Enhancing the competitive swarm optimizer with covariance matrix adaptation for large scale optimization

2021 ◽  
Author(s):  
Wei Li ◽  
Zhou Lei ◽  
Junqing Yuan ◽  
Haonan Luo ◽  
Qingzheng Xu
Keyword(s):  
Covariance Matrix ◽  
Large Scale ◽  
Large Scale Optimization ◽  
Covariance Matrix Adaptation ◽  
Scale Optimization

scholarly journals Large-Scale Evolutionary Strategy Based on Gradient Approximation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jin Jin
Keyword(s):  
Covariance Matrix ◽  
Large Scale ◽  
Experimental Results ◽  
Test Suite ◽  
Evolutionary Strategy ◽  
Large Scale Optimization ◽  
Next Generation ◽  
High Complexity ◽  
Historical Information ◽  
Scale Optimization

For large-scale optimization, CMA-ES has the disadvantages of high complexity and premature stagnation. An improved CMA-ES algorithm called GI-ES was proposed in this paper. For the problem of high complexity, the method in this paper replaces the calculation of a covariance matrix with the modeling of expected fitting degrees for a given covariance matrix. At the same time, to solve the problem of premature stagnation, this paper replaces the historical information of elite individuals with the historical information of all individuals. The information can be seen as approximate gradients. The parameters of the next generation of individuals are generated based on the approximate gradients. The experimental results were tested using CEC 2010 and CEC2013 LSGO benchmark test suite, and the experimental results verified the effectiveness of the algorithm on a number of different tasks.

scholarly journals Large-scale optimization of households with photovoltaic-battery system and demand response

2020 ◽  
Vol 53 (2) ◽  
pp. 12572-12577
Author(s):  
Fernando Lezama ◽  
Ricardo Faia ◽  
Omid Abrishambaf ◽  
Pedro Faria ◽  
Zita Vale
Keyword(s):  
Demand Response ◽  
Large Scale ◽  
Large Scale Optimization ◽  
Battery System ◽  
Scale Optimization

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.

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