Stochastic Komodo Algorithm.

A novel metaheuristic algorithm is proposed in this paper, namely stochastic komodo algorithm (SKA). This proposed algorithm is an improved version of Komodo mlipir algorithm (KMA), which is inspired by the behaviour of Komodo during foraging and mating. The improvement is conducted by simplifying the basic form of KMA. Like KMA, it consists of three types of Komodo: big male, female, and small male. Male Komodo focuses on intensification. On the other side, females conduct diversification or intensification based on the search space radius. It eliminates sorting mechanism at the beginning of the iteration. Rather than determined from the quality (fitness score), the distribution of the types of Komodo is conducted stochastically at the beginning of every iteration. This proposed algorithm is then tested by using ten functions. Five functions are unimodal, while the five others are multimodal. The proposed algorithm is also compared with several well-known algorithms: football game-based optimization, hide objects game optimization, cloud-theory-based simulated annealing, harmony search, and KMA. The result shows that this proposed algorithm is very competitive compared with these benchmark algorithms in both unimodal and multimodal functions. A female-dominant formation is proven to achieve optimal result. [ABSTRACT FROM AUTHOR]