Accelerating Fireworks Algorithm with Dynamic Population Size Strategy
Accelerating Fireworks Algorithm with Dynamic Population Size Strategy
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DOI：
10.1109/scisisis50064.2020.9322693
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Jun Yu;H. Takagi
中科院分区：
文献类型：
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作者：
Jun Yu;H. Takagi
针对Fireworks算法(FWA)提出了一种动态种群大小策略，以根据当前一代的搜索结果调整种群大小。当当前找到的最优个体被更新时，激活线性递减方法以保持有效的开发速度。人口规模减1，直到达到最小预设人口规模，然后保持人口规模不变。否则，我们随机产生一个比初始种群更大的种群规模，并人为地扩大所有烟花个体的爆炸幅度，这是我们可以逃脱当前局部极小值的期望。为了分析所提策略的有效性，我们将其与增强型FWA(EFWA)相结合，并对28个CEC 2013基准函数在三个不同维度上运行EFWA和(EFWA+我们提出的策略)。每个函数独立运行30次，并应用Wilcoxon符号等级检验来检验显著差异。统计结果表明，所提出的动态种群规模策略不仅可以获得更快的收敛速度，而且可以更容易地跳出陷入局部极小值的区域，从而保持较高的性能，特别是对于高维问题。
A dynamic population size strategy is proposed for the fireworks algorithm (FWA) to adjust the population size based to the search results of the current generation. When the currently found optimal individual is updated, a linear decreasing method is activated to maintain an efficient exploitation speed. The population size is reduced by 1 until the minimum preset population size is reached, then the population size remains unchanged. Otherwise, we randomly generate a larger population size than the initial population and expand the explosion amplitudes of all firework individuals artificially, which the expectation that we can escape current local minima. To analyze the effectiveness of the proposed strategy, we combined it with the enhanced FWA (EFWA) together, and run the EFWA and (the EFWA + our proposed strategy) on 28 CEC 2013 benchmark functions in three different dimensions. Each function is run 30 trial times independently, and the Wilcoxon signed-rank test is applied to check significant differences. The statistical results showed that the proposed dynamic population size strategy can not only achieve a faster convergence speed for the FWA but also can jump out of trapped local minima more easily to maintain a higher performance, especially for high-dimensional problems.