Privacy-Enhanced Multitasking Particle Swarm Optimization Based on Homomorphic Encryption

evolutionary multitasking optimization (EMTO) is a new optimization paradigm proposed in the field of evolutionary computation in recent years. EMTO can solve several different optimization tasks simultaneously and facilitate superior convergence characteristics by transferring effective knowledge among the tasks. However, existing EMTO usually focuses only on facilitating convergence characteristics while neglecting the potential privacy leakage problem in the knowledge transfer between different tasks. The privacy leakage could result in considerable financial losses or severe reputation impairment, which may impede the development of EMTO in real-world applications. To solve the problem of privacy leakage in EMTO, this article proposes a privacy-enhanced multitasking particle swarm optimization algorithm. A knowledge transfer strategy with privacy preservation is designed based on homomorphic encryption by combining multitasking particle swarm optimization. In addition, an intertask knowledge transfer mechanism implemented in a low-dimensional subspace is introduced to reduce the extra computational burden caused by privacy preservation. Comprehensive experiments are conducted on synthetic and NAS problems to verify the effectiveness of the proposed method. According to the experimental results, the proposed method has remarkable advantages in privacy preservation compared to existing EMTO.