Nature-inspired optimization is one of the most prevalent research domains with a confounding history that fascinates the research communities. Particle Swarm Optimization is one of the well-known optimizers that belongs to the family of nature-inspired algorithms. It often suffers from premature convergence leading to a local optimum. To address this, several methods were presented using different network topologies of the particles, but either lacked accuracy or were slow. To solve these problems, an improved version of the Directed Weighted Complex Network Particle Swarm Optimization using the Genetic Algorithm (GDWCN-PSO) is presented. This method uses the concept of the Genetic Algorithm after each update to enhance convergence and diversity. Since most of the real-world applications and complex optimization problems involve more than one objective function so to suit this problem, a multiobjective version of GDWCN-PSO is also proposed and validated on standard benchmarks. To demonstrate its applicability in real-world applications, GDWCN-PSO is applied to solve the optimal key-based medical image encryption. It is one of the most challenging problems in health IoTs for protecting sensitive and confidential patient data as well as addressing the major concern of integrity and security of data in today’s advanced digital world.

中文翻译：

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一种新的多目标GDWCN-PSO算法及其在医疗数据安全中的应用

受自然启发的优化是最流行的研究领域之一，有着令研究界着迷的令人困惑的历史。粒子群优化是著名的优化器之一，属于自然启发算法家族。它经常遭受导致局部最优的过早收敛。为了解决这个问题，使用不同的粒子网络拓扑提出了几种方法，但要么缺乏准确性，要么速度慢。为了解决这些问题，改进版的使用遗传算法的定向加权复杂网络粒子群优化 (GDWCN-PSO)被呈现。该方法在每次更新后使用遗传算法的概念来增强收敛性和多样性。由于大多数实际应用和复杂的优化问题都涉及多个目标函数，因此为了解决这个问题，还提出了 GDWCN-PSO 的多目标版本并在标准基准上进行了验证。为了证明其在实际应用中的适用性，GDWCN-PSO 用于解决基于最佳密钥的医学图像加密。它是健康物联网中最具挑战性的问题之一，用于保护敏感和机密的患者数据，以及解决当今先进数字世界中数据完整性和安全性的主要问题。