CCPSO Based on PCA for Large-Scale Optimization Problem

2012 ◽  
Vol 236-237 ◽  
pp. 1190-1194
Author(s):  
Wen Hua Han ◽  
Xu Chen ◽  
Jun Xu
Keyword(s):  
Large Scale ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Principal Component ◽  
Complex Problem ◽  
Large Scale Optimization ◽  
Multimodal Optimization ◽  
Swarm Optimization ◽  
Gaussian Distributions ◽  
Scale Optimization

This paper proposed a cooperative coevolving particle swarm optimization base on principal component analysis (PCA-CCPSO) algorithm for large-scale and complex problem. In this algorithm, PCA are used to pick up the available particles which gathered the important information of the initialized particles for CCPSO. The Cauchy and Gaussian distributions are used to update the position of the particles and the coevolving subcomponent size of the particles is determined dynamically. The experimental results demonstrate that the convergence speed of PCA-CCPSO is faster than that of CCPSO in solving the large-scale and complex multimodal optimization problems.

scholarly journals A Distributed Quantum-Behaved Particle Swarm Optimization Using Opposition-Based Learning on Spark for Large-Scale Optimization Problem

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1860
Author(s):  
Zhaojuan Zhang ◽  
Wanliang Wang ◽  
Gaofeng Pan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Optimization Problem ◽  
Computational Cost ◽  
Particle Swarm ◽  
Large Scale Optimization ◽  
Swarm Optimization ◽  
Opposition Based Learning ◽  
Scale Optimization

In the era of big data, the size and complexity of the data are increasing especially for those stored in remote locations, and whose difficulty is further increased by the ongoing rapid accumulation of data scale. Real-world optimization problems present new challenges to traditional intelligent optimization algorithms since the traditional serial optimization algorithm has a high computational cost or even cannot deal with it when faced with large-scale distributed data. Responding to these challenges, a distributed cooperative evolutionary algorithm framework using Spark (SDCEA) is first proposed. The SDCEA can be applied to address the challenge due to insufficient computing resources. Second, a distributed quantum-behaved particle swarm optimization algorithm (SDQPSO) based on the SDCEA is proposed, where the opposition-based learning scheme is incorporated to initialize the population, and a parallel search is conducted on distributed spaces. Finally, the performance of the proposed SDQPSO is tested. In comparison with SPSO, SCLPSO, and SALCPSO, SDQPSO can not only improve the search efficiency but also search for a better optimum with almost the same computational cost for the large-scale distributed optimization problem. In conclusion, the proposed SDQPSO based on the SDCEA framework has high scalability, which can be applied to solve the large-scale optimization problem.

A new three-term spectral conjugate gradient algorithm with higher numerical performance for solving large scale optimization problems based on Quasi-Newton equation

2021 ◽  
pp. 2150053
Author(s):  
Jie Guo ◽  
Zhong Wan
Keyword(s):  
Conjugate Gradient ◽  
Large Scale ◽  
Line Search ◽  
Optimization Problems ◽  
Gradient Algorithm ◽  
Large Scale Optimization ◽  
Newton Equation ◽  
Numerical Performance ◽  
Scale Optimization ◽  
Quasi Newton

A new spectral three-term conjugate gradient algorithm in virtue of the Quasi-Newton equation is developed for solving large-scale unconstrained optimization problems. It is proved that the search directions in this algorithm always satisfy a sufficiently descent condition independent of any line search. Global convergence is established for general objective functions if the strong Wolfe line search is used. Numerical experiments are employed to show its high numerical performance in solving large-scale optimization problems. Particularly, the developed algorithm is implemented to solve the 100 benchmark test problems from CUTE with different sizes from 1000 to 10,000, in comparison with some similar ones in the literature. The numerical results demonstrate that our algorithm outperforms the state-of-the-art ones in terms of less CPU time, less number of iteration or less number of function evaluation.

A modified competitive swarm optimizer for large scale optimization problems

2017 ◽  
Vol 59 ◽  
pp. 340-362 ◽  
Author(s):  
Prabhujit Mohapatra ◽  
Kedar Nath Das ◽  
Santanu Roy
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Large Scale Optimization ◽  
Scale Optimization
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