A new improved krill herd algorithm for global numerical optimization

2014 ◽  
Vol 138 ◽  
pp. 392-402 ◽  
Author(s):  
Lihong Guo ◽  
Gai-Ge Wang ◽  
Amir H. Gandomi ◽  
Amir H. Alavi ◽  
Hong Duan
Keyword(s):  
Numerical Optimization ◽  
Global Numerical Optimization ◽  
Krill Herd Algorithm ◽  
Krill Herd

scholarly journals Erratum to: Incorporating mutation scheme into krill herd algorithm for global numerical optimization

2013 ◽  
Vol 24 (5) ◽  
pp. 1231-1231 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo ◽  
Heqi Wang ◽  
Hong Duan ◽  
Luo Liu ◽  
...  
Keyword(s):  
Numerical Optimization ◽  
Global Numerical Optimization ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Mutation Scheme

Incorporating mutation scheme into krill herd algorithm for global numerical optimization

2012 ◽  
Vol 24 (3-4) ◽  
pp. 853-871 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo ◽  
Heqi Wang ◽  
Hong Duan ◽  
Luo Liu ◽  
...  
Keyword(s):  
Numerical Optimization ◽  
Global Numerical Optimization ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Mutation Scheme

Adaptive Krill Herd Algorithm for Global Numerical Optimization

2017 ◽  
pp. 517-525 ◽  
Author(s):  
Indrajit N. Trivedi ◽  
Amir H. Gandomi ◽  
Pradeep Jangir ◽  
Arvind Kumar ◽  
Narottam Jangir ◽  
...  
Keyword(s):  
Numerical Optimization ◽  
Global Numerical Optimization ◽  
Krill Herd Algorithm ◽  
Krill Herd

A chaotic particle-swarm krill herd algorithm for global numerical optimization

Kybernetes ◽  
2013 ◽  
Vol 42 (6) ◽  
pp. 962-978 ◽  
Author(s):  
Gai-Ge Wang ◽  
Amir Hossein Gandomi ◽  
Amir Hossein Alavi
Keyword(s):  
Numerical Optimization ◽  
Particle Swarm ◽  
Global Numerical Optimization ◽  
Krill Herd Algorithm ◽  
Krill Herd

A Multi-Stage Krill Herd Algorithm for Global Numerical Optimization

2016 ◽  
Vol 25 (02) ◽  
pp. 1550030 ◽  
Author(s):  
Gai-Ge Wang ◽  
Amir H. Gandomi ◽  
Amir H. Alavi ◽  
Suash Deb
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Method ◽  
Fine Tuning ◽  
Benchmark Problems ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Multi Stage ◽  
Krill Herd

A multi-stage krill herd (MSKH) algorithm is presented to fully exploit the global and local search abilities of the standard krill herd (KH) optimization method. The proposed method involves exploration and exploitation stages. The exploration stage uses the basic KH algorithm to select a good candidate solution set. This phase is followed by fine-tuning a good candidate solution in the exploitation stage with a focused local mutation and crossover (LMC) operator in order to enhance the reliability of the method for solving global numerical optimization problems. Moreover, the elitism scheme is introduced into the MSKH method to guarantee the best solution. The performance of MSKH is verified using twenty-five standard and rotated and shifted benchmark problems. The results show the superiority of the proposed algorithm to the standard KH and other well-known optimization methods.

An Effective Krill Herd Algorithm for Numerical Optimization

2016 ◽  
Vol 9 (7) ◽  
pp. 127-138
Author(s):  
Songwei Huang ◽  
Lifang He ◽  
Xu Si ◽  
Yuanyuan Zhang ◽  
Pengyu Hao
Keyword(s):  
Numerical Optimization ◽  
Krill Herd Algorithm ◽  
Krill Herd
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