scholarly journals COPS: Large-scale nonlinearly constrained optimization problems

2000 ◽  
Author(s):  
A S Bondarenko ◽  
D M Bortz ◽  
J J More
Keyword(s):  
Constrained Optimization ◽  
Large Scale ◽  
Optimization Problems ◽  
Constrained Optimization Problems ◽  
Nonlinearly Constrained Optimization

Sequential Quadratic Programming

Acta Numerica ◽  
1995 ◽  
Vol 4 ◽  
pp. 1-51 ◽  
Author(s):  
Paul T. Boggs ◽  
Jon W. Tolle
Keyword(s):  
Quadratic Programming ◽  
Constrained Optimization ◽  
Sequential Quadratic Programming ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Public Domain ◽  
Large Set ◽  
Constrained Optimization Problems ◽  
Nonlinearly Constrained Optimization

Since its popularization in the late 1970s, Sequential Quadratic Programming (SQP) has arguably become the most successful method for solving nonlinearly constrained optimization problems. As with most optimization methods, SQP is not a single algorithm, but rather a conceptual method from which numerous specific algorithms have evolved. Backed by a solid theoretical and computational foundation, both commercial and public-domain SQP algorithms have been developed and used to solve a remarkably large set of important practical problems. Recently large-scale versions have been devised and tested with promising results.

Constraint-Objective Cooperative Coevolution for Large-scale Constrained Optimization

2021 ◽  
Vol 1 (3) ◽  
pp. 1-26
Author(s):  
Peilan Xu ◽  
Wenjian Luo ◽  
Xin Lin ◽  
Jiajia Zhang ◽  
Yingying Qiao ◽  
...  
Keyword(s):  
Constrained Optimization ◽  
Large Scale ◽  
Optimization Problems ◽  
Cooperative Coevolution ◽  
Constrained Optimization Problems ◽  
Dual Nature ◽  
Scale Optimization ◽  
The Impact ◽  
Evolutionary Intelligence ◽  
Modular Nature

Large-scale optimization problems and constrained optimization problems have attracted considerable attention in the swarm and evolutionary intelligence communities and exemplify two common features of real problems, i.e., a large scale and constraint limitations. However, only a little work on solving large-scale continuous constrained optimization problems exists. Moreover, the types of benchmarks proposed for large-scale continuous constrained optimization algorithms are not comprehensive at present. In this article, first, a constraint-objective cooperative coevolution (COCC) framework is proposed for large-scale continuous constrained optimization problems, which is based on the dual nature of the objective and constraint functions: modular and imbalanced components. The COCC framework allocates the computing resources to different components according to the impact of objective values and constraint violations. Second, a benchmark for large-scale continuous constrained optimization is presented, which takes into account the modular nature, as well as both imbalanced and overlapping characteristics of components. Finally, three different evolutionary algorithms are embedded into the COCC framework for experiments, and the experimental results show that COCC performs competitively.

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