Work

Collaborative Optimization Algorithm Based on the Penalty Function

Year: 2014

Type: article

Abstract: The paper discusses the collaborative optimization problems with bounded. Based the penalty function the system-level optimization convert to a unconstraint programming. To the discipline-level optimization, the normalized weighted coefficients are used and combine relaxation factors to solve. It uses the relaxation factor to expand the feasible region, and possibly makes the iteration in the calculation process run inside feasible region. The data have shown that the algorithm has expanded the choice range of the initial points with high calculation accuracy and better algorithm stability.

Source: Applied Mechanics and Materials

DOI: 10.4028/www.scientific.net/amm.538.447

Authors: Zhi Xia Jiang, Pin Chao Meng, Yan Zhong Li, Wei Shi Yin

Institution: Changchun University of Science and Technology

Language: English

Topic: Advanced Optimization Algorithms Research

Subfield: Numerical Analysis

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Funder: Natural Science Foundation of Jilin Province

OpenAlex ID: W2070780349

FWCI:

Cites: 1

Cited by:

Related to: 10