.png){kind=icon}
返回
Squid Game Optimizer (SGO): a novel metaheuristic algorithm
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-023-32465-z.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
ATOMIC ORBITAL SEARCH
META-HEURISTIC OPTIMIZATION
LEARNING-BASED OPTIMIZATION
DIFFERENTIAL EVOLUTION
GLOBAL OPTIMIZATION
DESIGN
CRYSTAL
COLONY
SHAPE
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
28.2W
被引数:
83.5W
机构
引用论文
Kinematic and kinetic differences in the execution of vertical jumps between people with good and poor ankle joint dorsiflexion踝关节背屈良好和不良的人在执行垂直跳跃时的运动学和动力学差异
PREAI
分享收藏An Archive-Based Multi-Objective Arithmetic Optimization Algorithm for Solving Industrial Engineering Problems解决工业工程问题的基于档案的多目标算术优化算法.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Multiobjective Atomic Orbital Search (MOAOS) for Global and Engineering Design Optimization用于全局和工程设计优化的多目标原子轨道搜索 (MOAOS)IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏An effective multi-objective artificial hummingbird algorithm with dynamic elimination-based crowding distance for solving engineering design problems一种求解工程设计问题的动态消去拥挤距离多目标人工蜂鸟算法
PREAI
分享收藏An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox DiseaseDIAGNOSTICSOAAI
DIAGNOSTICSIF3.3
OAAI分享收藏
查看更多内容