A Modified Differential Evolution Algorithm for Numerical Optimization Problems

2014 ◽  
Vol 602-605 ◽  
pp. 3585-3588
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Local Optimum ◽  
Test Functions ◽  
De Algorithm ◽  
Mutation Operation ◽  
Evolution Algorithm ◽  
Pitch Adjustment

To efficiently enhance the global search and local search of Differential Evolution algorithm ( DE), A modified differential evolution algorithm (MDE) is proposed in this paper. The MDE and the DE are different in two aspects. The first is the MDE Algorithm use a strategy of Pitch adjustment instead of original mutation operation, this can enhance the convergence of the MDE, the second is integrate the opposed-learning operation in the crossover operation to prevent DE from being trapped into local optimum. Four test functions are adopted to make comparison with original DE, the MDE has demonstrated stronger velocity of convergence and precision of optimization than differential DE algorithm and PSO.

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.

Modified Differential Evolution Algorithm for Numerical Optimization Problems

2014 ◽  
Vol 989-994 ◽  
pp. 2536-2539
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Local Optimum ◽  
Algorithm Performance ◽  
De Algorithm ◽  
Benchmark Experiment ◽  
Random Position ◽  
Evolution Algorithm

In this paper, a modified differential evolution algorithm (MDE) developed to solve unconstrained numerical optimization problems. The MDE algorithm employed random position updating and disturbance operation to replaces the traditional mutation 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 crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark experiment simulations, the MDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and its two improved algorithms (JADE and SaDE) that reported in recent literature.

An Opposition-Based Modified Differential Evolution Algorithm for Numerical Optimization Problems

2013 ◽  
Vol 415 ◽  
pp. 309-313
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Recent Literature ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Local Optimum ◽  
Algorithm Performance ◽  
De Algorithm ◽  
New Strategy ◽  
Evolution Algorithm

In this paper, a new opposition-based modified differential evolution algorithm (OMDE) is proposed. This algorithm integrates the opposed-learning operation with the crossover operation to enhance the convergence of the algorithm and to prevent the algorithm from being trapped into the local optimum effectively. Besides, we employed a new strategy to dynamic adjust mutation rate (MR) and crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark functions tested, the OMDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and its two improved algorithms (JADE and SaDE) that reported in recent literature.

Enhanced Directed Differential Evolution Algorithm for Solving Constrained Engineering Optimization Problems

2019 ◽  
Vol 10 (1) ◽  
pp. 1-28 ◽  
Author(s):  
Ali Wagdy Mohamed ◽  
Ali Khater Mohamed ◽  
Ehab Z. Elfeky ◽  
Mohamed Saleh
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Test Problems ◽  
New Mutation ◽  
De Algorithm ◽  
Engineering Problems ◽  
Evolution Algorithm ◽  
Mutation Scheme ◽  
Exploration Exploitation

The performance of Differential Evolution is significantly affected by the mutation scheme, which attracts many researchers to develop and enhance the mutation scheme in DE. In this article, the authors introduce an enhanced DE algorithm (EDDE) that utilizes the information given by good individuals and bad individuals in the population. The new mutation scheme maintains effectively the exploration/exploitation balance. Numerical experiments are conducted on 24 test problems presented in CEC'2006, and five constrained engineering problems from the literature for verifying and analyzing the performance of EDDE. The presented algorithm showed competitiveness in some cases and superiority in other cases in terms of robustness, efficiency and quality the of the results.

Optimal Power Flow Using an Improved Hybrid Differential Evolution Algorithm

2017 ◽  
Vol 11 (1) ◽  
pp. 177-192
Author(s):  
Gonggui Chen ◽  
Zhengmei Lu ◽  
Zhizhong Zhang ◽  
Zhi Sun
Keyword(s):  
Differential Evolution ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Differential Evolution Algorithm ◽  
Local Optimum ◽  
Power Losses ◽  
De Algorithm ◽  
Optimal Power ◽  
Evolution Algorithm

Objective: In this paper, an improved hybrid differential evolution (IHDE) algorithm based on differential evolution (DE) algorithm and particle swarm optimization (PSO) has been proposed to solve the optimal power flow (OPF) problem of power system which is a multi-constrained, large-scale and nonlinear optimization problem. Method: In IHDE algorithm, the DE is employed as the main optimizer; and the three factors of PSO, which are inertia, cognition, and society, are used to improve the mutation of DE. Then the learning mechanism and the adaptive control of the parameters are added to the crossover, and the greedy selection considering the value of penalty function is proposed. Furthermore, the replacement mechanism is added to the IHDE for reducing the probability of falling into the local optimum. The performance of this method is tested on the IEEE30-bus and IEEE57-bus systems, and the generator quadratic cost and the transmission real power losses are considered as objective functions. Results: The simulation results demonstrate that IHDE algorithm can solve the OPF problem successfully and obtain the better solution compared with other methods reported in the recent literatures.

Opposition-Based Modified Differential Evolution Algorithm for Power System Economic Load Dispatch

2013 ◽  
Vol 365-366 ◽  
pp. 178-181
Author(s):  
Hong Gang Xia ◽  
Wei Xiang Ding
Keyword(s):  
Power System ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Local Optimum ◽  
Economic Load Dispatch ◽  
Algorithm Performance ◽  
Opposition Based Learning ◽  
De Algorithm ◽  
New Strategy ◽  
Evolution Algorithm

Opposition-based modified differential evolution algorithm (OMDE) is proposed for solving power System economic load dispatch in this paper. This algorithm integrates the opposition-based learning operation with the crossover operation to enhance the convergence of the algorithm and to prevent the algorithm from being trapped into the local optimum effectively. Besides, we employed a new strategy to dynamic adjust mutation rate (MR) and crossover rate (CR), which is aimed at further improving algorithm performance. Based on 6 units and 13 units power system experiment simulations, the OMDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and other improved algorithms that reported in recent literature.

Improved Differential Evolution Algorithm for Structural Optimization Design of Flumes with Hybrid Discrete Variables

2011 ◽  
Vol 383-390 ◽  
pp. 672-677
Author(s):  
Juan Zhou ◽  
Duo Xin Zhang ◽  
Xian Liang Liu
Keyword(s):  
Structural Optimization ◽  
Differential Evolution ◽  
Optimization Design ◽  
Optimization Problems ◽  
Logistic Map ◽  
Differential Evolution Algorithm ◽  
Local Optimum ◽  
Original Design ◽  
Design Scheme ◽  
De Algorithm

The traditional method applying to solve continuous variable optimization problems is not suit for flume structural optimization design with hybrid discrete variable. According to the mathematical model of structural optimum design of the prestressed U-shell flumes, differential evolution (DE) algorithm was introduced to flume structural optimization design. In order to improve the population’s diversity and the ability of escaping from the local optimum, a self-adapting crossover probability factor was presented. Furthermore, a chaotic sequence based on logistic map was employed to self-adaptively adjust mutation factor based on linear crossover, which can improve the convergence of DE algorithm. Dynamic penalty function, to transform the constrained problem to unconstrained one, was employed. The result shows that, compared with the original design scheme, the optimization design scheme can greatly reduce the amount of prestressed reinforcement. The construction cost of both the flume and the whole project can be reduced accordingly.

scholarly journals A Convergent Differential Evolution Algorithm with Hidden Adaptation Selection for Engineering Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zhongbo Hu ◽  
Shengwu Xiong ◽  
Zhixiang Fang ◽  
Qinghua Su
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Population Diversity ◽  
Engineering Optimization ◽  
De Algorithm ◽  
Self Adaptation ◽  
Evolution Algorithm ◽  
Selection For ◽  
Adaptation Scheme

Many improved differential Evolution (DE) algorithms have emerged as a very competitive class of evolutionary computation more than a decade ago. However, few improved DE algorithms guarantee global convergence in theory. This paper developed a convergent DE algorithm in theory, which employs a self-adaptation scheme for the parameters and two operators, that is, uniform mutation and hidden adaptation selection (haS) operators. The parameter self-adaptation and uniform mutation operator enhance the diversity of populations and guarantee ergodicity. The haS can automatically remove some inferior individuals in the process of the enhancing population diversity. The haS controls the proposed algorithm to break the loop of current generation with a small probability. The breaking probability is a hidden adaptation and proportional to the changes of the number of inferior individuals. The proposed algorithm is tested on ten engineering optimization problems taken from IEEE CEC2011.

Modified Harmony Search Algorithm for Unconstrained Numerical Optimization Problems

2014 ◽  
Vol 989-994 ◽  
pp. 2528-2531
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Differential Evolution Algorithm ◽  
Optimization Method ◽  
Harmony Search Algorithm ◽  
Local Optimum ◽  
Perfect State ◽  
Evolution Algorithm

Harmony search algorithm is a new meta-heuristic optimization method imitating the music improvisation process where musicians improvise their instruments’ pitches searching for a perfect state of harmony. To enable the harmony search algorithm to transcend its limited capability of local optimum, a modified harmony search algorithm is proposed in this paper. In the modified harmony search algorithm, the mutation operation of differential evolution algorithm is introduced into MHS algorithm, which improves its convergence. Several standard benchmark optimization functions are to be test and compare the performance of the MHS. The results revealed the superiority of the proposed method to the HS and recently developed variants.

Performance Analyses of Differential Evolution Algorithm Based on Dynamic Fitness Landscape

2019 ◽  
Vol 13 (1) ◽  
pp. 36-61 ◽  
Author(s):  
Kangshun Li ◽  
Zhuozhi Liang ◽  
Shuling Yang ◽  
Zhangxing Chen ◽  
Hui Wang ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Fitness Landscape ◽  
Optimization Problems ◽  
Dynamic Properties ◽  
Differential Evolution Algorithm ◽  
Global Optimum ◽  
De Algorithm ◽  
Evolution Algorithm ◽  
Fitness Distance Correlation ◽  
And Performance

Dynamic fitness landscape analyses contain different metrics to attempt to analyze optimization problems. In this article, some of dynamic fitness landscape metrics are selected to discuss differential evolution (DE) algorithm properties and performance. Based on traditional differential evolution algorithm, benchmark functions and dynamic fitness landscape measures such as fitness distance correlation for calculating the distance to the nearest global optimum, ruggedness based on entropy, dynamic severity for estimating dynamic properties, a fitness cloud for getting a visual rendering of evolvability and a gradient for analyzing micro changes of benchmark functions in differential evolution algorithm, the authors obtain useful results and try to apply effective data, figures and graphs to analyze the performance differential evolution algorithm and make conclusions. Those metrics have great value and more details as DE performance.

Sign in / Sign up

Export Citation Format

Share Document