Tuned parameters of algorithms. by Kaveh Faraji Googerdchi (18778727)

Scheduling time of five algorithms. by Huichao Guo (14515171)

“…A scheduling optimization model based on the particle swarm optimization (PSO) algorithm is proposed. …”

Convergence speed of five algorithms. by Huichao Guo (14515171)

“…A scheduling optimization model based on the particle swarm optimization (PSO) algorithm is proposed. …”

IEEE CEC 2017 benchmark function specifications. by Kankan Tang (21474968)

IEEE CEC 2022 benchmark function specifications. by Kankan Tang (21474968)

Improved Ant Colony Algorithm by AOBEI ZHANG (17657703)

“…To achieve this, we integrate the hyperbolic tangent function, fine-tuning the ACO algorithm's behavior to adaptively adjust its search strategy across iterations.(2) Recognizing the tendency of heuristic algorithms to converge prematurely into local optima, we devise a max-min ant colony strategy. …”

Visual depiction of the PCIA algorithm. by Kayson Fakhar (12890364)

Performance comparison of mainstream algorithms. by Junjie Lu (160350)

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. …”

Parameters of the genetic algorithm. by Jian Ma (170138)

Hyperparameter settings of the algorithm 1. by Jin Xu (31283)

“…Therefore, this paper presents a novel adaptive control structure for the Twin Delayed Deep Deterministic Policy Gradient algorithm, which is based on a reference trajectory model (TD3-RTM). …”

General idea of alternative structures of aptamers binding the same ligand and its specific implementation scheme for the BioRS function. by Alexander V. Spirov (11302085)

Algorithm flow of BBO. by Jian Yang (12136)

“…<div><p>The stable and site-specific operation of transmission lines is a crucial safeguard for grid functionality. This study introduces a comprehensive optimization design method for transmission line crossing frame structures based on the Biogeography-Based Optimization (BBO) algorithm, which integrates size, shape, and topology optimization. …”

Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart. by Gursimran Singh (575288)

Subjects:

Results of each algorithm. by Guilin Yang (583364)

“…<div><p>To address the issue of poor performance in the chimp optimization (ChOA) algorithm, a new algorithm called the manta ray-based chimpa optimization algorithm (MChOA) was developed. …”

Completion times for different algorithms. by Jianbin Zheng (587000)

“…In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. …”

The average cumulative reward of algorithms. by Jianbin Zheng (587000)

“…In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. …”

Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

The confusion matrix of the different algorithms. by Qiushi Wang (417314)

CEC2017 basic functions. by Tengfei Ma (597633)

“…<div><p>To address the limitations of the Zebra Optimization Algorithm (ZOA), including insufficient late-stage optimization search capability, susceptibility to local optima, slow convergence, and inadequate exploration, this paper proposes an enhanced Zebra Optimization Algorithm integrating opposition-based learning and a dynamic elite-pooling strategy (OP-ZOA: Opposition-Based Learning Dynamic Elite-Pooling Zebra Optimization Algorithm). he proposed search algorithm employs a good point set-elite opposition-based learning mechanism to initialize the population, enhancing diversity and facilitating escape from local optima. …”

Flowchart of improved FOX optimization algorithm. by Mahmood A. Jumaah (22272371)

“…Additionally, it achieved 880 wins, 228 ties, and 348 losses against 16 optimization algorithms across all involved functions and problems. …”