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Stroh KS, Souza PCT, Monticelli L, Risselada HJ. CGCompiler: Automated Coarse-Grained Molecule Parametrization via Noise-Resistant Mixed-Variable Optimization. J Chem Theory Comput 2023; 19:8384-8400. [PMID: 37971301 PMCID: PMC10688431 DOI: 10.1021/acs.jctc.3c00637] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 11/19/2023]
Abstract
Coarse-grained force fields (CG FFs) such as the Martini model entail a predefined, fixed set of Lennard-Jones parameters (building blocks) to model virtually all possible nonbonded interactions between chemically relevant molecules. Owing to its universality and transferability, the building-block coarse-grained approach has gained tremendous popularity over the past decade. The parametrization of molecules can be highly complex and often involves the selection and fine-tuning of a large number of parameters (e.g., bead types and bond lengths) to optimally match multiple relevant targets simultaneously. The parametrization of a molecule within the building-block CG approach is a mixed-variable optimization problem: the nonbonded interactions are discrete variables, whereas the bonded interactions are continuous variables. Here, we pioneer the utility of mixed-variable particle swarm optimization in automatically parametrizing molecules within the Martini 3 coarse-grained force field by matching both structural (e.g., RDFs) as well as thermodynamic data (phase-transition temperatures). For the sake of demonstration, we parametrize the linker of the lipid sphingomyelin. The important advantage of our approach is that both bonded and nonbonded interactions are simultaneously optimized while conserving the search efficiency of vector guided particle swarm optimization (PSO) methods over other metaheuristic search methods such as genetic algorithms. In addition, we explore noise-mitigation strategies in matching the phase-transition temperatures of lipid membranes, where nucleation and concomitant hysteresis introduce a dominant noise term within the objective function. We propose that noise-resistant mixed-variable PSO methods can both improve and automate parametrization of molecules within building-block CG FFs, such as Martini.
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Affiliation(s)
- Kai Steffen Stroh
- Department
of Physics, Technische Universität
Dortmund, 44227 Dortmund, Germany
- Institute
for Theoretical Physics, Georg-August University
Göttingen, 37077 Göttingen, Germany
| | - Paulo C. T. Souza
- Molecular
Microbiology and Structural Biochemistry (MMSB, UMR 5086), CNRS and University of Lyon, 69367 Lyon, France
| | - Luca Monticelli
- Molecular
Microbiology and Structural Biochemistry (MMSB, UMR 5086), CNRS and University of Lyon, 69367 Lyon, France
| | - Herre Jelger Risselada
- Department
of Physics, Technische Universität
Dortmund, 44227 Dortmund, Germany
- Institute
for Theoretical Physics, Georg-August University
Göttingen, 37077 Göttingen, Germany
- Leiden
Institute of Chemistry, Leiden University, 2333 CC Leiden, The Netherlands
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An evolutionary simulation-optimization approach for the problem of order allocation with flexible splitting rule in semiconductor assembly. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03701-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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3
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Integrating mutation operator into grasshopper optimization algorithm for global optimization. Soft comput 2021. [DOI: 10.1007/s00500-021-05752-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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4
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Wang X, Wang Y, Shi X, Gao L, Li P. A probabilistic multimodal optimization algorithm based on Buffon principle and Nyquist sampling theorem for noisy environment. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2020.107068] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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5
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Ali ZA, Han Z, Masood RJ. Collective Motion and Self-Organization of a Swarm of UAVs: A Cluster-Based Architecture. SENSORS 2021; 21:s21113820. [PMID: 34073061 PMCID: PMC8198933 DOI: 10.3390/s21113820] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 05/22/2021] [Accepted: 05/24/2021] [Indexed: 11/28/2022]
Abstract
This study proposes a collective motion and self-organization control of a swarm of 10 UAVs, which are divided into two clusters of five agents each. A cluster is a group of UAVs in a dedicated area and multiple clusters make a swarm. This paper designs the 3D model of the whole environment by applying graph theory. To address the aforesaid issues, this paper designs a hybrid meta-heuristic algorithm by merging the particle swarm optimization (PSO) with the multi-agent system (MAS). First, PSO only provides the best agents of a cluster. Afterward, MAS helps to assign the best agent as the leader of the nth cluster. Moreover, the leader can find the optimal path for each cluster. Initially, each cluster contains agents at random positions. Later, the clusters form a formation by implementing PSO with the MAS model. This helps in coordinating the agents inside the nth cluster. However, when two clusters combine and make a swarm in a dynamic environment, MAS alone is not able to fill the communication gap of n clusters. This study does it by applying the Vicsek-based MAS connectivity and synchronization model along with dynamic leader selection ability. Moreover, this research uses a B-spline curve based on simple waypoint defined graph theory to create the flying formations of each cluster and the swarm. Lastly, this article compares the designed algorithm with the NSGA-II model to show that the proposed model has better convergence and durability, both in the individual clusters and inside the greater swarm.
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Affiliation(s)
- Zain Anwar Ali
- School of Systems Science, Beijing Normal University, Zhuhai 519085, China
- Correspondence: (Z.A.A.); (Z.H.)
| | - Zhangang Han
- School of Systems Science, Beijing Normal University, Zhuhai 519085, China
- Correspondence: (Z.A.A.); (Z.H.)
| | - Rana Javed Masood
- Department of Electrical Engineering, Usman Institute of Technology, Karachi 75300, Pakistan;
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Taghiyeh S, Lengacher DC, Handfield RB. Loss rate forecasting framework based on macroeconomic changes: Application to US credit card industry. EXPERT SYSTEMS WITH APPLICATIONS 2021; 165:113954. [PMID: 32929309 PMCID: PMC7481134 DOI: 10.1016/j.eswa.2020.113954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Revised: 07/16/2020] [Accepted: 08/31/2020] [Indexed: 06/11/2023]
Abstract
A major part of the balance sheets of the largest U.S. banks consists of credit card portfolios. Hence, managing the charge-off rates is a vital task for the profitability of the credit card industry. Different macroeconomic conditions affect individuals' behavior in paying down their debts. In this paper, we propose an expert system for loss forecasting in the credit card industry using macroeconomic indicators. We select the indicators based on a thorough review of the literature and experts' opinions covering all aspects of the economy, consumer, business, and government sectors. The state of the art machine learning models are used to develop the proposed expert system framework. We develop two versions of the forecasting expert system, which utilize different approaches to select between the lags added to each indicator. Among 19 macroeconomic indicators that were used as the input, six were used in the model with optimal lags, and seven indicators were selected by the model using all lags. The features that were selected by each of these models covered all three sectors of the economy. Using the charge-off data for the top 100 US banks ranked by assets from the first quarter of 1985 to the second quarter of 2019, we achieve mean squared error values of 1.15E-03 and 1.04E-03 using the model with optimal lags and the model with all lags, respectively. The proposed expert system gives a holistic view of the economy to the practitioners in the credit card industry and helps them to see the impact of different macroeconomic conditions on their future loss.
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Wang X, Wang Y, Wu H, Gao L, Luo L, Li P, Shi X. Fibonacci multi-modal optimization algorithm in noisy environment. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2019.105874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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8
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An Enhanced Partial Search to Particle Swarm Optimization for Unconstrained Optimization. MATHEMATICS 2019. [DOI: 10.3390/math7040357] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Particle swarm optimization (PSO) is a population-based optimization technique that has been applied extensively to a wide range of engineering problems. This paper proposes a variation of the original PSO algorithm for unconstrained optimization, dubbed the enhanced partial search particle swarm optimizer (EPS-PSO), using the idea of cooperative multiple swarms in an attempt to improve the convergence and efficiency of the original PSO algorithm. The cooperative searching strategy is particularly devised to prevent the particles from being trapped into the local optimal solutions and tries to locate the global optimal solution efficiently. The effectiveness of the proposed algorithm is verified through the simulation study where the EPS-PSO algorithm is compared to a variety of exiting “cooperative” PSO algorithms in terms of noted benchmark functions.
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Li M, Zhang H, Chen B, Wu Y, Guan L. Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods. Sci Rep 2018; 8:3991. [PMID: 29507318 PMCID: PMC5838250 DOI: 10.1038/s41598-018-22332-7] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Accepted: 02/21/2018] [Indexed: 11/23/2022] Open
Abstract
The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained. The improved PSO algorithm was applied in the training of radial basis function artificial neural network (RBF ANN) model and the selection of molecular descriptors. A quantitative structure-activity relationship model based on RBF ANN trained by the improved PSO algorithm was proposed to predict the pKa values of 74 kinds of neutral and basic drugs and then validated by another database containing 20 molecules. The validation results showed that the model had a good prediction performance. The absolute average relative error, root mean square error, and squared correlation coefficient were 0.3105, 0.0411, and 0.9685, respectively. The model can be used as a reference for exploring other quantitative structure-activity relationships.
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Affiliation(s)
- Mengshan Li
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China.
| | - Huaijing Zhang
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Bingsheng Chen
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Yan Wu
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Lixin Guan
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
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Zhang S, Xu J, Lee LH, Chew EP, Wong WP, Chen CH. Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION : A PUBLICATION OF THE IEEE NEURAL NETWORKS COUNCIL 2017; 21:206-219. [PMID: 29170617 PMCID: PMC5695081 DOI: 10.1109/tevc.2016.2592185] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Particle Swarm Optimization (PSO) is a popular metaheuristic for deterministic optimization. Originated in the interpretations of the movement of individuals in a bird flock or fish school, PSO introduces the concept of personal best and global best to simulate the pattern of searching for food by flocking and successfully translate the natural phenomena to the optimization of complex functions. Many real-life applications of PSO cope with stochastic problems. To solve a stochastic problem using PSO, a straightforward approach is to equally allocate computational effort among all particles and obtain the same number of samples of fitness values. This is not an efficient use of computational budget and leaves considerable room for improvement. This paper proposes a seamless integration of the concept of optimal computing budget allocation (OCBA) into PSO to improve the computational efficiency of PSO for stochastic optimization problems. We derive an asymptotically optimal allocation rule to intelligently determine the number of samples for all particles such that the PSO algorithm can efficiently select the personal best and global best when there is stochastic estimation noise in fitness values. We also propose an easy-to-implement sequential procedure. Numerical tests show that our new approach can obtain much better results using the same amount of computational effort.
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Affiliation(s)
- Si Zhang
- School of Management, Shanghai University, Shanghai, China, 200444
| | - Jie Xu
- Department of Systems Engineering & Operations Research, George Mason University, Fairfax, Virginia 22030, USA
| | - Loo Hay Lee
- Department of Industrial and Systems Engineering, National University of Singapore, Singapore, 119260
| | - Ek Peng Chew
- Department of Industrial and Systems Engineering, National University of Singapore, Singapore, 119260
| | - Wai Peng Wong
- School of Management, Universiti Sains Malaysia, Penang, Malaysia
| | - Chun-Hung Chen
- Department of Systems Engineering & Operations Research, George Mason University, Fairfax, Virginia 22030, USA
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