Abstract
A bio-inspired is well known in a group of swarm intelligent. There are many researchers research algorithms to solve computation problems. This research is to develop foraging behavior improvement of primate swarm algorithm. The proposed algorithm is inspired by the behavior of the primate. In nature, primates live in wild as a small group for spreading foraging behavior and grown up mature primate creates new primate group. The primate who creates the group becomes a group leader to find other foraging. This research proposes adaptive K-mean algorithm for primate grouping to improve primate swarm intelligent for foraging behavior. A conventional primate swarm intelligent finds forage randomly which is an important problem of local search. Primate grouping is improved, simulates primate adaptation with adaptive K-mean technique for foraging behavior, number of new primate groups is gradually increase when forage is found. The results from this research are compared with PSA, CM-DNAGA, PSOCO, PSOTD algorithms. The proposed algorithm is tested with eight standard benchmark functions and most of which convergence to the optimal value.