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 Social Network Search for Solving Engineering Optimization Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-32
Author(s):  
Hadi Bayzidi ◽  
Siamak Talatahari ◽  
Meysam Saraee ◽  
Charles-Philippe Lamarche
Keyword(s):  
Social Network ◽  
Optimization Problems ◽  
Computational Cost ◽  
Engineering Optimization ◽  
Optimal Solutions ◽  
Network Search ◽  
The Social ◽  
Metaheuristic Optimization Algorithm ◽  
Engineering Optimization Problems ◽  
Better Than

In this paper, a new metaheuristic optimization algorithm, called social network search (SNS), is employed for solving mixed continuous/discrete engineering optimization problems. The SNS algorithm mimics the social network user’s efforts to gain more popularity by modeling the decision moods in expressing their opinions. Four decision moods, including imitation, conversation, disputation, and innovation, are real-world behaviors of users in social networks. These moods are used as optimization operators that model how users are affected and motivated to share their new views. The SNS algorithm was verified with 14 benchmark engineering optimization problems and one real application in the field of remote sensing. The performance of the proposed method is compared with various algorithms to show its effectiveness over other well-known optimizers in terms of computational cost and accuracy. In most cases, the optimal solutions achieved by the SNS are better than the best solution obtained by the existing methods.

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