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COSMO: A dynamic programming algorithm for multicriteria codon optimization

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2020年 18卷 1811-1818页

作者： Taneda, Akito Asai, Kiyoshi Hirosaki Univ Grad Sch Sci & Technol Hirosaki Aomori 0368561 Japan Univ Tokyo Grad Sch Frontier Sci Kashiwa Chiba 2778562 Japan

Codon optimization in protein-coding sequences (CDSs) is a widely used technique to promote the heterologous expression of target genes. In codon optimization, a combinatorial space of nucleotide sequences that code a given amino acid sequence and take into account user-prescribed forbidden sequence motifs is explored to optimize multiple criteria. Although evolutionary algorithms have been used to tackle such complex codon optimization problems, evolutionary codon optimization tools do not provide guarantees to find the optimal solutions for these multicriteria codon optimization problems. We have developed a novel multicriteria dynamic programming algorithm, COSMO. By using this algorithm, we can obtain all Pareto-optimal solutions for the multiple features of CDS, which include codon usage, codon context, and the number of hidden stop codons. User-prescribed forbidden sequence motifs are rigorously excluded from the Pareto-optimal solutions. To accelerate CDS design by COSMO, we introduced constraints that reduce the number of Pareto-optimal solutions to be processed in a branch-andbound manner. We benchmarked COSMO for run-time and the number of generated solutions by adapting selected human genes to yeast codon usage frequencies, and found that the constraints effectively reduce the run-time. In addition to the benchmarking of COSMO, a multi-objective genetic algorithm (MOGA) for CDS design was also benchmarked for the same two aspects and their performances were compared. In this comparison, (i) MOGA identified significantly fewer Pareto-optimal solutions than COSMO, and (ii) the MOGA solutions did not achieve the same mean hypervolume values as those provided by COSMO. These results suggest that generating the whole set of the Pareto-optimal solutions of the codon optimization problems is a difficult task for MOGA. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

关键词： Constraints Codon adaptation index Codon pair bias Hidden stop codon multi-objective genetic algorithm Codon optimization/deoptimization

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CFD-based multi-objective optimization method for ship design

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS 2006年第5期52卷 499-527页

作者： Tahara, Yusuke Tohyama, Satoshi Katsui, Tokihiro Osaka Prefecture Univ Sakai Osaka 591 Japan

This paper concerns development and demonstration of a computational fluid dynamics (CFD)-based multi-objective optimization method for ship design. Three main components of the method, i.e. computer-aided design (CAD), CFD, and optimizer modules are functionally independent and replaceable. The CAD used in the present study is NAPA system, which is one of the leading CAD systems in ship design. The CFD method is FLOWPACK version 2004d, a Reynolds-averaged Navier-Stokes (RaNS) solver developed by the present authors. The CFD method is implemented into a self-propulsion simulator, where the RaNS solver is coupled with a propeller-performance program. In addition, a maneuvering simulation model is developed and applied to predict ship maneuverability performance. Two nonlinear optimization algorithms are used in the present study, i.e. the successive quadratic programming and the multi-objective genetic algorithm, while the former is mainly used to verify the results from the latter. For demonstration of the present method, a multi-objective optimization problem is formulated where ship propulsion and maneuverability performances are considered. That is, the aim is to simultaneously minimize opposite hydrodynamic performances in design tradeoff. In the following, an overview of the present method is given, and results are presented and discussed for tanker stern optimization problem including detailed verification work on the present numerical schemes. Copyright (c) 2006 John Wiley & Sons, Ltd.

关键词： CAD RaNS solver hull-form optimization multi-objective genetic algorithm maneuverability propulsion

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A self-adaptive multi-objective feature selection approach for classification problems

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INTEGRATED COMPUTER-AIDED ENGINEERING 2022年第1期29卷 3-21页

作者： Xue, Yu Zhu, Haokai Neri, Ferrante Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Engn Res Ctr Digital Forens Minist Educ Nanjing Jiangsu Peoples R China Univ Nottingham Sch Comp Sci COL Lab Nottingham England

In classification tasks, feature selection (FS) can reduce the data dimensionality and may also improve classification accuracy, both of which are commonly treated as the two objectives in FS problems. Many meta-heuristic algorithms have been applied to solve the FS problems and they perform satisfactorily when the problem is relatively simple. However, once the dimensionality of the datasets grows, their performance drops dramatically. This paper proposes a self-adaptive multi-objective genetic algorithm (SaMOGA) for FS, which is designed to maintain a high performance even when the dimensionality of the datasets grows. The main concept of SaMOGA lies in the dynamic selection of five different crossover operators in different evolution process by applying a self-adaptive mechanism. Meanwhile, a search stagnation detection mechanism is also proposed to prevent premature convergence. In the experiments, we compare SaMOGA with five multi-objective FS algorithms on sixteen datasets. According to the experimental results, SaMOGA yields a set of well converged and well distributed solutions on most data sets, indicating that SaMOGA can guarantee classification performance while removing many features, and the advantage over its counterparts is more obvious when the dimensionality of datasets grows.

关键词： Feature selection self-adaptive multi-objective genetic algorithm stagnation detection classification

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An Application and Characteristic Analysis of MOGA for Bi-objective Optimal Component Allocation Problem in Series-Parallel Redundant System

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ELECTRONICS AND COMMUNICATIONS IN JAPAN 2009年第9期92卷 7-16页

作者： Yamachi, Hidemi Tsujimura, Yasuhiro Yamamoto, Hisashi Kambayashi, Yasushi Nippon Inst Technol Miyashiro Saitama Japan Tokyo Metropolitan Univ Tokyo Japan

We discuss a solution method based on evolutionary technology for the optimal component allocation problem in a series-parallel redundant system. A series-parallel system consists of subsystems that are connected in series and each subsystem consists of interchangeable components in parallel. There are some heuristic methods to approximately solve the optimal component allocation problem for series-parallel systems. We have formulated this problem as a multi-objective optimization problem minimizing the system cost and maximizing the system reliability, and proposed an algorithm that obtains the exact solutions (Pareto solutions) of the problems in an efficient way. Because this problem is one of the NP-complete problems, it is difficult to obtain the optimal solution for the large-scale problems and methods that obtain the exact solutions are not known. The algorithm utilizes the depth-first search method to eliminate useless searches and employs the branch-and-bound method to obtain the Pareto solutions. According to the results of our numerical experiments, the algorithm searches the Pareto solutions in practical execution time for not-so-large-scale problems. In order to solve larger-scale problems, we propose a multi-objective genetic algorithm (MOGA). We evaluate the ability of the MOGA by comparison with the exact solution method by using various scale problems. Through those experiments, we discuss the characteristics of this problem and analyze the effectiveness of our method. (C) 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(9): 7-16, 2009;Published online in Wiley InterScience (***). DOI 10.1002/ecj.10100

关键词： series-parallel redundant system optimal allocation problem multi-objective optimization multi-objective genetic algorithm

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Through hole-cutting conic posture optimization for a redundant 3D laser cutting machine

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第1-2期132卷 443-461页

作者： Ding, Zhipeng Soccio, Pietro Indri, Marina Rizzo, Alessandro Politecn Torino Dept Elect & Telecommun Corso Duca Abruzzi 24 I-10129 Turin Italy Eft EUROPE Srl Corso Duca Abruzzi 2 I-10128 Turin Italy

Productively reducing the time required to cut numerous through holes in automotive workpieces is crucial for enhancing parts manufacturing in the 3D laser cutting process. However, the conventional cutting strategy, in which the laser beam maintains a stationary posture along the hole path, lacks flexibility and fails to effectively leverage processing tolerances. In this study, we conduct a thorough analysis of the kinematics of a six-axis redundant laser cutting machine and resolve through a decoupling method with singularity management. We propose an innovative conic posture cutting strategy for 3D laser hole-cutting with thin materials. This approach adopts the geometry of a cone as the posture while cutting the hole path. In order to obtain the optimal vertex of the cone while minimizing the taper error generated by the conic posture and kinetic energy consumption of the actuators during motion, we formulate a multi-objective optimization problem and solve it using a genetic algorithm. Furthermore, we enhance the optimization by adopting a time minimization approach. Through the implementation of a B-pillar workpiece cutting experiment, we have successfully validated the credibility of our proposed cutting strategy, thereby demonstrating an enhancement of time on 26 hole-cutting paths.

关键词： 3D laser cutting Through hole-cutting Redundant kinematics Posture optimization multi-objective genetic algorithm

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Optimal design, intelligent fuzzy logic and model predictive control for high-purity ethyl-methyl carbonate and diethyl carbonate production using reactive dividing wall column

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2023年第1期195卷 691-710页

作者： Liu, Botong Han, Yicheng Liu, Botan Ge, Xiaolong Yuan, Xigang Tsinghua Univ Dept Chem Engn Beijing 100084 Peoples R China Tianjin Univ Sci & Technol Coll Chem Engn & Mat Sci Tianjin Key Lab Brine Chem Engn & Resource Ecoutil Tianjin 300457 Peoples R China Tianjin Univ State Key Lab Chem Engn Tianjin 300350 Peoples R China

Methyl carbonate (EMC) and Diethyl carbonate (DEC), as excellent solvents for lithium-ion electrolytes, are synthesized by transesterification, and consecutive reaction seriously complicate optimization and control issues. Process synthesis, parameters optimization and advanced control are crucial points that should be addressed for this ill-conditioned and highly nonlinear system, which is the motivation and novelty of present work. With single-objective and multi-objective genetic algorithm, intensified processes for EMC and DEC production were optimized respectively with heterogeneous catalyst. And reactive dividing wall column achieved 23% and 13% total annualized cost reductions compared with reactive distillation for two target carbonates. Based on optimal steady-state solution, intelligent fuzzy logic and model predictive control of EMC and DEC production is conducted to clarify their respective strengths in intensified process with consecutive reaction. Sequential optimization for parameters of FLC was carried out with Integral of Squared Error (ISE) as objective. Finally, control performance of FLC and LPMC are compared with that under PID control, and results show that overshoot and transition time under FLC and LMPC are obviously mitigated, along with evident reduction in ISE. & COPY;2023 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： Process intensification Reactive dividing wall column multi-objective genetic algorithm Fuzzy logic control Model predictive control

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Radial basis function surrogate model-based optimization of guardrail post embedment depth in different soil conditions

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2020年第2-3期234卷 739-761页

作者： Ozcanan, Sedat Atahan, Ali Osman Istanbul Tech Univ Istanbul Turkey

For guardrail designers, it is essential to achieve a crashworthy and optimal system design. One of the most critical parameters for an optimal road restraint system is the post embedment depth or the post-to-soil interaction. This study aims to assess the optimum post embedment depth values of three different guardrail posts embedded in soil with varying density. Posts were subjected to dynamic impact loads in the field while a detailed finite element study was performed to construct accurate models for the post-soil interaction. It is well-known that experimental tests and simulations are costly and time-consuming. Therefore, to reduce the computational cost of optimization, radial basis function-based metamodeling methodology was employed to create surrogate models that were used to replace the expensive three-dimensional finite element models. In order to establish the radial basis function model, samples were derived using the full factorial design. Afterward, radial basis function-based metamodels were generated from the derived data and objective functions performed using finite element analysis. The accuracy of the metamodels were validated by k-fold cross-validation, then optimized using multi-objective genetic algorithm. After optimum embedment depths were obtained, finite element simulations of the results were compared with full-scale crash test results. In comparison with the actual post embedment depths, optimal post embedment depths provided significant economic advantages without compromising safety and crashworthiness. It is concluded that the optimum post embedment depths provide an economic advantage of up to 17.89%, 36.75%, and 43.09% for C, S, and H types of post, respectively, when compared to actual post embedment depths.

关键词： Guardrail post embedment depth optimization numerical analysis radial basis function multi-objective genetic algorithm

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Vibration control of smart TMD for retractable-roof spatial structure considering closed and open roof condition

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INTERNATIONAL JOURNAL OF STEEL STRUCTURES 2017年第4期17卷 1537-1548页

作者： Kim, Hyun-Su Kang, Joo-Won Sunmoon Univ Div Architecture Architectural & Civil Engn Asan South Korea Yeungnam Univ Sch Architecture Gyongsan South Korea

A retractable-roof system is frequently adapted to the spatial structure of stadiums and sports halls. The geometric shape and mass location are varied by opening or closing the roof, resulting in a change in the dynamic characteristics of the retractable-roof spatial structure. A passive tuned mass damper (TMD) shows good vibration control performance of the spatial structures when it is tuned properly. Because the closed or open roof condition of a retractable spatial structure causes off-tuning, multiple smart tuned mass dampers (STMD) were developed in this study to increase the control performance in the case of off-tuning. Initially, the dynamic characteristics variations of the opened or closed roof conditions were investigated using an example retractable spatial structure. The control performances of a passive TMD and smart TMD were evaluated under off-tuning conditions. For this purpose, artificial ground motion was generated based on the Korean Building Code (KBC) 2016. multiple smart TMDs were comprised of magnetorheological (MR) dampers. A fuzzy logic control algorithm (FLC) was used to generate the command voltages sent to the MR dampers and the FLC was optimized by a multi-objective genetic algorithm. Numerical analyses showed that the smart TMD had better control performance than the passive TMD. In the case of the off-tuning condition, the difference in control performance between passive and smart TMDs increased.

关键词： retractable roof system spatial structure smart control system tuned mass damper MR damper seismic response control fuzzy logic controller multi-objective genetic algorithm

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multi-objective optimization design for pressure uniformity in a proton exchange membrane fuel cell stack

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2022年第13期46卷 18947-18963页

作者： Chen, Wei-Hsin Li, Yi-Wei Lin, Chih-Chia Chang, Min-Hsing Saravanakumar, Ayyadurai Saw, Lip H. Natl Cheng Kung Univ Dept Aeronaut & Astronaut Tainan 701 Taiwan Tunghai Univ Res Ctr Smart Sustainable Circular Econ Taichung Taiwan Natl Chin Yi Univ Technol Dept Mech Engn Taichung Taiwan Ind Technol Res Inst Green Energy & Environm Res Labs Tainan Taiwan Tatung Univ Dept Mech & Mat Engn Taipei 104 Taiwan SRM Inst Sci & Technol Directorate Res & Virtual Educ Ctr Environm Nucl Res Chennai Tamil Nadu India Tunku Abdul Rahman Univ Lee Kong Chian Fac Engn & Sci Kajang Malaysia

For a proton exchange membrane fuel cell (PEMFC), the pressure uniformity of fluid in the stack is a significant factor influencing the cell performance. This study employs the multi-objective genetic algorithm (MOGA) to optimize the pressure uniformity in a PEMFC stack by adjusting the geometric design of inlet and outlet flow channels. The flow channel geometry is divided into three parts or named factors: tube length, tapered tube length, and channel height. The target is to find the minimum pressure difference between the inlet and outlet flow channels and optimize the pressure uniformity in the stack. The Latin hypercube sampling (LHS) method is used to organize the simulations for the design of experiments (DoEs), and the genetic aggregation (GA) method is employed to generate the response surfaces of the three factors. The resultant response surfaces are utilized to analyze the effects of the three factors on two objective functions, namely, pressure uniformity and pressure drop. The analysis of variance (ANOVA) method is also used to evaluate the influences of the factors, and the results are consistent with those obtained by the response surface method. The results show that the channel height produces the greatest impact on pressure uniformity. An increase in channel height can improve the pressure uniformity and reduce the pressure drop between the inlet and outlet channels. The MOGA analysis determines the optimal geometric design where the maximized pressure uniformity is 0.97 and the minimized pressure drop is 31 kPa. The number of cells in the stack is also taken into consideration. It is found that the influence of the inlet/outlet channel geometry on pressure uniformity is more pronounced when the cell number increases. The results benefit the design of inlet/outlet flow channels and the improvement of pressure uniformity in a PEMFC stack.

关键词： analysis of variance (ANOVA) flow channel design Latin hypercube sampling (LHS) method multi-objective genetic algorithm pressure uniformity and pressure drop proton exchange membrane fuel cell

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Smart system management and techno-environmental optimal sizing of a desalination plant powered by renewables with energy storage

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2021年第5期45卷 7501-7520页

作者： Affi, Safa Cherif, Habib Belhadj, Jamel Univ Tunis ENSIT Tunis Tunisia Univ Tunis El Manar Lab Syst Elect ENIT Tunis Tunisia

The present study assessed the performances of a brackish water reverse osmosis desalination unit powered by (PV/Wind/Battery) system with hydraulic storage. The hybrid renewable energy system and the reverse osmosis desalination plant were modeled for a specific site in southern Tunisia using real meteorological and water consumption data for 1 year. A total of 8760 simulations were treated under a dynamic simulator coupled to a smart energy management strategy to reach the hourly water load demand. Although the recognized reliability of the hybrid renewable sources compared to one- power source, their design may still critical. Therefore, in this paper, the authors suggest applying the methodology of optimizing the presented unit using an integrated systemic meta-modeling which is built through the design of experiments tool and defined by an equation involving the decision variables of the optimization as its parameters. This equation outlines the objects of the optimum sizing. The selected optimal design of the proposed unit is predicted by the genetic algorithm NSGA-II based on minimizing two objectives in conflict: Embodied energy and hydraulic loss of power supply. The CPU time of the NSGA-II optimization via the meta-model reached only 15 minutes of treatment (with one day to build the design of experiments) compared to the classic optimization via dynamic simulation. The results of the optimum sizing show that it is possible to consider a low water shortage rate (0% <= LPSP-H < 5%) for a marginal variation of embodied energy rate which does not exceed (7%).

关键词： design of experiments hybrid renewable energy meta-model multi-objective genetic algorithm optimization water desalination water-energy nexus

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