Adaptive Fireworks Algorithm

Author

Junzhi Li ; Shaoqiu Zheng ; Ying Tan

Author_Institution

Dept. of Machine Intell., Peking Univ., Beijing, China

fYear

2014

fDate

6-11 July 2014

Firstpage

3214

Lastpage

3221

Abstract

In this paper, firstly, the amplitude used in the Enhanced Fireworks Algorithm (EFWA) is analyzed and its lack of adaptability is revealed, and then the adaptive amplitude method is proposed where amplitude is calculated according to the already evaluated fitness of the individuals adaptively. Finally, the Adaptive Fireworks Algorithm (AFWA) is proposed, replacing the amplitude operator in EFWA with the new adaptive amplitude. Some theoretical analyses are made to prove the adaptive explosion amplitude a promising method. Experiments on CEC13´s 28 benchmark functions are also conducted in order to illustrate the performance and it turns out that the AFWA where adaptive amplitude is adopted outperforms significantly the EFWA and meanwhile the time consumed is not longer. Moreover, according to experimental results, AFWA performs better than the Standard Particle Swarm Optimization (SPSO).

Keywords

evolutionary computation; particle swarm optimisation; AFWA; CEC13; EFWA; SPSO; adaptive amplitude; adaptive fireworks algorithm; amplitude operator; enhanced fireworks algorithm; standard particle swarm optimization; Algorithm design and analysis; Evolutionary computation; Explosions; Next generation networking; Nickel; Sparks; Upper bound;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation (CEC), 2014 IEEE Congress on

Conference_Location

Beijing

Print_ISBN

978-1-4799-6626-4

Type

conf

DOI

10.1109/CEC.2014.6900418

Filename

6900418