Evolutions in Particle Swarm Optimization: Benchmark on Continuous Cases

Abstract

This chapter aims to present the results of developing a novel swarm optimization method that responds to the need of using multidimensional parameters. This meta-heuristic optimization approach is inspired by the ecological system of animals and their hierarchical relationship. Animal Food Chain in the nature is known to have three groups: herbivores (plant eaters), omnivores (both plant and meat eaters) and carnivores (meat eaters). In the food pyramid, the number of herbivores is higher than the number of omnivores which is higher than the number of carnivores in ratios depending on the environment. Furthermore, herbivores are known to be slowest and carnivores are known to be the fastest of the chain. These features are represented by conditional and multidimensional parameters in the Foraging Search algorithm. Tests are run on continuous and non-linear benchmark problems with various numbers of constraints. Cross validations of the results are realized by comparison of classical and Predator–Prey based Particle Swarm algorithms. Test results emphasize the power of the Foraging Search as an optimization tool.