适应度相关优化器（FDO）算法最近于2019年推出。本文提出了一种改进的FDO（IFDO）算法，该算法大大提高了原FDO解决复杂优化问题的能力。为了改进FDO，IFDO计算对齐和内聚，然后根据FDO更新其位置的速度使用这些行为。此外，在确定权重时，FDO使用权重因子（wf），其在大多数情况下为零，仅在少数情况下为一。相反，IFDO在[0-1]范围内执行wf随机化，然后在实现更好的适应度权重值时最小化该范围。在这项工作中，IFDO算法和它的方法收敛到最优解的证明。另外，利用19个经典的标准测试函数组对IFDO算法进行测试，并选取FDO算法和粒子群算法（PSO）、遗传算法（DA）、遗传算法（GA）等3种常用算法对IFDO算法的性能进行评价。利用IEEE Congress of演化计算基准测试函数集CECC 06 2019 Competition对IFDO进行测试，并选取FDO和三种最新算法（salp swarm algorithm，SSA）、DA和whale optimization algorithm（WOA）对IFDO结果进行评价。结果表明，IFDO在某些情况下是实用的，并且在大多数情况下其结果得到了改善。最后，为了证明IFDO的实用性，它被用于实际应用。
英文摘要：
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.
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VŠB-Technical University of Ostrava（俄斯特拉发理工大学）；Torrens University Australia（澳大利亚托伦斯大学）；Nanjing University of Information Science and Technology（南京信息工程大学）
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Institute of Artificial Intelligence Application, Ningbo Polytechnic（宁波职业技术学院人工智能应用研究所）；Zhicheng College, Fuzhou University（福州大学至诚学院）；School of Artificial Intelligence, Ningbo Polytechnic（宁波职业技术学院人工智能学院）；Faculty of Electronic Engineering & Technology, University Malaysia Perlis（马来西亚玻璃市大学电子工程技术学院）；School of Electrical Automation and Information Engineering, Tianjin University（天津大学电气自动化与信息工程学院）
