Towards finding an effective way of discrete problems solving: The Particle Swarm Optimization, Genetic Algorithm and linkage learning techniques hybrydization

Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are well known optimization tools. PSO advantage is its capability for fast convergence to the promising solutions. On the other hand GAs are able to process schemata thanks to the use of crossover operator. However, both methods have also their drawbacks - PSO may fall into the trap of preconvergence, while GA capability of fast finding locally optimal (or close to optimal) solutions seems low when compared to PSO. Relatively new, important research direction in the field of Evolutionary Algorithms is linkage learning. The linkage learning methods gather the information about possible gene dependencies and use it to improve their effectiveness. Recently, the linkage learning evolutionary methods were shown to be effective tools to solve both: theoretical and practical problems. Therefore, this paper proposes a PSO and GA hybrid, improved by the linkage learning mechanisms, dedicated to solve binary problems. The proposed method tries to combine the GA schema processing ability, linkage information processing and uses fast PSO convergence to quickly improve the quality of already known solutions.