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.

scholarly journals Composite Differential Evolution with Modified Oracle Penalty Method for Constrained Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Minggang Dong ◽  
Ning Wang ◽  
Xiaohui Cheng ◽  
Chuanxian Jiang
Keyword(s):  
Differential Evolution ◽  
Constrained Optimization ◽  
Penalty Function ◽  
Optimization Problems ◽  
Real Life ◽  
Optimization Criterion ◽  
Constrained Optimization Problems ◽  
Constraints Handling ◽  
Efficient Alternative ◽  
Handling Methods

Motivated by recent advancements in differential evolution and constraints handling methods, this paper presents a novel modified oracle penalty function-based composite differential evolution (MOCoDE) for constrained optimization problems (COPs). More specifically, the original oracle penalty function approach is modified so as to satisfy the optimization criterion of COPs; then the modified oracle penalty function is incorporated in composite DE. Furthermore, in order to solve more complex COPs with discrete, integer, or binary variables, a discrete variable handling technique is introduced into MOCoDE to solve complex COPs with mix variables. This method is assessed on eleven constrained optimization benchmark functions and seven well-studied engineering problems in real life. Experimental results demonstrate that MOCoDE achieves competitive performance with respect to some other state-of-the-art approaches in constrained optimization evolutionary algorithms. Moreover, the strengths of the proposed method include few parameters and its ease of implementation, rendering it applicable to real life. Therefore, MOCoDE can be an efficient alternative to solving constrained optimization problems.

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.

scholarly journals Differential Evolution with Novel Mutation and Adaptive Crossover Strategies for Solving Large Scale Global Optimization Problems

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Ali Wagdy Mohamed ◽  
Abdulaziz S. Almazyad
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Large Scale ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
High Dimensional ◽  
Gradual Change ◽  
Comparison Results ◽  
Mutation Strategy ◽  
Common Trade

This paper presents Differential Evolution algorithm for solving high-dimensional optimization problems over continuous space. The proposed algorithm, namely, ANDE, introduces a new triangular mutation rule based on the convex combination vector of the triplet defined by the three randomly chosen vectors and the difference vectors between the best, better, and the worst individuals among the three randomly selected vectors. The mutation rule is combined with the basic mutation strategy DE/rand/1/bin, where the new triangular mutation rule is applied with the probability of 2/3 since it has both exploration ability and exploitation tendency. Furthermore, we propose a novel self-adaptive scheme for gradual change of the values of the crossover rate that can excellently benefit from the past experience of the individuals in the search space during evolution process which in turn can considerably balance the common trade-off between the population diversity and convergence speed. The proposed algorithm has been evaluated on the 20 standard high-dimensional benchmark numerical optimization problems for the IEEE CEC-2010 Special Session and Competition on Large Scale Global Optimization. The comparison results between ANDE and its versions and the other seven state-of-the-art evolutionary algorithms that were all tested on this test suite indicate that the proposed algorithm and its two versions are highly competitive algorithms for solving large scale global optimization problems.

Centroid Opposition-Based Differential Evolution

2014 ◽  
Vol 5 (4) ◽  
pp. 1-25 ◽  
Author(s):  
Shahryar Rahnamayan ◽  
Jude Jesuthasan ◽  
Farid Bourennani ◽  
Greg F. Naterer ◽  
Hojjat Salehinejad
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Extreme Points ◽  
Search Space ◽  
Population Based ◽  
Effective Population ◽  
Centroid Point ◽  
Opposite Point ◽  
Novel Concept ◽  
Multiple Variants

The capabilities of evolutionary algorithms (EAs) in solving nonlinear and non-convex optimization problems are significant. Differential evolution (DE) is an effective population-based EA, which has emerged as very competitive. Since its inception in 1995, multiple variants of DE have been proposed with higher performance. Among these DE variants, opposition-based differential evolution (ODE) established a novel concept in which individuals must compete with theirs opposites in order to make an entry in the next generation. The generation of opposite points is based on the current extreme points (i.e., maximum and minimum) in the search space. This paper develops a new scheme that utilizes the centroid point of a population to calculate opposite individuals. The classical scheme of an opposite point is modified. Incorporating this new scheme into DE leads to an enhanced ODE that is identified as centroid opposition-based differential evolution (CODE). The accuracy of the CODE algorithm is comprehensively evaluated on well-known complex benchmark functions and compared with the performance of conventional DE, ODE, and other state-of-the-art algorithms. The results for CODE are found to be promising.

scholarly journals Multiscale Cooperative Differential Evolution Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Yongzhao Du ◽  
Yuling Fan ◽  
Xiaofang Liu ◽  
Yanmin Luo ◽  
Jianeng Tang ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Early Stage ◽  
Differential Evolution Algorithm ◽  
Search Range ◽  
Adaptive Parameter ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Evolution Algorithms ◽  
The Individual

A multiscale cooperative differential evolution algorithm is proposed to solve the problems of narrow search range at the early stage and slow convergence at the later stage in the performance of the traditional differential evolution algorithms. Firstly, the population structure of multipopulation mechanism is adopted so that each subpopulation is combined with a corresponding mutation strategy to ensure the individual diversity during evolution. Then, the covariance learning among populations is developed to establish a suitable rotating coordinate system for cross operation. Meanwhile, an adaptive parameter adjustment strategy is introduced to balance the population survey and convergence. Finally, the proposed algorithm is tested on the CEC 2005 benchmark function and compared with other state-of-the-art evolutionary algorithms. The experiment results showed that the proposed algorithm has better performance in solving global optimization problems than other compared algorithms.

Multiobjective Differential Evolution Based on Fuzzy Performance Feedback

2014 ◽  
Vol 5 (4) ◽  
pp. 45-64 ◽  
Author(s):  
Chatkaew Jariyatantiwait ◽  
Gary G. Yen
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Performance Feedback ◽  
Performance Metrics ◽  
Fuzzy Inference ◽  
Optimization Problems ◽  
Evolution Process ◽  
Exploration And Exploitation ◽  
Multiobjective Optimization Problems ◽  
Mutation Strategy

Differential evolution is often regarded as one of the most efficient evolutionary algorithms to tackle multiobjective optimization problems. The key to success of any multiobjective evolutionary algorithms (MOEAs) is maintaining a delicate balance between exploration and exploitation throughout the evolution process. In this paper, the authors propose a Fuzzy-based Multiobjective Differential Evolution (FMDE) that uses performance metrics, specifically hypervolume, spacing, and maximum spread, to measure the state of the evolution process. The authors apply the fuzzy inference rules to these metrics in order to dynamically adjust the associated control parameters of a chosen mutation strategy used in this algorithm. One parameter controls the degree of greedy or exploitation, while another regulates the degree of diversity or exploration of the reproduction phase. Therefore, the authors can appropriately adjust the degree of exploration and exploitation through performance feedback. The performance of FMDE is evaluated on well-known ZDT and DTLZ test suites. The results validate that the proposed algorithm is competitive with respect to chosen state-of-the-art MOEAs.

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 Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Xiaobing Yu ◽  
Jie Cao ◽  
Haiyan Shan ◽  
Li Zhu ◽  
Jun Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Based ◽  
Stochastic Search ◽  
Adaptive Mutation ◽  
Swarm Optimization ◽  
Local Optima

Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and DE. Adaptive mutation is carried out on current population when the population clusters around local optima. The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population. Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive. The balanced parameter sensitivity is discussed in detail.

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.

Sign in / Sign up

Export Citation Format

Share Document