Compact algorithms are Estimation of Distribution Algorithms which mimic the behavior of population-based algorithms by means of a probabilistic representation of the population of candidate solutions. These algorithm...Compact algorithms are Estimation of Distribution Algorithms which mimic the behavior of population-based algorithms by means of a probabilistic representation of the population of candidate solutions. These algorithms have a similar behaviour with respect to population-based algorithms but require a much smaller memory. This feature is crucially important in some engineering applications, especially in robotics. A high performance compact algorithm is the compact Differential Evolution （cDE） algorithm. This paper proposes a novel implementation of cDE, namely compact Differential Evolution light （cDElight）, to address not only the memory saving necessities but also real-time requirements, cDElight employs two novel algorithmic modifications for employing a smaller computational overhead without a performance loss, with respect to cDE. Numerical results, carried out on a broad set of test problems, show that cDElight, despite its minimal hardware requirements, does not deteriorate the performance of cDE and thus is competitive with other memory saving and population-based algorithms. An application in the field of mobile robotics highlights the usability and advantages of the proposed approach.展开更多
文摘Compact algorithms are Estimation of Distribution Algorithms which mimic the behavior of population-based algorithms by means of a probabilistic representation of the population of candidate solutions. These algorithms have a similar behaviour with respect to population-based algorithms but require a much smaller memory. This feature is crucially important in some engineering applications, especially in robotics. A high performance compact algorithm is the compact Differential Evolution （cDE） algorithm. This paper proposes a novel implementation of cDE, namely compact Differential Evolution light （cDElight）, to address not only the memory saving necessities but also real-time requirements, cDElight employs two novel algorithmic modifications for employing a smaller computational overhead without a performance loss, with respect to cDE. Numerical results, carried out on a broad set of test problems, show that cDElight, despite its minimal hardware requirements, does not deteriorate the performance of cDE and thus is competitive with other memory saving and population-based algorithms. An application in the field of mobile robotics highlights the usability and advantages of the proposed approach.
