PSO and GA optimization methods comparison on simulation model of a real hexapod robot

The Szabad(ka)-II hexapod robot with 18 DOF is a suitable mechatronic device for the development of hexapod walking algorithm and engine control [1, 2]. The required full dynamic model has already been built [3], which is used as a black-box for the walking optimizations in this research. The ellipse-based walking trajectory has been generated that was required by the low-cost straight line walking [4], and the purpose was to optimize its parameters. The Particle Swarm Optimization (PSO) method was chosen for simple and effective working, which does not require the model´s mathematical description or differentiation. Previously the authors performed an evolutionary Genetic Algorithm (GA) optimization for a similar trial case [5], and posed the principles of the quality measurement of hexapod walking [4, 5]. The same visual evaluation and comparison was applied in this paper for the results of both optimization methods. PSO has produced better and faster results compared to GA.