Scouting strategy for biasing fireworks algorithm search to promising directions
Scouting strategy for biasing fireworks algorithm search to promising directions
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DOI：
10.1145/3205651.3205740
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Jun Yu;Ying Tan;H. Takagi
中科院分区：
文献类型：
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作者：
Jun Yu;Ying Tan;H. Takagi
为了提高烟花算法的搜索能力，提出了一种搜索策略来寻找更好的搜索方向。它通过检查所生成的火花是否向上爬到更好的方向来逐个地从烟花个体生成火花个体，并且该过程继续，直到生成向下爬的火花个体，而规范FWA立即围绕烟花个体生成火花个体。我们可以知道潜在的搜索方向，从有意识地爬上火花的数量。除了这个策略之外，我们还使用了一个过滤策略来随机选择FWA，当它们的适应性比它们的父母差时，更差的火花被淘汰，即烟花，并且无法在下一代中生存。我们将这些策略与增强的FWA（EFWA）相结合，并使用28个CEC 2013基准函数进行评估。实验结果表明，所提出的策略是有效的，并表现出更好的性能，在收敛速度和精度。最后，我们分析了它们的适用性，并提供了一些开放的主题。
We propose a scouting strategy to find better searching directions in fireworks algorithm (FWA) to enhance its exploitation capability. It generates spark individuals from a firework individual one by one by checking if the generated spark climbs up to a better direction, and this process continues until spark individual climbing down is generated, while canonical FWA generates spark individuals around a firework individual at once. We can know potential search directions from the number of consciously climbing up sparks. Besides this strategy, we use a filtering strategy for a random selection of FWA, where worse sparks are eliminated when their fitness is worse than their parents, i.e. fireworks, and become unable to survive in the next generation. We combined these strategies with the enhanced FWA (EFWA) and evaluated using 28 CEC2013 benchmark functions. Experimental results confirm that the proposed strategies are effective and show better performance in terms of convergence speed and accuracy. Finally, we analyze their applicability and provide some open topics.