Behaviour of pseudo-random and chaotic sources of stochasticity in nature-inspired optimization methods

Stochasticity, noisiness, and ergodicity are the key concepts behind many natural processes and its modeling is an important part of their implementation. There is a handful of soft-computing methods that are directly inspired by nature or stochastic natural processes. The implementation of such a nature-inspired optimization and search methods usually depends on streams of integer and floating point numbers generated in course of their execution. The pseudo-random numbers are utilized for in-silico emulation of probability-driven natural processes such as arbitrary modification of genetic information (mutation, crossover), partner selection, and survival of the fittest (selection, migration) and environmental effects (small random changes in motion direction and velocity). Deterministic chaos is a well known mathematical concept that can be used to generate sequences of seemingly random real numbers within selected interval in a predictable and well controllable way. In the past, it has been used as a basis for various pseudo-random number generators (PRNGs) with interesting properties. This work provides an empirical comparison of the behavior of selected nature-inspired optimization algorithms using different PRNGs and chaotic systems as sources of stochasticity.