Stochastic Diffusion Optimisation: the application of partial function evaluation and stochastic recruitment in Swarm Intelligence optimisation

The concept of partial evaluation of ¯tness functions, together with mechanisms manipulating the resource allocation of population based search methods, are presented in the context of Stochastic Diffusion Search, a novel swarm intelligence metaheuristic that has many similarities with ant and evolutionary algorithms. It is demonstrated that the stochastic process ensuing from these algorithmic concepts has properties that allow the algorithm to optimise noisy fitness functions, to track moving optima, and to redistribute the population after quantitative changes in the fitness function. Empirical results are used to validate theoretical arguments