This paper presents a computationally efficient and robust evolutionary algorithm to find the better permutation of weighting phase factors in minimizing envelope fluctuations of orthogonal frequency division multiplexing signals. The proposed optimization method is called the seasons algorithm, in which its main inspiration is the growth and survival of trees in nature. This algorithm formulates fluctuation reduction as an optimization problem. It is combined with the partial transmit sequence method to decreases both the large fluctuations of signals and the search cost for larger sub-blocks at the same time. The search complexity of the proposed hybrid algorithm is polynomial, while the complexity of the exhaustive search partial transmit sequence scheme increases exponentially with the number of sub-blocks. The proposed algorithm is evaluated using different benchmarks and compared with several counterpart methods according to the fluctuation reduction performance and search cost. The simulation results show that the proposed algorithm outperformed the existing optimization meta-heuristics in minimizing the envelop fluctuations.

本文提出了一种计算效率高且稳健的进化算法，以找到在最小化正交频分复用信号的包络波动方面的加权相位因子的更好排列。提出的优化方法称为季节算法，其主要灵感来自自然界中树木的生长和生存。该算法将波动减少公式化为优化问题。它与部分传输序列方法相结合，可以同时降低信号的大波动和更大子块的搜索成本。所提出的混合算法的搜索复杂度是多项式的，而穷举搜索部分传输序列方案的复杂度随着子块的数量呈指数增加。所提出的算法使用不同的基准进行评估，并根据波动减少性能和搜索成本与几种对应方法进行比较。仿真结果表明，所提出的算法在最小化包络波动方面优于现有的优化元启发式算法。