Accelerating Fireworks Algorithm with Weight-Based Guiding Sparks
Accelerating Fireworks Algorithm with Weight-Based Guiding Sparks
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DOI：
10.1007/978-3-030-26369-0_24
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Yuhao Li;Jun Yu;H. Takagi;Ying Tan
中科院分区：
文献类型：
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作者：
Yuhao Li;Jun Yu;H. Takagi;Ying Tan
我们引入了两种策略的引导烟花算法（GFWA），以进一步提高其性能，生成一个或多个基于权重的指导火花个人（S）为每个烟花个人。第一种策略根据每个烟花个体下的火花个体的适应度为火花个体分配不同的权重，然后计算一个或多个引导向量，以引导烟花个体向潜在方向进化。第二策略基于烟花个体的进化动态地决定基于重量的引导火花个体的数量，即，如果烟花个体没有进化并在下一代中生存，则第二策略减少在烟花个体周围生成的火花个体的数量，并且另外生成相同的减少数量的基于重量的引导火花个体。我们设计了一个对照实验，使用CEC 2013基准测试功能的五个不同的维度来评估我们的建议的性能。实验结果表明，所提策略能够提供有效的引导信息，显著提高GFWA的性能，且对高维任务的加速效果更为明显。
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 firework individual. The first strategy assigns different weights to spark individuals under each firework 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 CEC 2013 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.