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Aiming at the problems that SSA is prone to fall into local optimal and relatively low accuracy during search iteration. This paper proposed an enhanced sparrow search algorithm with multiple strategies (ESSA). At first, ESSA applied Lévy flight and cloud based adaptive weights to refine the original discoverers’ position update equation, which expanded the search range and enriched population diversity of the algorithm. Secondly, ESSA adopted a fuzzy control based adaptive lens imaging strategy to get the reversed position of the current optimal position to enhance the algorithm’s ability to jump out of local optimal. This paper selected 12 test functions from CEC2017 testbed as benchmark to evaluate the performance of ESSA with standard SSA, and other 4 improved sparrow algorithms: ISSA, MSSSA, HSSA, and SHSSA. Experiment result shows that ESSA can achieve the supreme results among evaluated algorithms. This paper also applied ESSA to the 3D UAV path planning problem. The simulation result illustrates that ESSA can also find the supreme 3D paths for UAV. [ABSTRACT FROM AUTHOR]

In the Multi-objective evolutionary algorithm, the solution of the rank distribution takes up most of the computation time. According to the Pareto dominance relationship, this paper researched the solution of the rank and distribution, and found that there are some redundancy operation of rank and allocation in the classic methods. The classic algorithms need to rank all solutions, and select the next generation after sort, so it caused some unnecessary sort operations. To reduce the redundancy, this paper introduced a fast method of non-dominated sorting. The proposed algorithm only dealed the individual which belong the highest level in current population, and meanwhile it could choose individual into the next generation during the process of distribution level. When the next generation individuals were selected enough, the program was ended. This method reduced the number of the sorting of individuals, and also reduced the time complexity greatly. Then, it used three objectives optimization problem as an example, combined the fast non-dominated sorting method with the differential evolution algorithm, and proposed a fast three objective differential evolution algorithm based on non-dominated sorting. Finally, it compared this method with the famous ranking method and ENS/BNS method. The simulation results show that the proposed algorithm in time complexity and convergence speed is better than the classical method, and is not worse than the ENS/BNS algorithm. On the performance of the standard test function DTLZ1-DTLZ6, the proposed method is similar to or better than the comparison algorithms. The experiment verifies the correctness and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]