A Study of Global Numerical Maximization using Hybrid Chemical Reaction Algorithms

<p>Several approaches are proposed to solve global numerical optimization problems. Most of researchers have experimented the robustness of their algorithms by generating the result based on minimization aspect. In this paper, we focus on maximization problems by using several hybrid chemical...

Fuld beskrivelse

Saved in:
Bibliografiske detaljer
Main Authors: Ransikarn Ngambusabongsopa1 (Author), Vincent Havyarimana2 (Author), Zhiyong Li (Author)
Format: Bog
Udgivet: Trends in Computer Science and Information Technology - Peertechz Publications, 2017-03-07.
Fag:
Online adgang:Connect to this object online.
Tags: Tilføj Tag
Ingen Tags, Vær først til at tagge denne postø!

MARC

LEADER 00000 am a22000003u 4500
001 peertech__10_17352_tcsit_000004
042 |a dc 
100 1 0 |a Ransikarn Ngambusabongsopa1  |e author 
700 1 0 |a  Vincent Havyarimana2  |e author 
700 1 0 |a Zhiyong Li  |e author 
245 0 0 |a A Study of Global Numerical Maximization using Hybrid Chemical Reaction Algorithms 
260 |b Trends in Computer Science and Information Technology - Peertechz Publications,   |c 2017-03-07. 
520 |a <p>Several approaches are proposed to solve global numerical optimization problems. Most of researchers have experimented the robustness of their algorithms by generating the result based on minimization aspect. In this paper, we focus on maximization problems by using several hybrid chemical reaction optimization algorithms including orthogonal chemical reaction optimization (OCRO), hybrid algorithm based on particle swarm and chemical reaction optimization (HP-CRO), real-coded chemical reaction optimization (RCCRO) and hybrid mutation chemical reaction optimization algorithm (MCRO), which showed success in minimization. The aim of this paper is to demonstrate that the approaches inspired by chemical reaction optimization are not only limited to minimization, but also are suitable for maximization. Moreover, experiment comparison related to other maximization algorithms is presented and discussed.</p> 
540 |a Copyright © Ransikarn Ngambusabongsopa1 et al. 
546 |a en 
655 7 |a Research Article  |2 local 
856 4 1 |u https://doi.org/10.17352/tcsit.000004  |z Connect to this object online.