论文标题
改进的健身依赖性优化算法
Improved Fitness-Dependent Optimizer Algorithm
论文作者
论文摘要
与健身依赖性优化器（FDO）算法最近在2019年推出。在这项工作中提出了一种改进的FDO（IFDO）算法，该算法对完善原始FDO解决复杂优化问题的能力有很大贡献。为了改善FDO，IFDO计算了对齐和内聚力，然后将这些行为与FDO更新位置更新的步伐使用这些行为。此外，在确定权重时，FDO使用权重因子（WF），在大多数情况下，仅在少数情况下为零。相反，IFDO在[0-1]范围内执行WF随机化，然后在实现更好的健身重量值时最小化范围。在这项工作中，展示了IFDO算法及其在最佳解决方案上的收敛方法。此外，还利用了19个经典的标准测试功能组来测试IFDO，然后使用FDO和其他三种众所周知的算法，即粒子群算法（PSO），蜻蜓算法（DA）和遗传算法（GA），以评估IFDO结果。 Furthermore, the CECC06 2019 Competition, which is the set of IEEE Congress of Evolutionary Computation benchmark test functions, is utilized to test the IFDO, and then, the FDO and three recent algorithms, namely, the salp swarm algorithm (SSA), DA and whale optimization algorithm (WOA), are chosen to gauge the IFDO results.结果表明，在某些情况下，如果IFDO是实用的，并且在大多数情况下，其结果会得到改善。最后，为了证明IFDO的实用性，它用于现实世界应用程序。
The fitness-dependent optimizer (FDO) algorithm was recently introduced in 2019. An improved FDO (IFDO) algorithm is presented in this work, and this algorithm contributes considerably to refining the ability of the original FDO to address complicated optimization problems. To improve the FDO, the IFDO calculates the alignment and cohesion and then uses these behaviors with the pace at which the FDO updates its position. Moreover, in determining the weights, the FDO uses the weight factor (wf), which is zero in most cases and one in only a few cases. Conversely, the IFDO performs wf randomization in the [0-1] range and then minimizes the range when a better fitness weight value is achieved. In this work, the IFDO algorithm and its method of converging on the optimal solution are demonstrated. Additionally, 19 classical standard test function groups are utilized to test the IFDO, and then the FDO and three other well-known algorithms, namely, the particle swarm algorithm (PSO), dragonfly algorithm (DA), and genetic algorithm (GA), are selected to evaluate the IFDO results. Furthermore, the CECC06 2019 Competition, which is the set of IEEE Congress of Evolutionary Computation benchmark test functions, is utilized to test the IFDO, and then, the FDO and three recent algorithms, namely, the salp swarm algorithm (SSA), DA and whale optimization algorithm (WOA), are chosen to gauge the IFDO results. The results show that IFDO is practical in some cases, and its results are improved in most cases. Finally, to prove the practicability of the IFDO, it is used in real-world applications.
