Single particle algorithms for continuous optimization

This paper introduces two lightweight variants of ISPO, a Single Particle Optimization algorithm recently proposed in the literature. The goal of this work is to improve upon the performance of the original ISPO, still bearing in mind its admirable algorithmic simplicity. The first variant, namely ISPOrestart, combines in a memetic fashion the logics of ISPO with a partial restart mechanism similar to the binomial crossover typically used in Differential Evolution. The second variant, named VISPO, builds on top of the restart process a very simple learning stage which tries to adapt the algorithm behaviour to the (non)-separability of the problem. Numerical results obtained on three complete optimization benchmarks show that not only the two algorithms are able to improve, incrementally, upon the performance of ISPO, but also they show respectable performance in comparison with modern complex state-of-the-art methods, especially when the problem dimensionality increases.