School of Electronics and Communication Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel). 2023 Jan 5;23(2):606. doi: 10.3390/s23020606.
The flower pollination algorithm (FPA) is a novel heuristic optimization algorithm inspired by the pollination behavior of flowers in nature. However, the global and local search processes of the FPA are sensitive to the search direction and parameters. To solve this issue, an improved flower pollination algorithm based on cosine cross-generation differential evolution (FPA-CCDE) is proposed. The algorithm uses cross-generation differential evolution to guide the local search process, so that the optimal solution is achieved and sets cosine inertia weights to increase the search convergence speed. At the same time, the external archiving mechanism and the adaptive adjustment of parameters realize the dynamic update of scaling factor and crossover probability to enhance the population richness as well as reduce the number of local solutions. Then, it combines the cross-generation roulette wheel selection mechanism to reduce the probability of falling into the local optimal solution. In comparing to the FPA-CCDE with five state-of-the-art optimization algorithms in benchmark functions, we can observe the superiority of the FPA-CCDE in terms of stability and optimization features. Additionally, we further apply the FPA-CCDE to solve the robot path planning issue. The simulation results demonstrate that the proposed algorithm has low cost, high efficiency, and attack resistance in path planning, and it can be applied to a variety of intelligent scenarios.
授粉算法（FPA）是一种新颖的启发式优化算法，灵感来自自然界中花朵的授粉行为。然而，FPA 的全局和局部搜索过程对搜索方向和参数敏感。为了解决这个问题，提出了一种基于余弦交叉世代差分进化的改进授粉算法（FPA-CCDE）。该算法使用交叉世代差分进化来指导局部搜索过程，从而达到最优解，并设置余弦惯性权重以增加搜索收敛速度。同时，外部归档机制和参数的自适应调整实现了缩放因子和交叉概率的动态更新，增强了种群丰富度，减少了局部解的数量。然后，它结合交叉世代轮盘选择机制来降低陷入局部最优解的概率。在与五个最先进的基准函数优化算法的 FPA-CCDE 比较中，可以观察到 FPA-CCDE 在稳定性和优化特征方面的优越性。此外，我们进一步将 FPA-CCDE 应用于解决机器人路径规划问题。仿真结果表明，该算法在路径规划方面具有低成本、高效率和抗攻击性，可以应用于多种智能场景。
