scholarly journals Optimization of steel roof trusses using machine learning-assisted differential evolution

2021 ◽  
Vol 15 (4) ◽  
pp. 99-110
Author(s):  
Nguyen Tran Hieu ◽  
Nguyen Quoc Cuong ◽  
Vu Anh Tuan
Keyword(s):  
Differential Evolution ◽  
Computational Cost ◽  
Differential Evolution Algorithm ◽  
Truss Structures ◽  
De Algorithm ◽  
Design Variables ◽  
Evolution Algorithm ◽  
Structural Engineers ◽  
Steel Trusses ◽  
Steel Roof

A steel truss is a preferred solution in large-span roof structures due to its good attributes such as lightweight, durability. However, designing steel trusses is a challenging task for engineers due to a large number of design variables. Recently, optimization-based design approaches have demonstrated the great potential to effectively support structural engineers in finding the optimal designs of truss structures. This paper aims to use the AdaBoost-DE algorithm for optimizing steel roof trusses. The AdaBoost-DE employed in this study is a hybrid algorithm in which the AdaBoost classification technique is used to enhance the performance of the Differential Evolution algorithm by skipping unnecessary fitness evaluations during the optimization process. An example of a duo-pitch steel roof truss with a span of 24 meters is carried out. The result shows that the AdaBoost-DE achieves the same optimal design as the original DE algorithm, but reduces the computational cost by approximately 36%.

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.

A Hybrid Differential Evolution Algorithm for Binary CSPs

2010 ◽  
Vol 108-111 ◽  
pp. 328-334 ◽  
Author(s):  
Hong Jie Fu
Keyword(s):  
Differential Evolution ◽  
Mutation Rate ◽  
Differential Evolution Algorithm ◽  
Premature Convergence ◽  
De Algorithm ◽  
Wrong Direction ◽  
Adaptive Adjustment ◽  
Evolution Algorithm

A novel hybrid elements exchange/electromagnetism meta-heuristic differential evolution algorithm, named EEMDE, is proposed in this paper, avoiding the premature convergence of original DE algorithm. A metric to measure the Simplification of force exerted on a point is defined as the mutation rate F in the EEMDE, which is used to get an adaptive adjustment of F. EEMDE may produce slight disturbance on the original vector for enhancing the exploring capacity and avoid the DE to the "uphill" in the wrong direction forward. Experiments demonstrate that the convergence of EEMDE is faster than DE and simulations based on some CSPs express the effectiveness, efficiency and robustness of it.

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 Solving Multiobjective Game in Multiconflict Situation Based on Adaptive Differential Evolution Algorithm with Simulated Annealing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Huimin Li ◽  
Shuwen Xiang ◽  
Wensheng Jia ◽  
Yanlong Yang ◽  
Shiguo Huang
Keyword(s):  
Simulated Annealing ◽  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Payoff Function ◽  
Game Model ◽  
Entropy Weight ◽  
Strategy Space ◽  
De Algorithm ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm

In this paper, we study the multiobjective game in a multiconflict situation. First, the feasible strategy set and synthetic strategy space are constructed in the multiconflict situation. Meanwhile, the value of payoff function under multiobjective is determined, and an integrated multiobjective game model is established in a multiconflict situation. Second, the multiobjective game model is transformed into the single-objective game model by the Entropy Weight Method. Then, in order to solve this multiobjective game, an adaptive differential evolution algorithm based on simulated annealing (ADESA) is proposed to solve this game, which is to improve the mutation factor and crossover operator of the differential evolution (DE) algorithm adaptively, and the Metropolis rule with probability mutation ability of the simulated annealing (SA) algorithm is used. Finally, the practicability and effectiveness of the algorithm are illustrated by a military example.

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.

scholarly journals Sufficient Conditions for Global Convergence of Differential Evolution Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Zhongbo Hu ◽  
Shengwu Xiong ◽  
Qinghua Su ◽  
Xiaowei Zhang
Keyword(s):  
Differential Evolution ◽  
Infinite Product ◽  
Differential Evolution Algorithm ◽  
Sufficient Conditions ◽  
Common Mutation ◽  
Convergence Conditions ◽  
De Algorithm ◽  
Mutation Operators ◽  
Evolution Algorithm ◽  
Algorithm Framework

The differential evolution algorithm (DE) is one of the most powerful stochastic real-parameter optimization algorithms. The theoretical studies on DE have gradually attracted the attention of more and more researchers. However, few theoretical researches have been done to deal with the convergence conditions for DE. In this paper, a sufficient condition and a corollary for the convergence of DE to the global optima are derived by using the infinite product. A DE algorithm framework satisfying the convergence conditions is then established. It is also proved that the two common mutation operators satisfy the algorithm framework. Numerical experiments are conducted on two parts. One aims to visualize the process that five convergent DE based on the classical DE algorithms escape from a local optimal set on two low dimensional functions. The other tests the performance of a modified DE algorithm inspired of the convergent algorithm framework on the benchmarks of the CEC2005.

Calibration Zone for the Parameters of the Differential Evolution Algorithm and Its Application to a Real Burst Location Problem

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
A. Pérez-González ◽  
A. Badillo-Olvera ◽  
O. Begovich ◽  
J. Ruíz-León
Keyword(s):  
Differential Evolution ◽  
Location Problem ◽  
Heuristic Algorithms ◽  
Differential Evolution Algorithm ◽  
Iterative Technique ◽  
Location Task ◽  
De Algorithm ◽  
Evolution Algorithm ◽  
Calibration Parameters

Numerical problems are usually solved using heuristic algorithms, due to their simplicity and easy understanding. Nevertheless, most of these methods have calibration parameters that do not count with selection premises oriented to obtain the best performance for the algorithm. This paper introduces an iterative technique that deals with this problem, searching for the calibration parameters that improve the Differential Evolution (DE) algorithm. The application of the proposed technique is illustrated on a real burst location problem in a pipeline prototype. The obtained results show the good performance of the methodology proposed for the burst location task, including the mapping of the calibration parameters that ameliorate the searching process.

scholarly journals Synthesis of Sparse Concentric Ring Arrays Based on Fitness-Associated Differential Evolution Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Xujian Wang ◽  
Minli Yao ◽  
Fenggan Zhang ◽  
Dingcheng Dai
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Population Diversity ◽  
Concentric Ring ◽  
Exploration Process ◽  
De Algorithm ◽  
Crossover Probability ◽  
Negative Effect ◽  
Evolution Algorithm ◽  
Enrich Population

In this paper, fitness-associated differential evolution (FITDE) algorithm is proposed and applied to the synthesis of sparse concentric ring arrays under constraint conditions, whose goal is to reduce peak sidelobe level. In unmodified differential evolution (DE) algorithm, crossover probability is constant and remains unchanged during the whole optimization process, resulting in the negative effect on the population diversity and convergence speed. Therefore, FITDE is proposed where crossover probability can change according to certain information. Firstly, the population fitness variance is introduced to the traditional differential evolution algorithm to adjust the constant crossover probability dynamically. The fitness variance in the earlier iterations is relatively large. Under this circumstance, the corresponding crossover probability shall be small to speed up the exploration process. As the iteration progresses, the fitness variance becomes small on the whole and the crossover probability should be set large to enrich population diversity. Thereby, we construct three variation strategies of crossover probability according to the above changing trend. Secondly, FITDE is tested on benchmark functions, and the best one of the three strategies is determined according to the test results. Finally, sparse concentric ring arrays are optimized using FITDE, of which the results are compared with reference algorithms. The optimization results manifest the advantageous effectiveness of FITDE.

Differential Evolution Algorithm for Solving a Nonlinear Single Pendulum Problem

2014 ◽  
Vol 931-932 ◽  
pp. 1129-1133
Author(s):  
Natee Panagant ◽  
Sujin Bureerat
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Approximate Solutions ◽  
Scaling Factor ◽  
Fourier Series Expansion ◽  
De Algorithm ◽  
Pendulum Equation ◽  
Expansion Form ◽  
Evolution Algorithm ◽  
Speed And Accuracy

A differential evolution (DE) algorithm has been employed to approximate the solution of a nonlinear single pendulum equation. The solution has been approximated as a Fourier series expansion form. Then, weighted-residual and penalty functions are employed to transform the problem into a constrained optimization problem while optimum solutions will be carried out by DE. This paper also studies an effect of a scaling factor of DE to the results. The results reveal that the scaling factor significantly affects the convergent speed and accuracy of DE. Approximate solutions well agree with the exact solutions for the scaling factor being 0.5.

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