A Novel Multiobjective GDWCN-PSO Algorithm and its Application to Medical Data Security

Nature-inspired optimization is one of the most prevalent research domains with a confounding history that fascinates the research communities. Particle Swarm Optimization (PSO) is one of the well-known optimizer 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, a improved version of the Directed Weighted Complex Network Particle Swarm Optimization using Genetic Algorithm (GDWCN-PSO) is presented. This method uses the concept of Genetic Algorithm after each iteration to enhance convergence and diversity. Since most of the real world applications and complex optimization problems involve more than one objective functions so to suit this problem a multi-objective 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 which is one of the most challenging problems in health IoTs for protecting sensitive and confidential patient data and addressing the major concern of integrity and security of data in today's advanced digital world.

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