Using differential evolution strategies in chemical reaction optimization for global numerical optimization

2017 ◽  
Vol 47 (3) ◽  
pp. 935-961 ◽  
Author(s):  
Mourad Nouioua ◽  
Zhiyong Li
Keyword(s):  
Differential Evolution ◽  
Chemical Reaction ◽  
Numerical Optimization ◽  
Evolution Strategies ◽  
Chemical Reaction Optimization ◽  
Global Numerical Optimization ◽  
Reaction Optimization

scholarly journals A Hybrid Mutation Chemical Reaction Optimization Algorithm for Global Numerical Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Ransikarn Ngambusabongsopa ◽  
Zhiyong Li ◽  
Esraa Eldesouky
Keyword(s):  
Chemical Reaction ◽  
Optimization Algorithm ◽  
Numerical Optimization ◽  
Statistical Hypothesis ◽  
Statistical Hypothesis Testing ◽  
Chemical Reaction Optimization ◽  
Global Numerical Optimization ◽  
Mutation Operators ◽  
Reaction Optimization ◽  
Optimal Quality

This paper proposes a hybrid metaheuristic approach that improves global numerical optimization by increasing optimal quality and accelerating convergence. This algorithm involves a recently developed process for chemical reaction optimization and two adjustment operators (turning and mutation operators). Three types of mutation operators (uniform, nonuniform, and polynomial) were combined with chemical reaction optimization and turning operator to find the most appropriate framework. The best solution among these three options was selected to be a hybrid mutation chemical reaction optimization algorithm for global numerical optimization. The optimal quality, convergence speed, and statistical hypothesis testing of our algorithm are superior to those previous high performance algorithms such as RCCRO, HP-CRO2, and OCRO.

Feature Selection Algorithm for High-dimensional Biomedical Data Using Information Gain and Improved Chemical Reaction Optimization

2021 ◽  
Vol 15 (8) ◽  
pp. 912-926
Author(s):  
Ge Zhang ◽  
Pan Yu ◽  
Jianlin Wang ◽  
Chaokun Yan
Keyword(s):  
Feature Selection ◽  
Chemical Reaction ◽  
Information Gain ◽  
Feature Selection Method ◽  
Search Space ◽  
Neighborhood Search ◽  
Biomedical Data ◽  
Chemical Reaction Optimization ◽  
Search Mechanism ◽  
Reaction Optimization

Background: There have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. However, these datasets usually involve thousands of features and include much irrelevant or redundant information, which leads to confusion during diagnosis. Feature selection is a solution that consists of finding the optimal subset, which is known to be an NP problem because of the large search space. Objective: For the issue, this paper proposes a hybrid feature selection method based on an improved chemical reaction optimization algorithm (ICRO) and an information gain (IG) approach, which called IGICRO. Methods: IG is adopted to obtain some important features. The neighborhood search mechanism is combined with ICRO to increase the diversity of the population and improve the capacity of local search. Results: Experimental results of eight public available data sets demonstrate that our proposed approach outperforms original CRO and other state-of-the-art approaches.

Sign in / Sign up

Export Citation Format

Share Document