Enhanced Particle Swarm Optimization for Automated Compatibility Testing in Cloud-based Distributed Systems

Ensuring software compatibility in cloud-based distributed systems presents significant challenges due to theheterogeneous nature of cloud environments and the complexity of distributed architectures. This paper proposes anenhanced particle swarm optimization (PSO) approach for automated compatibility testing that addresses the limitationsof traditional testing methods. The methodology integrates improved PSO algorithms with TLA+ formal verificationand Jepsen distributed testing frameworks, incorporating dispersion adjustment mechanisms to prevent prematureconvergence and enhance testing coverage. Key improvements include adaptive weight adjustment, collision radiusoptimization, and fitness function modification based on branch path coverage metrics. Experimental validationdemonstrates significant improvements in compatibility testing efficiency, coverage breadth, and system robustnessacross diverse cloud computing environments. The proposed approach effectively optimizes software performance andreliability while ensuring seamless operation of distributed systems in dynamic cloud infrastructures.