Cuckoo Search (CS) algorithm is a newly proposed meta-heuristic optimization algorithm inspired by natural phenomena. It received wide attention due to its powerful global searching capability and fast convergence speed. The Multi-Objective Cuckoo Search (MOCS) algorithm is a multi-objective optimization algorithm developed on the basis of the single-objective cuckoo search which can directly obtain a set of Pareto solutions. Aiming at alleviating the shortcomings of the original MOCS algorithm, a series of methodologies are introduced to improve the convergence accuracy, convergence speed, and distribution of the solutions:the fast non-dominated sorting and crowding distance are introduced to improve the fitness evaluation of solutions, a random walk strategy is modified to improve local search ability, an adaptive abandon probability strategy is used to improve the convergence speed, and an archive management mechanism is added to improve the uniformity of the distribution of the Pareto set. The results of the benchmark analytical multi-objective tests show that the improved MOCS algorithm has a faster convergence speed and higher convergence accuracy than the original MOCS as well as the NSGA-Ⅱ algorithm. Finally, taking the RAE2822 two-point lift-to-drag ratio maximization design as an example, the improved MOCS algorithm is applied to a multi-objective aerodynamic optimization problem. The results show that the improved MOCS algorithm can obtain 64 Pareto solutions. The aerodynamic performances of the optimized airfoils are significantly improved, and the designers can choose different Pareto solutions based on their own requirements. For the aerodynamic optimization problem, the convergence speed of improved MOCS algorithm is faster than MOCS and NSGA-Ⅱ algorithms.
ZHANG Xinshuai
,
LIU Jun
,
LUO Shibin
. An improved multi-objective cuckoo search algorithm for airfoil aerodynamic optimization design[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2019
, 40(6)
: 122550
-122550
.
DOI: 10.7527/S1000-6893.2018.22550
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