This paper introduces a new stochastic bio-inspired optimization algorithm, denoted as seasons optimization (SO) algorithm. This algorithm is inspired by the growth cycle of trees in different seasons of a year. It is an iterative and population-based algorithm working with a population of initial solutions known as a forest. Each individual in the forest is referred to as a tree. Until the termination conditions are satisfied, the trees in the forest are updated to a new generation by applying four operators similar to the trees’ life cycles in nature: renew, competition, seeding, and resistance. These operators hopefully cause the trees to converge towards the global optimum of the optimization problem. The effectiveness of the proposed SO algorithm is evaluated using multi-variable single-objective test problems and compared with several well-known baseline and state-of-the-art algorithms. The results show that the proposed algorithm outperformed its counterparts in terms of solution quality and finding the global optimum on most benchmark functions.

中文翻译：

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季节优化算法

本文介绍了一种新的随机仿生优化算法，称为季节优化（SO）算法。该算法的灵感来自于一年中不同季节树木的生长周期。它是一种迭代的基于种群的算法，处理称为森林的初始解种群。森林中的每个人都被称为一棵树。在满足终止条件之前，通过应用类似于自然界中树木生命周期的四个算子：更新、竞争、播种和抵抗，将森林中的树木更新为新一代。这些算子有望使树向优化问题的全局最优收敛。使用多变量单目标测试问题评估所提出的 SO 算法的有效性，并与几个众所周知的基线和最先进的算法进行比较。结果表明，所提出的算法在解决方案质量和在大多数基准函数上找到全局最优方面都优于其对应算法。