The aim of this paper is to present two heuristics vaguely inspired from the evolution of star systems. These methods are simple and can produce a good solution in a short time. While for small dimensional search spaces they can work alone, for large dimensional spaces their results can be used as an initial solution for some other heuristics. We study the effects of the initial solutions generated with these methods on the local search heuristics and on a genetic algorithm. Experimental results show that good solutions can be obtained with a combination of these methods.
This paper presents an attempt to transform PSO into a self-adaptive algorithm based on specific swarm-inspired operators. New features are introduced: spatial expansion intended to overcome premature convergence (an algorithm called Improved PSO, IPSO) and auto-adaptation (an algorithm called Adaptive PSO, APSO). Experiments show that APSO and IPSO outperform the basic PSO on benchmark problems, proving their efficiency especially on multimodal functions.