An efficient multi-objective PSO algorithm assisted by Kriging metamodel for expensive black-box problems

2016 ◽  
Vol 67 (1-2) ◽  
pp. 399-423 ◽  
Author(s):  
Haoxiang Jie ◽  
Yizhong Wu ◽  
Jianjun Zhao ◽  
Jianwan Ding ◽  
Liangliang
Keyword(s):  
Pso Algorithm ◽  
Black Box ◽  
Multi Objective ◽  
Kriging Metamodel

Kriging-Based Multi-objective Infill Criterion using NSGA-III for Expensive Black-Box Functions

2020 ◽  
Author(s):  
Latifa Rhanimi Karim ◽  
Rachid Ellaia ◽  
El-Ghazali Talbi
Keyword(s):  
Black Box ◽  
Multi Objective

Multiobjective Optimization for High Dimensional Expensively Constrained Black-Box Problems

2021 ◽  
pp. 1-59
Author(s):  
George Cheng ◽  
G. Gary Wang ◽  
Yeong-Maw Hwang
Keyword(s):  
Multiobjective Optimization ◽  
Trust Region ◽  
Adaptive Strategy ◽  
Black Box ◽  
Superior Performance ◽  
High Dimensional ◽  
Multi Objective Optimization ◽  
Semiconductor Substrate ◽  
Multi Objective ◽  
Computationally Expensive

Abstract Multi-objective optimization (MOO) problems with computationally expensive constraints are commonly seen in real-world engineering design. However, metamodel based design optimization (MBDO) approaches for MOO are often not suitable for high-dimensional problems and often do not support expensive constraints. In this work, the Situational Adaptive Kreisselmeier and Steinhauser (SAKS) method was combined with a new multi-objective trust region optimizer (MTRO) strategy to form the SAKS-MTRO method for MOO problems with expensive black-box constraint functions. The SAKS method is an approach that hybridizes the modeling and aggregation of expensive constraints and adds an adaptive strategy to control the level of hybridization. The MTRO strategy uses a combination of objective decomposition and K-means clustering to handle MOO problems. SAKS-MTRO was benchmarked against four popular multi-objective optimizers and demonstrated superior performance on average. SAKS-MTRO was also applied to optimize the design of a semiconductor substrate and the design of an industrial recessed impeller.

scholarly journals Distribution Network Expansion Planning Based on Multi-objective PSO Algorithm

2013 ◽  
Vol 05 (04) ◽  
pp. 975-979 ◽  
Author(s):  
Chunyu Zhang ◽  
Yi Ding ◽  
Qiuwei Wu ◽  
Qi Wang ◽  
Jacob Østergaard
Keyword(s):  
Distribution Network ◽  
Pso Algorithm ◽  
Network Expansion ◽  
Multi Objective ◽  
Expansion Planning

scholarly journals Efficient Hybrid Method of FEA-Based RSM and PSO Algorithm for Multi-Objective Optimization Design for a Compliant Rotary Joint for Upper Limb Assistive Device

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Ngoc Le Chau ◽  
Hieu Giang Le ◽  
Thanh-Phong Dao ◽  
Minh Phung Dang ◽  
Van Anh Dang
Keyword(s):  
Hybrid Method ◽  
Optimization Design ◽  
Pso Algorithm ◽  
Algorithm Analysis ◽  
Rank Test ◽  
Multi Objective Optimization ◽  
Rotational Angle ◽  
Multi Objective ◽  
Rotary Joint ◽  
Weight Factors

This paper proposes an efficient hybrid methodology for multi-objective optimization design of a compliant rotary joint (CRJ). A combination of the Taguchi method (TM), finite element analysis (FEA), the response surface method (RSM), and particle swarm optimization (PSO) algorithm is developed to solving the optimization problem. Firstly, the TM is applied to determine the number of numerical experiments. And then, 3D models of the CRJ is built for FEA simulation, and mathematical models are formed using the RSM. Subsequently, the suitability of the regression equation is assessed. At the same time, the calculation of weight factors is identified based on the series of statistical equations. Based on the well-established equations, a minimum mass and a maximum rotational angle are simultaneously optimized through the PSO algorithm. Analysis of variance is used to analyze the contribution of design variables. The behavior of the proposed method is compared to the adaptive elitist differential evolution and cuckoo search algorithm through the Wilcoxon signed rank test and Friedman test. The results determined the weight factors of the mass and rotational angle are about 0.4983 and 0.5017, respectively. The results found that the optimum the mass and rotational angle are 0.0368 grams and 59.1928 degrees, respectively. It revealed that the maximum stress of 335 MPa can guarantee a long working time. The results showed that the proposed hybrid method outperforms compared to other evolutionary algorithms. The predicted results are close to the validation results. The proposed method is useful for related engineering fields.

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