scholarly journals ALGORITMA DIFFERENTIAL EVOLUTION UNTUK PENJADWALAN FLOW SHOP BANYAK MESIN DENGAN MULTI OBYEKTIF

2012 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Stefanus Eko Wiratno ◽  
Nurdiansyah Rudi ◽  
Budi Santosa
Keyword(s):  
Differential Evolution ◽  
Flow Shop ◽  
Computation Time ◽  
Parameter Determination ◽  
Adaptive Parameter ◽  
De Algorithm ◽  
Adaptive Parameters ◽  
Local Search Strategy ◽  
Reduce Computation Time

This research focuses on the development of Differential Evolution(DE) algorithmto solve m-machine flow shopscheduling problems with respect to both makespan and total flow time. Development of DE algorithm is done by modifyingthe adaptive parameter determination procedure in order to change the value of adaptive parameters in each generation,adding local search strategy to the algorithm in order to improve the quality of the resulting solutions, as ewell as modifyingthe crossover in order to reduce computation time. The result indicates that the proposed DE algorithm has proven to bebetter than the original DE algorithm, Genetic Algorithm (GA), and for certain cases it also out performs Multi-ObjectiveAnt Colony System Algorithm (MOCSA).

Differential evolution tuned fuzzy supervisor adapted extended Kalman filtering for SLAM problems in mobile robots

Robotica ◽  
2009 ◽  
Vol 27 (3) ◽  
pp. 411-423 ◽  
Author(s):  
Amitava Chatterjee
Keyword(s):  
Differential Evolution ◽  
Mobile Robots ◽  
Autonomous Vehicles ◽  
Extended Kalman Filtering ◽  
Extended Kalman Filters ◽  
De Algorithm ◽  
Localization And Mapping ◽  
Recent Method ◽  
Fuzzy Supervisor

SUMMARYThe present paper proposes a successful application of differential evolution (DE) optimized fuzzy logic supervisors (FLS) to improve the quality of solutions that extended Kalman filters (EKFs) can offer to solve simultaneous localization and mapping (SLAM) problems for mobile robots and autonomous vehicles. The utility of the proposed system can be readily appreciated in those situations where an incorrect knowledge of Q and R matrices of EKF can significantly degrade the SLAM performance. A fuzzy supervisor has been implemented to adapt the R matrix of the EKF online, in order to improve its performance. The free parameters of the fuzzy supervisor are suitably optimized by employing the DE algorithm, a comparatively recent method, popularly employed now-a-days for high-dimensional parallel direct search problems. The utility of the proposed system is aptly demonstrated by solving the SLAM problem for a mobile robot with several landmarks and with wrong knowledge of sensor statistics. The system could successfully demonstrate enhanced performance in comparison with usual EKF-based solutions for identical environment situations.

scholarly journals Data Clustering based on Modified Differential Evolution and Quasi-Opposition-based Learning

2020 ◽  
Vol 13 (6) ◽  
pp. 168-178
Author(s):  
Pyae Cho ◽  
◽  
Thi Nyunt ◽  
Keyword(s):  
Differential Evolution ◽  
Heuristic Search ◽  
Clustering Algorithm ◽  
Population Based ◽  
Initial Population ◽  
Local Optima ◽  
Opposition Based Learning ◽  
De Algorithm ◽  
Mutation Strategy

Differential Evolution (DE) has become an advanced, robust, and proficient alternative technique for clustering on account of their population-based stochastic and heuristic search manners. Balancing better the exploitation and exploration power of the DE algorithm is important because this ability influences the performance of the algorithm. Besides, keeping superior solutions for the initial population raises the probability of finding better solutions and the rate of convergence. In this paper, an enhanced DE algorithm is introduced for clustering to offer better cluster solutions with faster convergence. The proposed algorithm performs a modified mutation strategy to improve the DE’s search behavior and exploits Quasi-Opposition-based Learning (QBL) to choose fitter initial solutions. This mutation strategy that uses the best solution as a target solution and applies three differentials contributes to avoiding local optima trap and slow convergence. The QBL based initialization method also contributes to increasing the quality of the clustering results and convergence rate. The experimental analysis was conducted on seven real datasets from the UCI repository to evaluate the performance of the proposed clustering algorithm. The obtained results showed that the proposed algorithm achieves more compact clusters and stable solutions than the competing conventional DE variants. Moreover, the performance of the proposed algorithm was compared with the existing state of the art clustering techniques based on DE. The corresponding results also pointed out that the proposed algorithm is comparable to other DE based clustering approaches in terms of the value of the objective functions. Therefore, the proposed algorithm can be regarded as an efficient clustering tool.

An Improved Differential Evolution and its Application in Function Optimization Problem

2011 ◽  
Vol 267 ◽  
pp. 632-634
Author(s):  
Jing Feng Yan ◽  
Chao Feng Guo
Keyword(s):  
Differential Evolution ◽  
Optimization Problem ◽  
Search Strategy ◽  
Optimization Problems ◽  
Function Optimization ◽  
Final Solution ◽  
De Algorithm ◽  
Ranking Strategy ◽  
The Stability

An Improved Differential evolution (IDE) is proposed in this paper. It has some new features: 1) using multi-parent search strategy and stochastic ranking strategy to maintain the diversity of the population; 2) a novel convex mutation to accelerate the convergence rate of the classical DE algorithm.; The algorithm of this paper is tested on 13 benchmark optimization problems with linear or/and nonlinear constraints and compared with other evolutionary algorithms. The experimental results demonstrate that the performance of IDE outperforms DE in terms of the quality of the final solution and the stability.

On the Parameter Determination of a Stress Relaxation Model Based on Creep Equations Using Differential Evolution Algorithm

2013 ◽  
Vol 441 ◽  
pp. 476-479
Author(s):  
Wei Wei Zhang ◽  
Hong Xu
Keyword(s):  
Stress Relaxation ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Parameter Determination ◽  
Model Parameters ◽  
Relaxation Model ◽  
Good Convergence ◽  
Model Based ◽  
De Algorithm ◽  
Creep And Stress Relaxation

A robust and efficient parameter identification method of the stress relaxation model based on Altenbach-Gorash-Naumenko creep equations is discussed. The differential evolution (DE) algorithm with a modified forward-Euler scheme is used in the identification procedure. Besides its good convergence properties and suitability for parallelization, initial guesses close to the solutions are not required for the DE algorithm. The parameter determination problem of the stress relaxation model is based on a very broad range specified for each parameter. The performance of the proposed DE algorithm is compared with a step-by-step model parameter determination technology and the genetic algorithm (GA). The model parameters of 12Cr-1Mo-1W-1/4V stainless steel bolting material at 550°C have been determined, and the creep and stress relaxation behaviors have been calculated. Results indicate that the optimum solutions can be obtained more easily by DE algorithm than others.

scholarly journals Effect of Strategy Adaptation on Differential Evolution in Presence and Absence of Parameter Adaptation: An Investigation

2018 ◽  
Vol 8 (3) ◽  
pp. 211-235 ◽  
Author(s):  
Deepak Dawar ◽  
Simone A. Ludwig
Keyword(s):  
Differential Evolution ◽  
Control Mechanisms ◽  
Control Parameters ◽  
Parameter Adaptation ◽  
Strategy Adaptation ◽  
Adaptive Parameter ◽  
De Algorithm ◽  
Mutation Strategy ◽  
Presence And Absence ◽  
The Impact

AbstractDifferential Evolution (DE) is a simple, yet highly competitive real parameter optimizer in the family of evolutionary algorithms. A significant contribution of its robust performance is attributed to its control parameters, and mutation strategy employed, proper settings of which, generally lead to good solutions. Finding the best parameters for a given problem through the trial and error method is time consuming, and sometimes impractical. This calls for the development of adaptive parameter control mechanisms. In this work, we investigate the impact and efficacy of adapting mutation strategies with or without adapting the control parameters, and report the plausibility of this scheme. Backed with empirical evidence from this and previous works, we first build a case for strategy adaptation in the presence as well as in the absence of parameter adaptation. Afterwards, we propose a new mutation strategy, and an adaptive variant SA-SHADE which is based on a recently proposed self-adaptive memory based variant of Differential evolution, SHADE. We report the performance of SA-SHADE on 28 benchmark functions of varying complexity, and compare it with the classic DE algorithm (DE/Rand/1/bin), and other state-of-the-art adaptive DE variants including CoDE, EPSDE, JADE, and SHADE itself. Our results show that adaptation of mutation strategy improves the performance of DE in both presence, and absence of control parameter adaptation, and should thus be employed frequently.

Parameter Identification of a Stress Relaxation Model Based on Differential Evolution Algorithm

2013 ◽  
Vol 842 ◽  
pp. 482-485
Author(s):  
Wei Wei Zhang ◽  
Hong Xu
Keyword(s):  
Stress Relaxation ◽  
Differential Evolution ◽  
Parameter Identification ◽  
Differential Evolution Algorithm ◽  
Parameter Determination ◽  
Model Parameters ◽  
Relaxation Model ◽  
Model Based ◽  
De Algorithm ◽  
Creep And Stress Relaxation

In order to determine the parameters of a stress relaxation model based on Altenbach-Gorash-Naumenko creep equations, an efficient parameter identification scheme is discussed. The differential evolution (DE) algorithm is used in the identification procedure with a modified forward-Euler scheme. The model parameters of 1Cr-0.5Mo-0.25V stainless steel bolting material at 500°C have been determined, and the creep and stress relaxation behaviors have been calculated. Comparing with a step-by-step model parameter determination technology and the genetic algorithm (GA), it shows that the DE algorithm has better convergence property and suitability for parallelization, and no need of initial guesses close to the solution. Results indicate that the optimum solutions can be obtained more easily by DE algorithm than others.

scholarly journals An Improved Differential Evolution Algorithm Based on Adaptive Parameter

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Zhehuang Huang ◽  
Yidong Chen
Keyword(s):  
Differential Evolution ◽  
Optimization Technique ◽  
Differential Evolution Algorithm ◽  
Function Optimization ◽  
Control Parameters ◽  
Adaptive Parameter ◽  
De Algorithm ◽  
Evolution Stage ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

The differential evolution (DE) algorithm is a heuristic global optimization technique based on population which is easy to understand, simple to implement, reliable, and fast. The evolutionary parameters directly influence the performance of differential evolution algorithm. The adjustment of control parameters is a global behavior and has no general research theory to control the parameters in the evolution process at present. In this paper, we propose an adaptive parameter adjustment method which can dynamically adjust control parameters according to the evolution stage. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and faster convergence speed.

scholarly journals Differential Evolution Algorithm for Multilevel Assignment Problem: A Case Study in Chicken Transportation

2018 ◽  
Vol 23 (4) ◽  
pp. 55 ◽  
Author(s):  
Sasitorn Kaewman ◽  
Tassin Srivarapongse ◽  
Chalermchat Theeraviriya ◽  
Ganokgarn Jirasirilerd
Keyword(s):  
Differential Evolution ◽  
Assignment Problem ◽  
Differential Evolution Algorithm ◽  
Computation Time ◽  
Search Performance ◽  
De Algorithm ◽  
Minimum Number ◽  
Average Computation Time ◽  
Two Stages

This study aims to solve the real-world multistage assignment problem. The proposed problem is composed of two stages of assignment: (1) different types of trucks are assigned to chicken farms to transport young chickens to egg farms, and (2) chicken farms are assigned to egg farms. Assigning different trucks to the egg farms and different egg farms to the chicken farms generates different costs and consumes different resources. The distance and the idle space in the truck have to be minimized, while constraints such as the minimum number of chickens needed for all egg farms and the longest time that chickens can be in the truck remain. This makes the problem a special case of the multistage assignment (S-MSA) problem. A mathematical model representing the problem was developed and solved to optimality using Lingo v.11 optimization software. Lingo v.11 can solve to optimality only small- and medium-sized test instances. To solve large-sized test instances, the differential evolution (DE) algorithm was designed. An excellent decoding method was developed to increase the search performance of DE. The proposed algorithm was tested with three randomly generated datasets (small, medium, and large test instances) and one real case study. Each dataset is composed of 12 problems, therefore we tested with 37 instances, including the case study. The results show that for small- and medium-sized test instances, DE has 0.03% and 0.05% higher cost than Lingo v.11. For large test instances, DE has 3.52% lower cost than Lingo v.11. Lingo v.11 uses an average computation time of 5.8, 103, and 4320 s for small, medium and large test instances, while DE uses 0.86, 1.68, and 8.79 s, which is, at most, 491 times less than Lingo v.11. Therefore, the proposed heuristics are an effective algorithm that can find a good solution while using less computation time.

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.

scholarly journals A Novel Differential Evolution Algorithm for TWET-NFSSP with SDSTs and RDs

2012 ◽  
Vol 6-7 ◽  
pp. 748-756
Author(s):  
Bin Qian ◽  
Hua Bing Zhou ◽  
Rong Hu ◽  
Li Ping Wu
Keyword(s):  
Local Search ◽  
Differential Evolution ◽  
Evaluation Method ◽  
Flow Shop ◽  
Differential Evolution Algorithm ◽  
Solution Space ◽  
Flow Shop Scheduling ◽  
De Algorithm ◽  
Weighted Earliness ◽  
Dependent Setup Times

A novel differential evolution (DE) algorithm, namely DE_TWET, is presented to deal with the no-wait flow-shop scheduling problem (NFSSP) with sequence-dependent setup times (SDSTs) and release dates (RDs). The criterion is to minimize a total weighted earliness/tardiness (TWET) cost function. The presented algorithm is a hybrid of DE, problem’s properties, and a special designed local search. In DE_TWET, DE is adopted to execute global search in the solution space, and the problem’s properties are utilized to give a speed-up evaluation method and construct the local search, and the special local search is designed to enhance the local search ability of DE. Experimental results and comparisons demonstrate the effectiveness and robustness of the presented algorithm.

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