(μ, λ) evolutionary and particle swarm hybrid algorithm over cloud computing, with an application to dinosaur gait optimization

A hybrid evolutionary algorithm based on (μ, λ) evolutionary algorithms and particle swarm optimization is proposed for numerical optimization problems. In order to evaluate the performance of the hybrid, a computer experiment was conducted on a dinosaur´s gait generation problem. Experimental results show that hybrid optimization finds maximum fitness and is faster at the beginning of the search.