Department of Civil Engineering, University of Tabriz, Tabriz, Iran.
Department of Civil Engineering, Near East University, Nicosia, Cyprus.
Sci Rep. 2023 Apr 1;13(1):5373. doi: 10.1038/s41598-023-32465-z.
In this paper, Squid Game Optimizer (SGO) is proposed as a novel metaheuristic algorithm inspired by the primary rules of a traditional Korean game. Squid game is a multiplayer game with two primary objectives: attackers aim to complete their goal while teams try to eliminate each other, and it is usually played on large, open fields with no set guidelines for size and dimensions. The playfield for this game is often shaped like a squid and, according to historical context, appears to be around half the size of a standard basketball court. The mathematical model of this algorithm is developed based on a population of solution candidates with a random initialization process in the first stage. The solution candidates are divided into two groups of offensive and defensive players while the offensive player goes among the defensive players to start a fight which is modeled through a random movement toward the defensive players. By considering the winning states of the players of both sides which is calculated based on the objective function, the position updating process is conducted and the new position vectors are produced. To evaluate the effectiveness of the proposed SGO algorithm, 25 unconstrained mathematical test functions with 100 dimensions are used, alongside six other commonly used metaheuristics for comparison. 100 independent optimization runs are conducted for both SGO and the other algorithms with a pre-determined stopping condition to ensure statistical significance of the results. Statistical metrics such as mean, standard deviation, and mean of required objective function evaluations are calculated. To provide a more comprehensive analysis, four prominent statistical tests including the Kolmogorov-Smirnov, Mann-Whitney, and Kruskal-Wallis tests are used. Meanwhile, the ability of the suggested SGOA is assessed through the cutting-edge real-world problems on the newest CEC like CEC 2020, while the SGO demonstrate outstanding performance in dealing with these complex optimization problems. The overall assessment of the SGO indicates that the proposed algorithm can provide competitive and remarkable outcomes in both benchmark and real-world problems.
在本文中，我们提出了一种新的元启发式算法——鱿鱼游戏优化器（SGO），该算法受到传统韩国游戏基本规则的启发。鱿鱼游戏是一种多人游戏，有两个主要目标：攻击者的目标是完成他们的任务，而团队则试图消灭对方。它通常在没有固定大小和尺寸指南的大型开阔场地上进行。这个游戏的游戏场通常呈鱿鱼形状，根据历史背景，它的大小似乎是标准篮球场的一半。该算法的数学模型是基于候选解决方案的群体开发的，在第一阶段采用随机初始化过程。候选解决方案分为攻击方和防守方两组，而攻击方在防守方中移动，开始战斗，这通过随机向防守方移动来建模。通过考虑双方玩家的获胜状态（根据目标函数计算），进行位置更新过程，并生成新的位置向量。为了评估所提出的 SGO 算法的有效性，我们使用了 25 个具有 100 维的无约束数学测试函数，以及其他六种常用的元启发式算法进行比较。对于 SGO 和其他算法，都进行了 100 次独立的优化运行，具有预定的停止条件，以确保结果的统计显著性。计算了平均值、标准差和所需目标函数评估的平均值等统计指标。为了提供更全面的分析，我们使用了四种著名的统计检验，包括柯尔莫哥洛夫-斯米尔诺夫检验、曼-惠特尼检验和克鲁斯卡尔-沃利斯检验。同时，通过最新的 CEC 中的前沿实际问题（如 CEC 2020）评估了所建议的 SGOA 的能力，而 SGO 在处理这些复杂的优化问题方面表现出色。对 SGO 的整体评估表明，该算法在基准问题和实际问题中都能提供有竞争力的显著结果。
