Hardware reconfigurable coding and evolution algorithm based on evolvable hardware by Ting WANG, Ju-long LAN, Jun-ting WU

“…A planar mapping function increments chromosome coding method based on FPGA platform with SRAM-architecture was proposed.The method could improve hardware reconfiguration efficiency by coding mapping realized by double platform mapping of binary configurable file string.Meanwhile,a betterment difference evolution algorithm was proposed based on local optimal mechanism introduced.The algorithm could promote convergence rate and whole efficiency.Finally,the result of the algorithm emulation shows that:MDE improves disadvantage of difference evolution algorithm with local optimal and approaches actual optimization outcome.…”
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Computational complexity, algorithmic scope, and evolution by Leonhard Sidl, Maximilian Faissner, Manuel Uhlir, Cristian A Velandia-Huerto, Maria Waldl, Hua-Ting Yao, Ivo L Hofacker, Peter F Stadler

“…Here we explore the idea that evolution confines biological computation to subsets of instances that can be solved efficiently with algorithms that are ‘hardcoded’ in the system itself. …”
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Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm by Sanglin Zhao, Zhetong Li, Hao Deng, Xing You, Jiaang Tong, Bingkun Yuan, Zihao Zeng

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Traffic Optimization Through Waiting Prediction and Evolutive Algorithms by Francisco García, Helena Hernández, María N. Moreno-García, Juan F. de Paz Santana, Vivian F. López, Javier Bajo

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Corrigendum: Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm by Sanglin Zhao, Zhetong Li, Hao Deng, Xing You, Jiaang Tong, Bingkun Yuan, Zihao Zeng

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Bayesian network structure learning algorithm based on hybrid binary salp swarm-differential evolution algorithm by Bin LIU, Ruixing FAN, Haoran LIU, Liyue ZHANG, Haiyu WANG, Chunlan ZHANG

“…Aiming at the disadvantages of Bayesian network structure learned by heuristic algorithms,which were trapping in local minimums and having low search efficiency,a method of learning Bayesian network structure based on hybrid binary slap swarm-differential evolution algorithm was proposed.An adaptive scale factor was used to balance local and global search in the swarm grouping stage.The improved mutation operator and crossover operator were taken into salp search strategy and differential search strategy respectively to renew different subswarms in the update stage.Two-point mutation operator was adopted to improve the swarm’s diversity in the stage of merging of subswarms.The convergence analysis of the proposed algorithm demonstrates that best structure can be found through the iterative search of population.Experimental results show that the convergence accuracy and efficiency of the proposed algorithm are improved compared with other algorithms.…”
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Multi-Strategy-Assisted Hybrid Crayfish-Inspired Optimization Algorithm for Solving Real-World Problems by Wenzhou Lin, Yinghao He, Gang Hu, Chunqiang Zhang

Subjects: “…crawfish optimization algorithm…”
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Different faces of the stethoscope: history, usefulness, evolution, contamination, and disinfection practices by Dorota Ochońska, Monika Brzychczy-Włoch, Katarzyna Talaga-Ćwiertnia

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A quasi affine transformation evolution algorithm with evolution matrix selection operation for parameter estimation of proton exchange membrane fuel cells by Mohammad Aljaidi, Pradeep Jangir, Sunilkumar P. Agrawal, Sundaram B. Pandya, Anil Parmar, Samar Hussni Anbarkhan, Laith Abualigah

“…It is challenging to find the best PEMFC parameters because the model is complex and the problem is nonlinear; not all optimization algorithms can solve this problem. This paper presents a new approach that applies QUasi-Affine TRansformation Evolution algorithm with a new adaptation of Evolution Matrix and Selection operation (QUATRE-EMS) to determine optimal values of uncertain parameters in PEMFC stack references. …”
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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 Optimized a Second-Order Divided Difference Particle Filter by Ting Cao, Huo-tao Gao, Chun-feng Sun, Guo-bao Ru

“…In order to improve the estimation accuracy of particle filter algorithm in a nonlinear system state estimation problem, a new algorithm based on the second-order divided difference filter to generate the proposed distribution and the differential evolution algorithm for resampling is proposed. …”
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Learning and Evolution: Factors Influencing an Effective Combination by Paolo Pagliuca

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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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Set-Based Differential Evolution Algorithm Based on Guided Local Exploration for Automated Process Discovery by Si-Yuan Jing

“…This paper proposes a hybrid evolutionary algorithm for automated process discovery, which consists of a set-based differential evolution algorithm and guided local exploration. …”
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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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