Particle Swarm Optimization inspired by r- and K-Selection in ecology

An optimization technique named r/KPSO (particle swarm optimization with r- and K-selection) was developed in this paper. In Ecology, two evolutionary ldquostrategiesrdquo are termed, r-selection for those species that breed many ldquocheaprdquo offspring and live in unstable environments and K-selection for those species that produce few ldquoexpensiverdquo offspring and live in stable environments. r-selection can be characterized as: quantitative, little parent care, large growth rate and rapid development and K-selection as: qualitative, much parent care, small growth rate and slow development. r/KPSO selects r- and K-selection to produce the progenies in the iterative procedure according to the concerned particlepsilas fitness value. K-selection is performed for those particles (K-subswarm called in this paper) in high fitness, and K-subswarm only can produce few progenies but the progenies are nurtured delicately with much parent care. On the other hand, r-selection is performed for those particles (r-subswarm called) in relatively low fitness. And with little parent care, r-subswarm can produce a large number of progenies, the progenies have to compete with the r-subswarm for survival according to fitness and only the best ones can survive. In r/KPSO, the particles performed r-selection mainly explore the search space as possible as they can to find more potential solutions in large speed, and those particles performed K-selection keep the current optimum solutions and exploit the space as they can to find more ideal solutions. Combined the advantages of r-selection and K-selection, r/KPSO can converge in higher speed and higher precision.