Quantum Snowflake Algorithm (QSA): A Snowflake-Inspired, Quantum-Driven Metaheuristic for Large-Scale Continuous and Discrete Optimization with Application to the Traveling Salesma... by Zeki Oralhan, Burcu Oralhan

“…The Quantum Snowflake Algorithm (QSA) is a novel metaheuristic for both continuous and discrete optimization problems, combining collision-based diversity, quantum-inspired tunneling, superposition-based partial solution sharing, and local refinement steps. …”
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Multi-Objective Optimization Algorithm Based Bidirectional Long Short Term Memory Network Model for Optimum Sizing of Distributed Generators and Shunt Capacitors for Distribution S... by Amarendra Alluri, Srinivasa Rao Gampa, Balaji Gutta, Mahesh Babu Basam, Kiran Jasthi, Nibir Baran Roy, Debapriya Das

“…The stochastic traits of evolutionary computing methods necessitate running the algorithm repeatedly to confirm the global optimum. …”
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Effective Facial Expression Recognition System Using Artificial Intelligence Technique by Imad S. Yousif, Tarik A. Rashid, Ahmed S. Shamsaldin, Sabat A. Abdulhameed, Abdulhady Abas Abdullah

“…As the complexities surrounding these relationships have become better understood, it has allowed us to develop increasingly more complex systems for identifying and detecting facial expressions of different emotions. This paper presents an improved performance of the Facial Expression Recognition (FER) systems via augmentation in Artificial Neural Networks and Genetic Algorithms, two renowned artificial intelligence techniques possessing disparate strengths. …”
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LHS-GA Based H-Infinity Control for Robust Airfoil Flutter Suppression by Malek Rekik, Omar Khaled, Karolos Grigoriadis, Matthew A. Franchek

“…The approach utilizes a heuristic evolutionary optimization Genetic Algorithm (GA) combined with Latin Hypercube Sampling (LHS) to synthesize a gain scheduling controller for a four-degree-of-freedom aeroelastic airfoil model, accounting for uncertainties and nonlinearities. …”
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Exploring Procedural Content Generation of Environments for Virtual Museums: A Mixed-Initiative Approach by Claudio Rubio, Nicolas A. Barriga, Ben Ingram, Huizilopoztli Luna-García, Felipe Besoain

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Q-learning-based exponential distribution optimizer with multi-strategy guidance for solving engineering design problems and robot path planning by Fengbin Wu, Shaobo Li, Junxing Zhang, Liya Yu, Xuan Xiong, Libang Wu

“…Finally, a Q-learning-based switching parameter adaptive mechanism improves the adaptability of the algorithm under different conditions. The QMEDO’s performance was verified on the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC) 2020 and 2022 test functions. …”
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Modeling of Common Mode Interference of Secondary Side of Potential Transformer in VFTO Excitation by PANG Fubin, JI Jianfei, WU Xianqiang, ZHOU Mengxia, YAN Wei, XU Dong

“…Finally, a VFTO interference model for substation is established based on attenuated damping oscillation waves and a method for extracting the model parameters is proposed based on difference evolutionary algorithm. The experimental results show that predictions made by the model are basically consistent with the measured results with a coefficient of determination (R2) is 0.85. …”
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Bionic search for transportation problem solution on the basis of adaptation strategy by Yury Olegovich Chernyshev, Anna Yuryevna Poluyan, Pavel Alexandrovich Panasenko, Denis Yuryevich Paskevich

“…In this regard, in order to reduce the algorithm time complexity (ATC), the development of sequential and parallel bionic algorithms for solving the transportation problems based on the evolutionary strategies is prospective. …”
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A Multi-Objective Bio-Inspired Optimization for Voice Disorders Detection: A Comparative Study by Maria Habib, Victor Vicente-Palacios, Pablo García-Sánchez

“…This paper introduces a multi-objective bio-inspired, AI-based optimization approach for the automated detection of voice disorders. Different multi-objective evolutionary algorithms (the Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA-II), and the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D)) have been compared to detect voice disorders by optimizing two conflicting objectives: error rate and the number of features. …”
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Dynamic and Heterogeneous Identity-Based Cooperative Co-Evolution for Distributed Lot-Streaming Flowshop Scheduling Problem by Juan Wang, Guanghui Zhang, Xiaoling Li, Yanxiang Feng

“…Because individuals with different identities require different evolutionary mechanisms to fully unleash their respective potentials, identity-specific evolutionary operators are devised to evolve them in a cooperative co-evolutionary way. …”
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Topology optimization of the plate using neural networks by Yu. S. Selivanov, K. A. Matveev

“…The main ones are the method of solid isotropic material with Penalization (SIMP), methods of unidirectional and bidirectional evolutionary optimization (Evolutionary topology optimization and Bi-directional evolutionary topology optimization – ESO/BESO) and the LevelSet method. …”
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Multilevel Thresholding Segmentation Based on Harmony Search Optimization by Diego Oliva, Erik Cuevas, Gonzalo Pajares, Daniel Zaldivar, Marco Perez-Cisneros

“…Different to other evolutionary algorithms, HSA exhibits interesting search capabilities still keeping a low computational overhead. …”
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Optimizing Ontology Alignment through Improved NSGA-II by Yikun Huang, Xingsi Xue, Chao Jiang

“…In this study, for further enhancing the performance of NSGA-II, we propose to incorporate into NSGA-II’s evolutionary process the monkey king evolution algorithm (MKE) as the local search algorithm. …”
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Concentric Circular Antenna Array Synthesis Using Biogeography Based Optimization by Urvinder Singh, Tara Kamal

“…The results obtained by BBO are compared with the Real coded Genetic Algorithm (RGA), Craziness based Particle Swarm Optimization (CRPSO) and Hybrid Evolutionary Programming (HEP). …”
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Implementation of a Parallel Protein Structure Alignment Service on Cloud by Che-Lun Hung, Yaw-Ling Lin

“…This service includes a protein structure alignment algorithm, a refinement algorithm, and a MapReduce programming model. …”
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An Enhanced Comprehensive Learning Particle Swarm Optimizer with the Elite-Based Dominance Scheme by Chengcheng Chen, Xianchang Wang, Helong Yu, Nannan Zhao, Mingjing Wang, Huiling Chen

“…When it comes to the particle swarm optimization (PSO), the comprehensive learning PSO (CLPSO) is a well-established evolutionary algorithm that introduces a comprehensive learning strategy (CLS), which effectively boosts the efficacy of the PSO. …”
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Predicting the diversity of photosynthetic light-harvesting using thermodynamics and machine learning. by Callum Gray, Samir Chitnavis, Tamara Buja, Christopher D P Duffy

“…Using a generalized thermodynamic model of light-harvesting, coupled with an evolutionary algorithm, we predict the type of light-harvesting structures that might evolve in light of different intensities and spectral profiles. …”
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Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction Data by A. Gaspar-Cunha, G. Recio, L. Costa, C. Estébanez

“…In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved. …”
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An Artificial Intelligence-Based Hybrid Approach to Detect the Type of Buried Objects with Broad Frequency Band Antenna Systems by Ebru Efeoğlu

“…Genetic algorithm (GA), Particle Swarm Optimization (PSO), Harmony search (HA), and Evolutionary search (EA), which are among the metaheuristic optimization algorithms, were preferred as search methods in attribute selection with CFS. …”
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Multiobjective Optimization Design of a Fractional Order PID Controller for a Gun Control System by Qiang Gao, Jilin Chen, Li Wang, Shiqing Xu, Yuanlong Hou

“…With assistance of the evolutionary algorithm, the optimal solution for the specified problem is selected. …”
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