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The AI-driven Drug Design (AIDD) platform: an interactive multi-parameter optimization system integrating molecular evolution with physiologically based pharmacokinetic simulations

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JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN 2024年第1期38卷 14-14页

作者： Jones, Jeremy Clark, Robert D. Lawless, Michael S. Miller, David W. Waldman, Marvin Simulat Plus Inc 42505 10th St West Lancaster CA 93534 USA Indiana Univ Luddy Sch Informat Comp & Engn 700 N Woodlawn Ave Bloomington IN 47408 USA

Computer-aided drug design has advanced rapidly in recent years, and multiple instances of in silico designed molecules advancing to the clinic have demonstrated the contribution of this field to medicine. Properly designed and implemented platforms can drastically reduce drug development timelines and costs. While such efforts were initially focused primarily on target affinity/activity, it is now appreciated that other parameters are equally important in the successful development of a drug and its progression to the clinic, including pharmacokinetic properties as well as absorption, distribution, metabolic, excretion and toxicological (ADMET) properties. In the last decade, several programs have been developed that incorporate these properties into the drug design and optimization process and to varying degrees, allowing for multi-parameter optimization. Here, we introduce the Artificial Intelligence-driven Drug Design (AIDD) platform, which automates the drug design process by integrating high-throughput physiologically-based pharmacokinetic simulations (powered by GastroPlus) and ADMET predictions (powered by ADMET Predictor) with an advanced evolutionary algorithm that is quite different than current generative models. AIDD uses these and other estimates in iteratively performing multi-objective optimizations to produce novel molecules that are active and lead-like. Here we describe the AIDD workflow and details of the methodologies involved therein. We use a dataset of triazolopyrimidine inhibitors of the dihydroorotate dehydrogenase from Plasmodium falciparum to illustrate how AIDD generates novel sets of molecules.

关键词： Artificial intelligence De novo drug design evolutionary algorithms Multi-parameter optimization Pareto optimization Pharmacokinetic simulation QSAR QSPR Molecular modeling Generative chemistry

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Multi-objective optimization of portfolio selection involving non-convex attributes in an anti-fragile perspective

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EVOLVING SYSTEMS 2024年第3期15卷 703-715页

作者： Gotardelo, Davi Goliatt, Leonardo UFRRJ DCAS Ave Pref Alberto Silva Lavinas 1847 BR-25802100 Tres Rios RJ Brazil Univ Fed Juiz de Fora Appl & Computat Mech Dept Rua Jose Lourenco Kelmer S-N BR-36036900 Juiz de Fora MG Brazil

This research aims to expand the portfolio selection horizon beyond the mean and variance metrics derived from the Markowitz model and widely used in CAPM. Although well built theoretically, it is well known that CAPM does not work empirically. Would there be market portfolios higher than the theoretical CAPM market portfolio? This study seeks to answer this question by initially optimizing purely convex attributes. In addition, this study proposes, in a pioneering way, beyond the higher order moments, an antifragile metric, called CVIX, which seeks to evaluate the conditional correlation in relation to the VIX (Volatility Index). Thus, this approach incorporates non-convex attributes through evolutionary algorithms, resulting in an empirical multi-objective optimization proposition involving convex and non-convex attributes. In-sample optimizations were applied in the US market and sample tested from 1994 to 2017. The results indicated that the optimization of purely convex attributes produces worse results than optimizations involving the Sharpe, Omega and the naive portfolio (1/n). On the other hand, tests using the antifragile metric and higher-order attributes presented superior results in all scenarios, indicating that investors can may take other attributes than the mean and variance in the assembly of their portfolios.

关键词： Portfolio selection evolutionary algorithms Antifragility Multiobjective optimization

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Interactive evolutionary Reoptimization for Groundfish Survey Planning 13th

Interactive Evolutionary Reoptimization for Groundfish Surv...

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13th International Conference on evolutionary Multi-Criterion Optimization, EMO 2025

作者： Runarsson, Thomas Philip School of Engineering and Natural Sciences University of Iceland Reykjavik Iceland

ISBN: (纸本)9789819635054

This study introduces an interactive evolutionary algorithm (EA) for optimizing path planning in groundfish surveys. The approach employs interactive reoptimization to iteratively refine plans by adjusting constraints and incorporating new information, addressing the limitations of traditional optimization models and including real-world factors not initially captured. Developed with input from experienced surveyors, the method provides a practical way to refine survey plans at sea, where fast optimization is crucial. We compare the EA’s performance with a mixed integer programming model applied to a groundfish survey in Iceland involving four vessels, aiming to find the shortest route while considering vessel capacity and port unloading. Results show the EA’s potential in complex marine surveys, highlighting its advantages in interactive optimization and sustainable fisheries management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： evolutionary algorithms interactive reoptimization planning

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Enhancing Feature Selection in High-Dimensional Data With Fuzzy Fitness-Integrated Memetic algorithms

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IEEE ACCESS 2024年 12卷 130675-130692页

作者： Reddy, Keerthi Gabbi Mishra, Deepasikha VIT AP Univ Sch Comp Sci & Engn Amaravati 522237 Andhra Pradesh India

In environments rich in data, machine learning models often encounter challenges such as data sparsity and overfitting, primarily due to datasets with an excessive number of features. To address these issues, this paper introduces a novel feature selection method employing a Memetic Algorithm (MA) enhanced with a fuzzy fitness function. This method is articulated in three variations: the Fuzzy Fitness Memetic Algorithm with Tabu Search (FFMATS), the Fuzzy Fitness Memetic Algorithm with Hill Climbing (FFMAHC), and a hybrid that combines both techniques, each utilizing specific local search strategies to refine feature selection. When tested across 16 UCI datasets using four different classifiers, these algorithms not only demonstrated competitive accuracy but frequently outperformed existing methods. These results highlight the critical importance of customizing feature selection strategies to meet the specific needs of various datasets and classifiers, ultimately enhancing the practicality and effectiveness of machine learning models.

关键词： Classification algorithms Rough sets Machine learning algorithms Genetic algorithms Feature extraction Uncertainty evolutionary computation Memetics evolutionary algorithms feature selection fuzzy fitness functions local search algorithms machine learning memetic algorithms

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A hybridized approach for minimizing energy in cloud computing

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第1期27卷 53-70页

作者： Kak, Sanna Mehraj Agarwal, Parul Alam, M. Afshar Siddiqui, Farheen Jamia Hamdard Dept Comp Sci & Engn Hamdard Nagar New Delhi 110062 India

Traditional scheduling techniques are designed to reduce processing times while disregarding energy costs. One way of lowering energy usage is to implement scheduling strategies that distribute tasks to specified resources, which influence the processing time and power usage. Among the primary objectives to be achieved in cloud computing, power and energy consumption of the cloud environment have become issues due to ecological and economic reasons. Despite the existence of research efforts from the past pertaining to the same topic, an ideal solution to this problem has not yet been found. One of the main drawbacks of utilizing cloud computing is that the cloud data centers hosting cloud computing applications use higher volumes of energy, adding to the increased functioning cost and carbon footprint in the environment, which in turn increases the need for energy-efficient systems. In this research work, a hybrid optimization algorithm is presented, which is intended to minimize energy in the cloud computing environment. Thus, the crow search algorithm (CSA) and the sparrow search algorithm (SSA) are combined to obtain the proposed hybrid model. The hybrid approach achieves the optimal position in the shortest period of time and with the least amount of energy, load, and makespan, hence improving system performance. The experiments were conducted using three setups with different task sizes. The analysis while using the task size = 300 shows that the proposed method improved QoS, Resource Utilization (RU) and decreased makespan, energy usage, and load at a rate of 0.5073, 4.4035, 0.0331, and 0.0014, respectively.

关键词： Carbon footprint Cloud service provider Crow search algorithm Dynamic scheduling Energy consumption Energy-aware scheduling evolutionary algorithms Green cloud computing Load Makespan Multi-heuristics Multi-cloud Optimization Quality of service Resource allocation Resource utilization Sparrow search algorithm Sustainable Task scheduling

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An Opposition-Based Learning-Based Search Mechanism for Flying Foxes Optimization Algorithm

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Computers, Materials & Continua 2024年第6期79卷 5201-5223页

作者： Chen Zhang Liming Liu Yufei Yang Yu Sun Jiaxu Ning Yu Zhang Changsheng Zhang Ying Guo Software College Northeastern UniversityShenyang110169China School of Information Science and Engineering Shenyang Ligong UniversityShenyang110159China China Telecom Digital Intelligence Technology Co. Ltd.Beijing100035China College of Computer Science and Engineering Ningxia Institute of Science and TechnologyShizuishan753000China

The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave *** preferentially selects the best-performing *** tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search *** address this issue,the paper introduces an opposition-based learning-based search mechanism for FFO algorithm(IFFO).Firstly,this paper introduces niching techniques to improve the survival list method,which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search ***,an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its ***,to verify the superiority of the improved search mechanism,IFFO,FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test *** results prove that compared with other algorithms,IFFO is characterized by its rapid convergence,precise results and robust stability.

关键词： Flying foxes optimization(FFO)algorithm opposition-based learning niching techniques swarm intelligence metaheuristics evolutionary algorithms

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Automated design of CNN architecture based on efficient evolutionary search

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NEUROCOMPUTING 2022年 491卷 160-171页

作者： Xie, Yirong Chen, Hong Ma, Yongjie Xu, Yang Northwest Normal Univ Coll Phys & Elect Engn Lanzhou 730070 Peoples R China

evolutionary Neural Architecture Search (ENAS) is a promising method for the automated design of deep network architecture, which has attracted extensive attention in the field of automated machine learning. However, the existing ENAS methods often need a lot of computing resources to design CNN architecture automatically. In order to achieve efficient and automated design of CNNs, this paper focuses on two aspects to improve efficiency. On the one hand, efficient CNN-based building blocks are introduced to ensure the effectiveness of the generated architectures and a triplet attention mechanism is incorporated into the architectures to further improve the classification performance. On the other hand, a random forest-based performance predictor is used in the fitness evaluation to reduce the amount of computation required to train each individual from scratch. Experimental results show that the proposed algorithm can significantly reduce the computational resources required and achieve competitive classification performance on the CIFAR dataset. Also, the architecture designed for the traffic sign recognition task exceeds the accuracy of manual expert design.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Convolutional neural networks Neural Architecture Search evolutionary algorithms

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On self-adaptive stochastic ranking in decomposition many-objective evolutionary optimization

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NEUROCOMPUTING 2022年 489卷 547-557页

作者： Li, Li Li, Guangpeng Chang, Liang Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China

evolutionary multi-objective optimization problems have attracted increasingly attention in the evolutionary computing community. Now a lot of efforts have been devoted to this direction. For example, the proposed Pareto-dominated, decomposition-based and indicator-based methods have improved the scalability of multi-objective evolutionary algorithms (MOEAs). However, with the increase of the number of objectives, the portion of non-dominated individuals in the population is too large, and the distinguishability of individuals in the objective space decreases, which will affect the selection of elite solutions, thereby losing the balance of convergence and diversity. In this paper, a self-adaptive stochastic ranking method (SSR) is proposed to adaptively balance the convergence and diversity in high dimensional space according to the state of the population. It has been embedded into the MOEA/D framework to form a novel multi-objective optimization algorithm, named MOEA/D-SSR. In addition, an improved shift-based density estimation strategy (ISDE) is adopted to enhance the convergence and diversity. Compared with the existing MOEAs on benchmark suite DTLZ and WFG with up to 10 objectives, the performance of the our algorithm has been verified. Experimental results show that the proposed algorithm is competitive compared with the most advanced MOEAs.(c) 2021 Elsevier B.V. All rights reserved.

关键词： evolutionary algorithms Many-objective optimization MOEA D Self-adaptive stochastic ranking

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Maximizing chitin and chitosan recovery yields from Fusarium verticillioides using a many-factors-at-a-time approach

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INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES 2024年第Pt 1期282卷 136708页

作者： Arbia, Wassila Amar, Mohamed Kouider Adour, Lydia Amrane, Abdeltif Natl Polytech Sch Res Unit Engn & Environm URIE Algiers 16000 Algeria Univ Yahia Fares Medea Medea 26000 Algeria Univ Yahia Fares Medea Fac Technol Lab Biomat & Transport Phenomena LBMPT Medea 26000 Algeria Univ Algiers Fac Sci Dept Chem Algiers 16000 Algeria

The extraction of chitin and chitosan presents challenges due to the complexity of the process and the influence of many variables. This study aimed to optimize chitin and chitosan extraction from Fusarium verticillioides by analyzing many additives and processing variables and modeling their yields using multiple linear regression (MLR) and evolutionary algorithms. FT-IR analysis confirmed the presence of characteristic bands in the extracted samples, and SEM analysis further revealed the microfibrillar appearance of the chitin and the dense, non-porous structure of the chitosan. The Ant Lion Optimizer (ALO) was employed to select significant factors and optimize model parameters. A transformation was applied to capture nonlinear relationships, and the finetuned models showed improved predictive power, with p-values of 0.00203 for chitin and 0.00884 for chitosan. Multi-objective optimization (MOO) using the Adaptive Geometry Estimation-based Multi-Objective evolutionary Algorithm (AGE-MOEA) further identified significant factors for optimal yields, achieving 3 g of Arginine, 100 ml of culture medium volume, 7 to 11 days of incubation time, 0.2 to 1.76 ml of Oligochitin, 1.4 g of FeSO4, 1.5 g of K2HPO4, and 1 g of NaCl. Therefore, the integration of ALO and AGE-MOEA algorithms effectively modeled and optimized chitin and chitosan yields by maximizing biopolymer recovery, enabling significant industrial exploitation.

关键词： Chitin Chitosan evolutionary algorithms

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10 MW FOWT Semi-Submersible Multi-Objective Optimization: A Comparative Study of PSO, SA, and ACO

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ENERGIES 2024年第23期17卷 5914页

作者： Drabo, Souleymane Lai, Siqi Liu, Hongwei Feng, Xiangheng Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China Zhejiang Univ Ocean Acad Zhoushan 316021 Peoples R China Zhejiang Windey Co Ltd Hangzhou 310012 Peoples R China Zhejiang Key Lab Wind Power Generat Technol Hangzhou 310012 Peoples R China

The present study aims to carry out a comparative Multi-Objective Optimization (MOO) of a 10 MW FOWT semi-submersible using three different metaheuristic optimization techniques and a sophisticated approach for optimizing a floating platform. This novel framework enables highly efficient 3D plots, an optimization loop, and the automatic and comparative output of solutions. Python, the main interface, integrated PyMAPDL and Pymoo for intricate modeling and simulation tasks. For this case study, the ZJUS10 Floating Offshore Wind Turbine (FOWT) platform, developed by the state key laboratory of mechatronics and fluid power at Zhejiang University, was employed as the basis. Key criteria such as platform stability, overall structural mass, and stress were pivotal in formulating the objective functions. Based on a preliminary study, the three metaheuristic optimization algorithms chosen for optimization were Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Ant Colony Optimization (ACO). Then, the solutions were evaluated based on Pareto dominance, leading to a Pareto front, a curve that represents the best possible trade-offs among the objectives. Each algorithm's convergence was meticulously evaluated, leading to the selection of the optimal design solution. The results evaluated in simulations elucidate the strengths and limitations of each optimization method, providing valuable insights into their efficacy for complex engineering design challenges. In the post-processing phase, the performances of the optimized FOWT platforms were thoroughly compared both among themselves and with the original model, resulting in validation. Finally, the ACO algorithm delivered a highly effective solution within the framework, achieving reductions of 19.8% in weight, 40.1% in pitch, and 12.7% in stress relative to the original model.

关键词： renewable energy Floating Offshore Wind Turbine metaheuristic evolutionary algorithms Particle Swarm Optimization ant colony analysis Simulated Annealing

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