Modified Krill Herd Algorithm for Global Numerical Optimization Problems

2018 ◽  
pp. 205-221 ◽  
Author(s):  
Laith Mohammad Abualigah ◽  
Ahamad Tajudin Khader ◽  
Essam Said Hanandeh
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
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

Hybrid krill herd algorithm with differential evolution for global numerical optimization

2013 ◽  
Vol 25 (2) ◽  
pp. 297-308 ◽  
Author(s):  
Gai-Ge Wang ◽  
Amir H. Gandomi ◽  
Amir H. Alavi ◽  
Guo-Sheng Hao
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Global Numerical Optimization ◽  
Krill Herd Algorithm ◽  
Krill Herd

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

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

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.

ONE IMPROVED AGENT GENETIC ALGORITHM — RING-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION

2009 ◽  
Vol 26 (04) ◽  
pp. 479-502 ◽  
Author(s):  
BIN LIU ◽  
TEQI DUAN ◽  
YONGMING LI
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Genetic Operators ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Low Dimension ◽  
Multi Agent ◽  
Agent Structure

In this paper, a novel genetic algorithm — dynamic ring-like agent genetic algorithm (RAGA) is proposed for solving global numerical optimization problem. The RAGA combines the ring-like agent structure and dynamic neighboring genetic operators together to get better optimization capability. An agent in ring-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and they can also use knowledge to increase energies. Global numerical optimization problems are the most important ones to verify the performance of evolutionary algorithm, especially of genetic algorithm and are mostly of interest to the corresponding researchers. In the corresponding experiments, several complex benchmark functions were used for optimization, several popular GAs were used for comparison. In order to better compare two agents GAs (MAGA: multi-agent genetic algorithm and RAGA), the several dimensional experiments (from low dimension to high dimension) were done. These experimental results show that RAGA not only is suitable for optimization problems, but also has more precise and more stable optimization results.

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