| 作成者 |
|
|
|
|
|
|
|
| 本文言語 |
|
| 発行日 |
|
| 会議情報 |
|
| 出版タイプ |
|
| アクセス権 |
|
| 概要 |
We introduce two strategies into the guided fireworks algorithm (GFWA) to further improve its performance by generating one or more weight-based guiding spark individual(s) for each rework individual.... The first strategy assigns different weights to spark individuals under each rework individual according to their _fitness and then calculates one or more guiding vector(s) to guide the firework individual to evolve into potential directions. The second strategy decides the number of weight-based guiding spark individuals dynamically based on the evolution of a firework individual, i.e. if a firework individual does not evolve and survive in the next generation, then the second strategy reduces the number of spark individuals generated around the firework individual and generates the same reduced number of weight based guiding spark individuals additionally. We design a controlled experiment to evaluate the performance of our proposal using CEC2013 benchmark functions with five different dimensions. The experiment results confirm that the proposed strategies can provide effective guidance information to improve the GFWA performance significantly, and its acceleration effect for higher dimensional tasks is more obvious.続きを見る
|
Mendeley出力
