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.

An Improved Differential Evolution Algorithm for Numerical Optimization Problems

2013 ◽  
Vol 415 ◽  
pp. 349-352
Author(s):  
Hong Wei Zhao ◽  
Hong Gang Xia
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Local Optimum ◽  
Local Optima ◽  
De Algorithm ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

Differential evolution (DE) is a population-based stochastic function minimizer (or maximizer), whose simple yet powerful and straightforward features make it very attractive for numerical optimization. However, DE is easy to trapped into local optima. In this paper, an improved differential evolution algorithm (IDE) proposed to speed the convergence rate of DE and enhance the global search of DE. The IDE employed a new mutation operation and modified crossover operation. The former can rapidly enhance the convergence of the MDE, and the latter can prevent the MDE from being trapped into the local optimum effectively. Besides, we dynamic adjust the scaling factor (F) and the crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark experiment simulations, the IDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and other algorithms (PSO and JADE) that reported in recent literature.

scholarly journals An improved differential evolution algorithm with a restart technique to solve systems of nonlinear equations

2020 ◽  
Vol 10 (1) ◽  
pp. 118-136
Author(s):  
Jeerayut Wetweerapong ◽  
Pikul Puphasuk
Keyword(s):  
Differential Evolution ◽  
Nonlinear Equations ◽  
Nonlinear Models ◽  
Differential Evolution Algorithm ◽  
Systems Of Nonlinear Equations ◽  
Test Problems ◽  
De Algorithm ◽  
New Strategy ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

In this research, an improved differential evolution algorithm with a restart technique (DE-R) is designed for solutions of systems of nonlinear equations which often occurs in solving complex computational problems involving variables of nonlinear models. DE-R adds a new strategy for mutation operation and a restart technique to prevent premature convergence and stagnation during the evolutionary search to the basic DE algorithm. The proposed method is evaluated on various real world and synthetic problems and compared with the recently developed methods in the literature. Experiment results show that DE-R can successfully solve all the test problems with fast convergence speed and give high quality solutions. It also outperforms the compared methods.

Optimization of FLADE Variable Cycle Engine Performance Based on Improved Differential Evolution Algorithm

2017 ◽  
Author(s):  
Zhang Xiao-bo ◽  
Wang Zhan-xue
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Engine Performance ◽  
Optimization Method ◽  
Performance Simulation ◽  
De Algorithm ◽  
Civil Aircraft ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm ◽  
Integrated Performance

In this paper, a double bypass variable cycle engine with FLADE (Fan on Blade) is considered. The FLADE VCE is one of the research hotspots for future military and civil aircraft power device, which shows outstanding performance advantages. Compared to the mixed-flow turbofan, FLADE VCE is more complex than conventional aero-engine for its multi-components and multi-variable parts, which make it difficult to modeling and optimization. For getting the performance of FLADE VCE, the model for engine performance simulation is researched. The method for FLADE performance simulation and the steady-state performance simulation model for FLADE VCE are developed. And a component-based engine performance simulation system is established based on object-oriented modeling method. For obtaining the optimal integrated performance of FLADE VCE, suitable optimization method is required. Unfortunately, the optimization of FLADE VCE is a non-linear non-differentiable problem, which makes it difficult to solve by conventional deterministic optimization method. In order to solve this problem, the differential evolution (DE) algorithm is considered. To overcome the limitations of original DE algorithm, an improved DE algorithm with modifying mutation operator is proposed by this paper. The FLADE VCE optimization problem is solved by employing the improved DE algorithm.

scholarly journals U-Shaped Assembly Line Balancing by Using Differential Evolution Algorithm

2018 ◽  
Vol 23 (4) ◽  
pp. 79 ◽  
Author(s):  
Poontana Sresracoo ◽  
Nuchsara Kriengkorakot ◽  
Preecha Kriengkorakot ◽  
Krit Chantarasamai
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Search Time ◽  
Assembly Line Balancing ◽  
The Other ◽  
Optimal Solutions ◽  
De Algorithm ◽  
Metaheuristic Methods ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

The objective of this research is to develop metaheuristic methods by using the differential evolution (DE) algorithm for solving the U-shaped assembly line balancing problem Type 1 (UALBP-1). The proposed DE algorithm is applied for balancing the lines (manufacturing a single product within a fixed given cycle time), where the aim is to minimize the number of workstations. After establishing the method, the results from previous research studies were compared with the results from this study. For the UALBP, two groups of benchmark problems were used for the experiments: (1) For the medium-sized UALBP (21–45 tasks), it was found that the DE algorithm DE/best/2 to Exponential Crossover 1 produced better solutions when compared to the other metaheuristic methods: it could generate 25 optimal solutions from a total of 25 instances, and the average time used for the calculation was 0.10 seconds/instance; (2) for the large-scale UALBP (75–297 tasks), it was found that the basic DE algorithm and improved differential evolution algorithm generated better solutions, and DE/best/2 to Exponential Crossover 1 generated the optimal solutions and achieved the minimum solution search time when compared to the other metaheuristic methods: it could generate 36 optimal solutions from a total of 62 instances, and the average time used for the calculation was 4.88 seconds/instance. From the comparison of the DE algorithms, it was found that the improved differential evolution algorithm generated optimal solutions with a better solution search time than the search time of the basic differential evolution algorithm. The basic and improved DE algorithm are the effective methods for balancing UALBP-1 when compared to the other metaheuristic methods.

scholarly journals An Adaptive Cauchy Differential Evolution Algorithm for Global Numerical Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Tae Jong Choi ◽  
Chang Wook Ahn ◽  
Jinung An
Keyword(s):  
Differential Evolution ◽  
Control Method ◽  
Differential Evolution Algorithm ◽  
Cauchy Distribution ◽  
Parameter Control ◽  
Control Parameters ◽  
Adaptive Parameter ◽  
De Algorithm ◽  
Parameter Values ◽  
Adaptive Parameter Control

Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt the control parameters for problem. In this paper, we present an adaptive parameter control DE algorithm. In the proposed algorithm, each individual has its own control parameters. The control parameters of each individual are adapted based on the average parameter value of successfully evolved individuals’ parameter values by using the Cauchy distribution. Through this, the control parameters of each individual are assigned either near the average parameter value or far from that of the average parameter value which might be better parameter value for next generation. The experimental results show that the proposed algorithm is more robust than the standard DE algorithm and several state-of-the-art adaptive DE algorithms in solving various unimodal and multimodal problems.

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