DISH-XX Solving CEC2020 Single Objective Bound Constrained Numerical optimization Benchmark

2020 ◽  
Author(s):  
Adam Viktorin ◽  
Roman Senkerik ◽  
Michal Pluhacek ◽  
Tomas Kadavy ◽  
Ales Zamuda
Keyword(s):  
Numerical Optimization ◽  
Single Objective

scholarly journals Success History-Based Adaptive Differential Evolution Using Turning-Based Mutation

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1565 ◽  
Author(s):  
Xingping Sun ◽  
Linsheng Jiang ◽  
Yong Shen ◽  
Hongwei Kang ◽  
Qingyi Chen
Keyword(s):  
Differential Evolution ◽  
Clustering Analysis ◽  
Numerical Optimization ◽  
Optimization Algorithms ◽  
Search Space ◽  
Population Diversity ◽  
Convergence Of Algorithms ◽  
Adaptive Differential Evolution ◽  
Complex Methods ◽  
Single Objective

Single objective optimization algorithms are the foundation of establishing more complex methods, like constrained optimization, niching and multi-objective algorithms. Therefore, improvements to single objective optimization algorithms are important because they can impact other domains as well. This paper proposes a method using turning-based mutation that is aimed to solve the problem of premature convergence of algorithms based on SHADE (Success-History based Adaptive Differential Evolution) in high dimensional search space. The proposed method is tested on the Single Objective Bound Constrained Numerical Optimization (CEC2020) benchmark sets in 5, 10, 15, and 20 dimensions for all SHADE, L-SHADE, and jSO algorithms. The effectiveness of the method is verified by population diversity measure and population clustering analysis. In addition, the new versions (Tb-SHADE, TbL-SHADE and Tb-jSO) using the proposed turning-based mutation get apparently better optimization results than the original algorithms (SHADE, L-SHADE, and jSO) as well as the advanced DISH and the jDE100 algorithms in 10, 15, and 20 dimensional functions, but only have advantages compared with the advanced j2020 algorithm in 5 dimensional functions.

scholarly journals A NEW PROBABILISTIC ALGORITHM FOR SOLVING A CLASS OF SINGLE OR MULTI-OBJECTIVE OPTIMAL PROBLEMS

2009 ◽  
Vol 12 (11) ◽  
pp. 11-26
Author(s):  
Hao Van Tran ◽  
Thong Huu Nguyen
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Fixed Number ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Probabilistic Algorithm ◽  
Multi Objective ◽  
Single Objective

We consider a class of single-objective optimization problems which haves the character: there is a fixed number k (1≤k<n) that is independent of the size n of the problem such that if we only need to change values of k variables then it has the ability to find a better solution than the current one, let us call it Ok. In this paper, we propose a new numerical optimization technique, Search Via Probability (SVP) algorithm, for solving single objective optimization problems of the class Ok. The SVP algorithm uses probabilities to control the process of searching for optimal solutions. We calculate probabilities of the appearance of a better solution than the current one on each of iterations, and on the performance of SVP algorithm we create good conditions for its appearance. We tested this approach by implementing the SVP algorithm on some test single-objective and multi objective optimization problems, and we found good and very stable results.

A study on numerical optimization and performance verification of multiphase pump for offshore plant

2017 ◽  
Vol 231 (5) ◽  
pp. 382-397 ◽  
Author(s):  
Jun-Won Suh ◽  
Jin-Hyuk Kim ◽  
Young-Seok Choi ◽  
Won-Gu Joo ◽  
Kyoung-Yong Lee
Keyword(s):  
Performance Evaluation ◽  
Numerical Optimization ◽  
High Efficiency ◽  
Internal Flow ◽  
Latin Hypercube Sampling ◽  
Mechanical Efficiency ◽  
Optimal Point ◽  
Axial Pump ◽  
Design Variables ◽  
Single Objective

Multiphase pumps are core equipment for offshore plant industry. They are utilized in diverse areas. According to a report about the tendency of multiphase pumps for offshore plant, a helico-axial pump is the most preferred. A helico-axial pump with advanced technologies is widely known to have large handling capacity and operability even at high GVF ranges. However, its disadvantage is that its mechanical efficiency is lower than other multiphase pumps. Because of this, a numerical optimization was performed in this study to enhance the hydraulic performance of multiphase pumps. Before numerical optimization, reliability verification of numerical analysis for single-phase and multiphase flow was carried out. To perform a single objective optimization for high efficiency, design variables and ranges were selected. The single objective optimization was conducted for both impeller and diffuser. The objective function was evaluated at design points using Latin-hypercube sampling, one experiment technique, in design ranges. The performance of the experiment sets was evaluated using advanced computational fluid dynamics. After that, the response surface of a second-order polynomial function which was produced based on the performance evaluation results was used to find the optimal point. The results showed remarkable increases in a higher performance level than the base model. The reason for performance improvement was analyzed through comparison of the internal flow field. Additionally, numerical results were compared to results of performance evaluation through experiment.

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