Integration of Particle Swarm Optimization with Immune Algorithm

Integrating Particle Swarm Optimization (PSO) with Immune Algorithm to calculate optimal extreme values of functions. By merging these two algorithms, we can enhance the efficiency and accuracy of the optimization process. Particle Swarm Optimization is a heuristic optimization algorithm based on swarm behavior, simulating bird flocks or fish schools to search for function optima through iterative updates. Key implementation aspects include maintaining particle positions/velocities and updating using personal-best and global-best values. Immune Algorithm mimics biological immune systems, simulating antibody production, cloning, and selection processes to search for optimal solutions, typically involving affinity calculation and memory cell mechanisms. Combining these algorithms leverages their respective advantages: PSO provides efficient global exploration through swarm intelligence, while Immune Algorithm offers strong local search capabilities and diversity maintenance through immune mechanisms. The hybrid approach may implement cross-algorithm information sharing, such as using immune memory to guide particle initialization or employing PSO's velocity update to enhance antibody evolution. For function optimization extremes, this integration represents an effective strategy that balances exploration and exploitation, often implemented through phased execution or parallel cooperation frameworks with parameter synchronization mechanisms.