  To construct a robot that can walk as efficiently and steadily as humans or
other legged animals, we develop an enhanced elitist-mutated ant colony
optimization~(EACO) algorithm with genetic and crossover operators in real-time
applications to humanoid robotics or other legged robots. This work presents
promoting global search capability and convergence rate of the EACO applied to
humanoid robots in real-time by estimating the expected convergence rate using
Markov chain. Furthermore, we put a special focus on the EACO algorithm on a
wide range of problems, from ACO, real-coded GAs, GAs with neural
networks~(NNs), particle swarm optimization~(PSO) to complex robotics systems
including gait synthesis, dynamic modeling of parameterizable trajectories and
gait optimization of humanoid robotics. The experimental results illustrate the
capability of this method to discover the premature convergence probability,
tackle successfully inherent stagnation, and promote the convergence rate of
the EACO-based humanoid robotics systems and demonstrated the applicability and
the effectiveness of our strategy for solving sophisticated optimization tasks.
We found reliable and fast walking gaits with a velocity of up to 0.47m/s using
the EACO optimization strategy. These findings have significant implications
for understanding and tackling inherent stagnation and poor convergence rate of
the EACO and provide new insight into the genetic architectures and control
optimization of humanoid robotics.
