Expensive Multiobjective Optimization and Validation with a Robotics Application
نویسندگان
چکیده
Many practical optimization problems, especially in robotics, involve multiple competing objectives, e.g. performance metrics such as speed and energy efficiency. Proper treatment of these objective functions is often overlooked. Additionally, optimization of the performance of robotic systems can be restricted due to the expensive nature of testing control parameters on a physical system. This paper presents a multi-objective optimization (MOO) algorithm for expensive-toevaluate functions which generates a Pareto set of solutions. This algorithm is compared against another leading MOO algorithm, and then used to optimize the speed and head stability of the sidewinding gait for a snake robot.
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