A new parameter setting-based modified differential evolution for function optimization

2020 ◽  
Vol 11 (04) ◽  
pp. 2050029
Author(s):  
Sukanta Nama ◽  
Apu Kumar Saha
Keyword(s):  
Performance Evaluation ◽  
Differential Evolution ◽  
Real World ◽  
Population Based ◽  
Function Optimization ◽  
Mutation Operator ◽  
Control Parameters ◽  
Test Results ◽  
Parameter Setting ◽  
De Algorithm

The population-based efficient iterative evolutionary algorithm (EA) is differential evolution (DE). It has fewer control parameters but is useful when dealing with complex problems of optimization in the real world. A great deal of progress has already been made and implemented in various fields of engineering and science. Nevertheless, DE is prone to the setting of control parameters in its performance evaluation. Therefore, the appropriate adjustment of the time-consuming control parameters is necessary to achieve optimal DE efficiency. This research proposes a new version of the DE algorithm control parameters and mutation operator. For the justifiability of the suggested method, several benchmark functions are taken from the literature. The test results are contrasted with other literary algorithms.

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: A Survey and Analysis

2018 ◽  
Vol 8 (10) ◽  
pp. 1945 ◽  
Author(s):  
Tarik Eltaeib ◽  
Ausif Mahmood
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
State Of The Art ◽  
Population Based ◽  
Vital Role ◽  
Fine Tuning ◽  
Control Parameters ◽  
Hybrid Techniques ◽  
Global Optimizer

Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based metaheuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. In particular, we present a state-of-the-art survey of the literature on DE and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques.

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 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.

Differential Evolution with Self-Adaptation

2011 ◽  
pp. 488-493 ◽  
Author(s):  
Janez Brest
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Objective Function ◽  
Optimization Method ◽  
Control Parameters ◽  
Research Areas ◽  
De Algorithm ◽  
Practical Engineering ◽  
Self Adaptation ◽  
Self Adaptive

Many practical engineering applications can be formulated as a global optimization problem, in which objective function has many local minima, and derivatives of the objective function are unavailable. Differential Evolution (DE) is a floating-point encoding evolutionary algorithm for global optimization over continuous spaces (Storn & Price, 1997) (Liu & Lampinen, 2005) (Price, Storn & Lampinen, 2005) (Feoktistov, 2006). Nowadays it is used as a powerful global optimization method within a wide range of research areas. Recent researches indicate that self-adaptive DE algorithms are considerably better than the original DE algorithm. The necessity of changing control parameters during the optimization process is also confirmed based on the experiments in (Brest, Greiner, Boškovic, Mernik, Žumer, 2006a). DE with self-adaptive control parameters has already been presented in (Brest et al., 2006a). This chapter presents self-adaptive approaches that were recently proposed for control parameters in DE algorithm.

scholarly journals LADE: Learning Automata Based Differential Evolution

2015 ◽  
Vol 24 (06) ◽  
pp. 1550023 ◽  
Author(s):  
Mahshid Mahdaviani ◽  
Javidan Kazemi Kordestani ◽  
Alireza Rezvanian ◽  
Mohammad Reza Meybodi
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Learning Automata ◽  
Real Life ◽  
Population Based ◽  
Exact Algorithms ◽  
Function Optimization ◽  
Crossover Probability ◽  
Mutation Strategy ◽  
Mathematical Techniques

Many engineering optimization problems do not standard mathematical techniques, and cannot be solved using exact algorithms. Evolutionary algorithms have been successfully used for solving such optimization problems. Differential evolution is a simple and efficient population-based evolutionary algorithm for global optimization, which has been applied in many real world engineering applications. However, the performance of this algorithm is sensitive to appropriate choice of its parameters as well as its mutation strategy. In this paper, we propose two different underlying classes of learning automata based differential evolution for adaptive selection of crossover probability and mutation strategy in differential evolution. In the first class, genomes of the population use the same mutation strategy and crossover probability. In the second class, each genome of the population adjusts its own mutation strategy and crossover probability parameter separately. The performance of the proposed methods is analyzed on ten benchmark functions from CEC 2005 and one real-life optimization problem. The obtained results show the efficiency of the proposed algorithms for solving real-parameter function optimization problems.

A GENERALIZED HYBRID GENERATION SCHEME OF DIFFERENTIAL EVOLUTION FOR GLOBAL NUMERICAL OPTIMIZATION

2011 ◽  
Vol 10 (01) ◽  
pp. 35-65 ◽  
Author(s):  
WENYIN GONG ◽  
ZHIHUA CAI ◽  
LIYUAN JIA ◽  
HUI LI
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Search Space ◽  
Mutation Operator ◽  
Test Functions ◽  
Hybrid Generation ◽  
Global Numerical Optimization ◽  
De Algorithm ◽  
Speed Up ◽  
Continuous Domain

Differential evolution (DE) is a simple yet powerful evolutionary algorithm for global numerical optimization over continuous domain, which has been widely used in many areas. Although DE is good at exploring the search space, it is slow at the exploitation of the solutions. To alleviate this drawback, in this paper, we propose a generalized hybrid generation scheme, which attempts to enhance the exploitation and accelerate the convergence velocity of the original DE algorithm. In the hybrid generation scheme the operator with powerful exploitation is hybridized with the original DE operator. In addition, a self-adaptive exploitation factor is introduced to control the frequency of the exploitation operation. In order to evaluate the performance of our proposed generation scheme, two operators, the migration operator of biogeography-based optimization and the "DE/best/1" mutation operator, are employed as the exploitation operator. Moreover, 23 benchmark functions (including 10 test functions provided by CEC2005 special session) are chosen from the literature as the test suite. Experimental results confirm that the new hybrid generation scheme is able to enhance the exploitation of the original DE algorithm and speed up its convergence rate.

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.

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.

scholarly journals Adapting Differential Evolution Algorithms For Continuous Optimization Via Greedy Adjustment Of Control Parameters

2016 ◽  
Vol 6 (2) ◽  
pp. 103-118 ◽  
Author(s):  
Miguel Leon ◽  
Ning Xiong
Keyword(s):  
Differential Evolution ◽  
Continuous Optimization ◽  
Evolutionary Process ◽  
Control Parameters ◽  
De Algorithm ◽  
Reliable Assessment ◽  
Current Parameter ◽  
Evolution Algorithms ◽  
Relative Errors ◽  
Differential Evolution Algorithms

AbstractDifferential evolution (DE) presents a class of evolutionary and meta-heuristic techniques that have been applied successfully to solve many real-world problems. However, the performance of DE is significantly influenced by its control parameters such as scaling factor and crossover probability. This paper proposes a new adaptive DE algorithm by greedy adjustment of the control parameters during the running of DE. The basic idea is to perform greedy search for better parameter assignments in successive learning periods in the whole evolutionary process. Within each learning period, the current parameter assignment and its neighboring assignments are tested (used) in a number of times to acquire a reliable assessment of their suitability in the stochastic environment with DE operations. Subsequently the current assignment is updated with the best candidate identified from the neighborhood and the search then moves on to the next learning period. This greedy parameter adjustment method has been incorporated into basic DE, leading to a new DE algorithm termed as Greedy Adaptive Differential Evolution (GADE). GADE has been tested on 25 benchmark functions in comparison with five other DE variants. The results of evaluation demonstrate that GADE is strongly competitive: it obtained the best rank among the counterparts in terms of the summation of relative errors across the benchmark functions with a high dimensionality.

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