scholarly journals Fertilization optimization algorithm on CEC2015 and large scale problems

2021 ◽  
Author(s):  
Hazim Nasir Ghafil ◽  
Shaymaa Alsamia ◽  
Károly Jármai
Keyword(s):  
Optimization Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Random Search ◽  
Search Space ◽  
Large Scale Optimization ◽  
Biologically Inspired ◽  
Large Scale Problems ◽  
Comparison Results ◽  
Scale Optimization

Abstract This work, presents a novel optimizer called fertilization optimization algorithm, which is based on levy flight and random search within a search space. It is a biologically inspired algorithm by the fertilization of the egg in reproduction of mammals. The performance of the algorithm was compared with other optimization algorithms on CEC2015 time expensive benchmarks and large scale optimization problems. Remarkably, the fertilization optimization algorithm has overcome other optimizers in many cases and the examination and comparison results are encouraging to use the fertilization optimization algorithm in other possible applications.

Knowledge Assisted Optimization for Large-Scale Problems: A Review and Proposition

2018 ◽  
Author(s):  
Di Wu ◽  
G. Gary Wang
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Future Research ◽  
Large Scale Optimization ◽  
Knowledge Based ◽  
Design Engineers ◽  
Large Scale Problems ◽  
Black Boxes ◽  
Scale Optimization

In engineering design, engineers often have certain knowledge about the design problem. However, in the last decades, we assume design functions are black-boxes. This paper discusses if knowledge can help with optimization, especially for large-scale optimization problems. Existing large-scale optimization methods based on black-box functions are first reviewed and the drawbacks of those methods are briefly discussed. To understand what knowledge is and what kinds of knowledge can be obtained and applied in design, the concepts of knowledge in both artificial intelligence (AI) and in the area of product design are reviewed. The relevant knowledge based engineering (KBE) system is also explained. Existing applications of knowledge in optimization, especially for large-scale optimization, are reviewed and categorized. Potential further applications of incorporating knowledge for optimization are discussed in more detail, in hope to identify possible directions for future research for knowledge assisted 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.

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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