DRaCOon: a novel algorithm for pathway-level differential co-expression analysis in transcriptomics by Fernando M. Delgado-Chaves, Ferdinand Spurny, Tanja Laske, Mhaned Oubounyt, Jan Baumbach

“…However, a key challenge is the lack of robust methods and benchmarks specifically for evaluating algorithms’ ability to identify disrupted gene-gene associations across conditions. …”
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State information-driven surrogate-assisted differential evolution for computationally expensive constrained optimization problems by Zihua Zhu, Zan Yang, Zhiyong Liu, Liming Chen, Xiwen Cai

“…Secondly, different ensemble mutation and environmental selection operations are tailored specially for subpopulations where both an inner evolution-driven parent expansion and update rate-based surrogate switch strategies are designed to regulate the search ability of the algorithm. …”
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A Memetic Differential Evolution Algorithm Based on Dynamic Preference for Constrained Optimization Problems by Ning Dong, Yuping Wang

“…The constrained optimization problem (COP) is converted into a biobjective optimization problem first, and then a new memetic differential evolution algorithm with dynamic preference is proposed for solving the converted problem. …”
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Design of Fully Digital Controlled Shaped Beam Synthesis Using Differential Evolution Algorithm by D. Mandal, A. Chatterjee, A. K. Bhattacharjee

“…The optimum 4-bit amplitudes generated by four-bit digital attenuators and 5-bit phases generated by 5-bit digital phase shifters are computed using Differential Evolution (DE) Algorithm. To illustrate the effectiveness of DE, the two beam patterns with specified characteristics are computed from the same array using Particle Swarm Optimization (PSO) algorithm and Genetic algorithm (GA) by finding out optimum discrete excitations among the elements. …”
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Cosmic Evolution Optimization: A Novel Metaheuristic Algorithm for Numerical Optimization and Engineering Design by Rui Wang, Zhengxuan Jiang, Guowen Ding

“…This study proposes a novel metaheuristic algorithm, Cosmic Evolution Optimization (CEO), for numerical optimization and engineering design. …”
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Optimization of Rendering Parameters of Cesium 3DTiles Model Based on Differential Evolution Algorithm by Doujun Zhang, Yong Wu, Youcong Ni, Tinghuang Zhang, Chenxiang Gao

“…In this paper, we proposed a multi-strategy probabilistic discrete differential evolution algorithm (MSPDDE) for finding the optimal values of the rendering parameters of Cesium 3DTiles model, which increases the search space and improves the convergence speed by introducing multiple mutation strategies. …”
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Differential Evolution with Novel Mutation and Adaptive Crossover Strategies for Solving Large Scale Global Optimization Problems by Ali Wagdy Mohamed, Abdulaziz S. Almazyad

“…This paper presents Differential Evolution algorithm for solving high-dimensional optimization problems over continuous space. …”
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Assessment method on protection effectiveness of differential privacy algorithms based on fuzzy influence diagram by TIAN Yuechi, LI Fenghua, ZHOU Zejun, SUN Zhe, GUO Shoukun, NIU Ben

“…Formalization link was proposed to solve the problem of completely opposite algorithms with idential evaluation results. Comparative experiments on electricity-carbon analysis model demonstrate the proposed method can assess the protection effectiveness of differential privacy algorithms effectively. …”
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Optimizing Combined Heat and Power Economic Dispatch Using a Differential Evolution Algorithm by Mahender K., Rao Gundala Srinivasa, Kamalakar Gangula, Venkateshwarlu S., Polasa Swathi, Pulluri Harish

“…The results acquired are compared with other algorithms to evaluate the effectiveness of the DE algorithm. …”
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An Experimental Comparison of Self-Adaptive Differential Evolution Algorithms to Induce Oblique Decision Trees by Rafael Rivera-López, Efrén Mezura-Montes, Juana Canul-Reich, Marco-Antonio Cruz-Chávez

“…This study addresses the challenge of generating accurate and compact oblique decision trees using self-adaptive differential evolution algorithms. Although traditional decision tree induction methods create explainable models, they often fail to achieve optimal classification accuracy. …”
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A hybrid differential evolution particle swarm optimization algorithm based on dynamic strategies by Huarong Xu, Qianwei Deng, Zhiyu Zhang, Shengke Lin

“…To address this issue, we propose a hybrid differential evolution (DE) particle swarm optimization algorithm based on dynamic strategies (MDE-DPSO). …”
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Improve Particle Swarm Optimization and Differential Evolution Algorithms Using Nash Bargaining Theory by Marziye Dadvar, Hamidreza Navidi, Hamid Haj Seyyed Javadi, Mitra Mirzarezaee

“…To evaluate the performance of the proposed algorithm, 25 benchmark functions are used in terms of the CEC2005 structure. …”
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A thermodynamic inspired AI based search algorithm for solving ordinary differential equations by V. Murugesh, M. Priyadharshini, T. R. Mahesh, Esmael Adem Esleman

“…In this paper, we introduce a new search algorithm called Thermodynamic Inspired Search Algorithm (TSA) for approximate solution to linear ODEs (LODEs), nonlinear ODEs (NLODEs) and systems of ODEs (SODEs). …”
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Learning and Evolution: Factors Influencing an Effective Combination by Paolo Pagliuca

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Performance Evaluation of Metaheuristics for LQR Controller Optimization: A Two-Wheel Balancing Robot Case Study by Jose Ricardo Rivera-Ruiz, Ricardo Rojas-Galvan, Jose R. Garcia-Martinez, Edson Eduardo Cruz Miguel, Omar A. Barra-Vazquez, Maria Ines Cruz Orduna, Jesus Enrique Escalante-Martinez, Juvenal Rodriguez-Resendiz

“…The performance of each algorithm is evaluated through a comparative analysis, focusing on control stability and dynamic responsiveness. …”
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Evolution of Algorithms and Applications for Unmanned Surface Vehicles in the Context of Small Craft: A Systematic Review by Luis Castano-Londono, Stefany del Pilar Marrugo Llorente, Edwin Paipa-Sanabria, María Belén Orozco-Lopez, David Ignacio Fuentes Montaña, Daniel Gonzalez Montoya

“…This study was developed based on three research questions about the evolution of research topics, areas of application, and types of algorithms related to USVs. …”
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An adaptive differential evolution algorithm using fitness distance correlation and neighbourhood-based mutation strategy by Wei Li, Yafeng Sun, Ying Huang, Jianbing Yi

“…Differential evolution (DE), as an extremely powerful evolutionary algorithm, has recently been widely employed within complex reality optimisation problems. …”
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Learning-Driven Algorithm With Dual Evolution Patterns for Solving Large-Scale Multiobjective Optimization Problems by Mingshuo Song, Wei Song, Khin Wee Lai

“…In this paper, we propose a learning-driven algorithm with dual evolution patterns (DEPLA) for solving LSMOPs. …”
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An Improved Human Evolution Optimization Algorithm for Unmanned Aerial Vehicle 3D Trajectory Planning by Xue Wang, Shiyuan Zhou, Zijia Wang, Xiaoyun Xia, Yaolong Duan

“…Second, recognizing the sensitivity of population diversity to Logistic Chaotic Mapping in a traditional Human Evolution Optimization Algorithm (HEOA), an opposition-based learning strategy is employed to uniformly initialize the population distribution, thereby enhancing the algorithm’s global optimization capability. …”
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Result aggregation algorithm based on differential evolution and Top-k ranking in learning Worker’s weight by Yuping XING, Yongzhao ZHAN

“…To solve the problem of quickly obtaining the optimal ranking result in the crowdsourcing result aggregation, an efficient and effective aggregation algorithm of Worker’s weight was proposed.The Worker’s weight optimization model based on differential evolution algorithm focused on the uncertainties and differences of Workers completing ranking tasks, the uncertainties and differences were reflected in the objective function and constraint conditions of the model.This model obtained the optimal weight of candidate results, and maximized the matching between Worker’s weight and result performance.Then, the optimization model solving method based on Top-k ranking was proposed to quickly obtain the optimal Worker’s weight with the appropriate k value for specific multi-data items ranking scenario.The optimization of Worker’s weight could realize optimized performance and speed of the result aggregation.The correctness of the algorithm is verified by qualitative analysis, the effectiveness and efficiency of the algorithm is verified by the simulation results, and the comparison with the relevant algorithms shows the optimal comprehensive performance of the algorithm.…”
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