Attractive and Repulsive Fully Informed Particle Swarm Optimization based on the modified Fitness Model

A novel Attractive and Repulsive Fully Informed Particle Swarm Optimization based on the modified Fitness Model (ARFIPSOMF) is presented. In ARFIPSOMF, a modified fitness model is used as a self-organizing population structure construction mechanism. The population structure is gradually generated as the construction and the optimization processes progress asynchronously. An attractive and repulsive interacting mechanism is also introduced. The cognitive and the social effects on each particle are distributed by its ‘contextual fitness’ value \(F\). Two kinds of experiments are conducted. Results focusing on the optimization performance show that the proposed algorithm maintains stronger diversity of the population during the convergent process, resulting in good solution quality on a wide range of test functions, and converge faster. Moreover, the results concerning on topologic characteristics of the population structure indicate that (1) the final population structures developed by optimizing different test functions differ, which is an important for improving ARFIPSOMF performance, and (2) the final structures developed by optimizing some test functions exhibit scale-free property approximately.