The heuristic algorithms are often used to find solutions to real complex world problems. In this paper, the Social Spider Algorithm (SSA) and Social Spider Optimization (SSO) which are heuristic algorithms created upon spider behaviors are considered. Performances of both algorithms are compared with each other from six different items. These are; fitness values of spider population which are obtained in different dimensions, number of candidate solution obtained in each iteration, the best value of candidate solutions obtained in each iteration, the worst value of candidate solutions obtained in each iteration, average fitness value of candidate solutions obtained in each iteration and running time of each iteration. Obtained results of SSA and SSO are applied to the Wilcoxon signed-rank test. Various unimodal, multimodal, and hybrid standard benchmark functions are studied to compare each other with the performance of SSO and SSA. Using these benchmark functions, performances of SSO and SSA are compared with well-known evolutionary and recently developed methods in the literature. Obtained results show that both heuristic algorithms have advantages to another from different aspects. Also, according to other algorithms have good performance.

启发式算法通常用于寻找现实复杂世界问题的解决方案。在本文中，社会蜘蛛算法（SSA）和社会蜘蛛优化（SSO）是基于蜘蛛行为创建的启发式算法。两种算法的性能从六个不同的项目相互比较。这些都是; 不同维度得到的蜘蛛种群适应度值，每次迭代得到的候选解的个数，每次迭代得到的候选解的最佳值，每次迭代得到的候选解的最差值，得到的候选解的平均适应值在每次迭代和每次迭代的运行时间。将 SSA 和 SSO 获得的结果应用于 Wilcoxon 符号秩检验。各种单峰、多峰、和混合标准基准功能进行研究，以相互比较与 SSO 和 SSA 的性能。使用这些基准函数，将 SSO 和 SSA 的性能与文献中众所周知的进化方法和最近开发的方法进行比较。得到的结果表明，两种启发式算法在不同方面都各有优势。另外，根据其他算法也有不错的表现。