Nature-inspired optimization method: Hydrozoan algorithm for solving continuous problems

Abstract

In this article, a new optimization algorithm that is inspired by the biology of hydrozoa (HA) is proposed. Our aim was to develop an algorithm that is based on the regeneration and transplantation processes of hydrozoa for finding the best solutions for continuous optimization problems. Basically, HA follows the same general processes of evolutionary algorithm; however, its distinctive processes mimic the life cycle of 3 basic forms of hydrozoa: motile planula, polyps, and medusa. In particular, the growth of strong buds from the polyp stage depends on levels of morphogens: activators and inhibitors. These 3 forms develop or evolve into the best solution. HA was performance tested with 20 standard benchmark functions and compared with genetic algorithm and Particle Swarm Optimization (PSO). The test results have confirmed that the proposed algorithm is computationally more efficient than both GA and PSO. It works very well on most benchmark functions.