solving the MDVRP using published benchmark instances. The results demonstrate that RI-DE obtained a near-optimal solution of 99.03% and outperformed the traditional DE algorithm with a 2.53% relative improvement, not only in terms of solution performance, but also in terms of computational time.

Stratified Sampling Differential Evolution Algorithm for Global Optimization Problem

2012 ◽  
Vol 452-453 ◽  
pp. 1491-1495
Author(s):  
Shu Hua Wen ◽  
Qing Bo Lu ◽  
Xue Liang Zhang
Keyword(s):  
Differential Evolution ◽  
Optimization Problem ◽  
Sampling Method ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Stratified Sampling ◽  
Local Optima ◽  
De Algorithm ◽  
Local Optimal Solution ◽  
Evolution Algorithm

Differential Evolution (DE) is one kind of evolution algorithm, which based on difference of individuals. DE has exhibited good performance on optimization problem. However, when a local optimal solution is reached with classical Differential Evolution, all individuals in the population gather around it, and escaping from these local optima becomes difficult. To avoid premature convergence of DE, we present in this paper a novel variant of DE algorithm, called SSDE, which uses the stratified sampling method to replace the random sampling method. The proposed SSDE algorithm is compared with some variant DE. The numerical results show that our approach is robust, competitive and fast.

scholarly journals Use Improved Differential Evolution Algorithms to Handle the Inverse Kinetics Problem for Robots with Residual Degrees of Freedom

2021 ◽  
Author(s):  
Trung Nguyen ◽  
Tam Bui
Keyword(s):  
Differential Evolution ◽  
Execution Time ◽  
Degrees Of Freedom ◽  
Single Point ◽  
Differential Evolution Algorithm ◽  
Adaptive Strategy ◽  
De Algorithm ◽  
Evolution Algorithms ◽  
Differential Evolution Algorithms

In this study, the Self-adaptive strategy algorithm for controlling parameters in Differential Evolution algorithm (ISADE) improved from the Differential Evolution (DE) algorithm, as well as the upgraded version of the algorithms has been applied to solve the Inverse Kinetics (IK) problem for the redundant robot with 7 Degree of Freedom (DoF). The results were compared with 4 other algorithms of DE and Particle Swarm Optimization (PSO) as well as Pro-DE and Pro-PSO algorithms. These algorithms are tested in three different Scenarios for the motion trajectory of the end effector of in the workspace. In the first scenario, the IK results for a single point were obtained. 100 points randomly generated in the robot’s workspace was input parameters for Scenario 2, while Scenario 3 used 100 points located on a spline in the robot workspace. The algorithms were compared with each other based on the following criteria: execution time, endpoint distance error, number of generations required and especially quality of the joints’ variable found. The comparison results showed 2 main points: firstly, the ISADE algorithm gave much better results than the other DE and PSO algorithms based on the criteria of execution time, endpoint accuracy and generation number required. The second point is that when applying Pro-ISADE, Pro-DE and Pro-PSO algorithms, in addition to the ability to significantly improve the above parameters compared to the ISADE, DE and PSO algorithms, it also ensures the quality of solved joints’ values.

An Intelligent Garbage Can Decision-Making Model Evolution Algorithm on Optimal Design of Fuzzy Controller of Intelligent Lighting Systems

2013 ◽  
Vol 284-287 ◽  
pp. 2215-2219
Author(s):  
Ching Hai Lin ◽  
Hsin Chuan Kuo ◽  
Chao Tsung Lee
Keyword(s):  
Differential Evolution ◽  
Energy Savings ◽  
Fuzzy Controller ◽  
Differential Evolution Algorithm ◽  
Search Performance ◽  
Lighting System ◽  
Population System ◽  
Evolution Algorithm ◽  
Evolution Algorithms ◽  
Lighting Systems

In this paper, an intelligent garbage can model-based differential evolution algorithm (IGCMDE) is proposed to simulate human social organization by its population system, based on the differential evolution algorithms (DEs) and the logical framework of the garbage can decision model with group meeting. When faced with issues such as unclear goals and technologies, participators turnover, etc., representatives of all participating parties will communicate, argue, compromise and adapt with each other, in order to find a solution to the problems. Group meetings are conducted to choose the best solution in a more objective, reasonable and efficient way. At last, we used IGCMDE to optimize the fuzzy controller with fuzzy logic control theory. We present a method for daylight blending control with two novel contributions: 1) smart luminance sensing; 2) daylight luminance control. The proposed method was implemented as an intelligent lighting system in a parking tower environment. The result demonstrated that IGCMDE possesses an excellent search performance and the intelligent lighting system showed significant energy savings.

scholarly journals Hybrid Taguchi-Differential Evolution Algorithm for Parameter Estimation of Differential Equation Models with Application to HIV Dynamics

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Wen-Hsien Ho ◽  
Agnes Lai-Fong Chan
Keyword(s):  
Parameter Estimation ◽  
Numerical Methods ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Dynamical Model ◽  
Hiv Dynamics ◽  
Differential Equation Models ◽  
Immunodeficiency Virus ◽  
Evolution Algorithm

This work emphasizes solving the problem of parameter estimation for a human immunodeficiency virus (HIV) dynamical model by using an improved differential evolution, which is called the hybrid Taguchi-differential evolution (HTDE). The HTDE, used to estimate parameters of an HIV dynamical model, can provide robust optimal solutions. In this work, the HTDE approach is effectively applied to solve the problem of parameter estimation for an HIV dynamical model and is also compared with the traditional differential evolution (DE) approach and the numerical methods presented in the literature. An illustrative example shows that the proposed HTDE gives an effective and robust way for obtaining optimal solution, and can get better results than the traditional DE approach and the numerical methods presented in the literature for an HIV dynamical model.

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