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.

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.

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.

scholarly journals Spacecraft Multiple-Impulse Trajectory Optimization Using Differential Evolution Algorithm with Combined Mutation Strategies and Boundary-Handling Schemes

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Yuehe Zhu ◽  
Hua Wang ◽  
Jin Zhang
Keyword(s):  
Differential Evolution ◽  
Trajectory Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Global Optimum ◽  
Local Optimum ◽  
Local Optima ◽  
Trial Vector ◽  
Mutation Strategies

Since most spacecraft multiple-impulse trajectory optimization problems are complex multimodal problems with boundary constraint, finding the global optimal solution based on the traditional differential evolution (DE) algorithms becomes so difficult due to the deception of many local optima and the probable existence of a bias towards suboptimal solution. In order to overcome this issue and enhance the global searching ability, an improved DE algorithm with combined mutation strategies and boundary-handling schemes is proposed. In the first stage, multiple mutation strategies are utilized, and each strategy creates a mutant vector. In the second stage, multiple boundary-handling schemes are used to simultaneously address the same infeasible trial vector. Two typical spacecraft multiple-impulse trajectory optimization problems are studied and optimized using the proposed DE method. The experimental results demonstrate that the proposed DE method efficiently overcomes the problem created by the convergence to a local optimum and obtains the global optimum with a higher reliability and convergence rate compared with some other popular evolutionary methods.

THE PARETO DIFFERENTIAL EVOLUTION ALGORITHM

2002 ◽  
Vol 11 (04) ◽  
pp. 531-552 ◽  
Author(s):  
H. A. ABBASS ◽  
R. SARKER
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Standard Test ◽  
Test Problems ◽  
Pareto Optimal Solutions ◽  
Continuous Domains ◽  
Evolution Algorithm ◽  
Multiobjective Algorithms

The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as Vector Optimization Problems (VOPs)) has attracted much attention recently. Being population based approaches, EAs offer a means to find a group of pareto-optimal solutions in a single run. Differential Evolution (DE) is an EA that was developed to handle optimization problems over continuous domains. The objective of this paper is to introduce a novel Pareto Differential Evolution (PDE) algorithm to solve VOPs. The solutions provided by the proposed algorithm for five standard test problems, is competitive to nine known evolutionary multiobjective algorithms for solving VOPs.

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.

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