An enhanced hybrid and adaptive meta-model based global optimization algorithm for engineering optimization problems

2016 ◽  
Vol 59 (8) ◽  
pp. 1147-1155 ◽  
Author(s):  
Guan Zhou ◽  
LiBin Duan ◽  
WanZhong Zhao ◽  
ChunYan Wang ◽  
ZhengDong Ma ◽  
...  
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Engineering Optimization ◽  
Global Optimization Algorithm ◽  
Meta Model ◽  
Model Based ◽  
Engineering Optimization Problems

DE-PSO: A NEW HYBRID META-HEURISTIC FOR SOLVING GLOBAL OPTIMIZATION PROBLEMS

2011 ◽  
Vol 07 (03) ◽  
pp. 363-381 ◽  
Author(s):  
MILLIE PANT ◽  
RADHA THANGARAJ ◽  
AJITH ABRAHAM
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Real Life ◽  
Test Functions ◽  
Global Optimization Algorithm ◽  
Two Phase ◽  
Swarm Optimization ◽  
Life Problems

This paper presents a simple, hybrid two phase global optimization algorithm called DE-PSO for solving global optimization problems. DE-PSO consists of alternating phases of Differential Evolution (DE) and Particle Swarm Optimization (PSO). The algorithm is designed so as to preserve the strengths of both the algorithms. Empirical results show that the proposed DE-PSO is quite competent for solving the considered test functions as well as real life problems.

scholarly journals AN INTRODUCTION OF KRILL HERD ALGORITHM FOR ENGINEERING OPTIMIZATION

2015 ◽  
Vol 22 (3) ◽  
pp. 302-310 ◽  
Author(s):  
Amir H. GANDOMI ◽  
Amir H. ALAVI
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
State Of The Art ◽  
Engineering Optimization ◽  
Design Problems ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Metaheuristic Optimization Algorithm ◽  
Engineering Optimization Problems ◽  
Better Than

A new metaheuristic optimization algorithm, called Krill Herd (KH), has been recently proposed by Gandomi and Alavi (2012). In this study, KH is introduced for solving engineering optimization problems. For more verification, KH is applied to six design problems reported in the literature. Further, the performance of the KH algorithm is com­pared with that of various algorithms representative of the state-of-the-art in the area. The comparisons show that the results obtained by KH are better than the best solutions obtained by the existing methods.

scholarly journals Chaotic Charged System Search with a Feasible-Based Method for Constraint Optimization Problems

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
B. Nouhi ◽  
S. Talatahari ◽  
H. Kheiri ◽  
C. Cattani
Keyword(s):  
Global Optimization ◽  
Present Method ◽  
Chaotic Maps ◽  
Optimization Problems ◽  
Engineering Optimization ◽  
Constraint Optimization ◽  
Charged System Search ◽  
Charged System ◽  
Constraint Optimization Problems ◽  
Engineering Optimization Problems

Recently developed chaotic charged system search was combined to feasible-based method to solve constraint engineering optimization problems. Using chaotic maps into the CSS increases the global search mobility for a better global optimization. In the present method, an improved feasible-based method is utilized to handle the constraints. Some constraint design examples are tested using the new chaotic-based methods, and the results are compared to recognize the most efficient and powerful algorithm.

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