A multi-objective optimization approach for green supply chain network design for the sea cucumber (Apostichopus japonicus) industry.

In China, aquatic supply chain network design does not include the green concept or the coordination of environmental and economic performance. Sea cucumber (Apostichopus japonicus) is an aquatic product of high economic value; however, studies on sea cucumber supply chain network optimization are lacking. This study is the first to design the sea cucumber supply chain and construct an optimization model. Considering the characteristics of the sea cucumber industry, LCA for Experts software and the CML-IA-Aug. 2016-world method were used to assess each aquaculture model's global warming potential (GWP), as the environmental performance indicator. In addition, multi-objective genetic algorithm (MOGA) coupled with Modified Technique for Order of Preference by Similarity to Ideal Solution (M-TOPSIS) integrates yield production, economic benefits, and environmental performance. The results demonstrated that cage seed rearing (CSR) combined bottom sowing aquaculture (BSA) represents the best production strategy upstream of the sea cucumber supply chain. In the downstream, the best proportion of sales channels in supermarkets, boutique stores and online shops accounted for 14.79 %, 58.02 % and 27.19 % of the production, respectively. The proposed optimization scenario 4 (S4) can increase product profit by 27.88 % and reduce GWP by 56.89 %. The following improvement measures are proposed: using sea cucumber aquaculture industry standards (cleaner production and green supplier selection) to regulate the behavior of enterprises, adopting an ecological and green production strategy, eliminating high-energy consumption and high emission production practices, and promoting widespread adoption of green consumption concepts. Finally, these measures may improve the sea cucumber supply chain, achieve coordinated environmental and economic performance development in the sea cucumber industry, and provide guidance for green optimization of other aquatic product supply chains in China.

Shape Sensing in Plate Structures through Inverse Finite Element Method Enhanced by Multi-Objective Genetic Optimization of Sensor Placement and Strain Pre-Extrapolation.

The real-time reconstruction of the displacement field of a structure from a network of in situ strain sensors is commonly referred to as "shape sensing". The inverse finite element method (iFEM) stands out as a highly effective and promising approach to perform this task. In the current investigation, this technique is employed to monitor different plate structures experiencing flexural and torsional deformation fields. In order to reduce the number of installed sensors and obtain more accurate results, the iFEM is applied in synergy with smoothing element analysis (SEA), which allows the pre-extrapolation of the strain field over the entire structure from a limited number of measurement points. For the SEA extrapolation to be effective for a multitude of load cases, it is necessary to position the strain sensors appropriately. In this study, an innovative sensor placement strategy that relies on a multi-objective genetic algorithm (NSGA-II) is proposed. This approach aims to minimize the root mean square error of the pre-extrapolated strain field across a set of mode shapes for the examined plate structures. The optimized strain reconstruction is subsequently utilized as input for the iFEM technique. Comparisons are drawn between the displacement field reconstructions obtained using the proposed methodology and the conventional iFEM. In order to validate such methodology, two different numerical case studies, one involving a rectangular cantilevered plate and the other encompassing a square plate clamped at the edges, are investigated. For the considered case studies, the results obtained by the proposed approach reveal a significant improvement in the monitoring capabilities over the basic iFEM algorithm with the same number of sensors.

Prediction of enzymatic function with high efficiency and a reduced number of features using genetic algorithm.

The post-genomic era has raised a growing demand for efficient procedures to identify protein functions, which can be accomplished by applying machine learning to the characteristics set extracted from the protein. This approach is feature-based and has been the focus of several works in bioinformatics. In this work, we investigated the characteristics of proteins, representing the primary, secondary, tertiary, and quaternary structures of the protein, that improve the model's quality by applying dimensionality reduction techniques and using the Support Vector Machine classifier for predicting the enzymes' classes. During the investigation, two approaches were evaluated: feature extraction/transformation, which was performed using the statistical technique Factor Analysis, and feature selection methods. For feature selection, we proposed an approach based on a genetic algorithm to face the optimization conflict between the simplicity and reliability of an ideal representation of the characteristics of the enzymes and also compared and employed other methods for this purpose. The best result was accomplished using a feature subset generated by our implementation of a multi-objective genetic algorithm enriched with features that this work identified as relevant to represent the enzymes. This subset representation reduced the dataset by about 87% and reached 85.78% of F-measure performance, improving the overall quality of the model classification. In addition, we verified in this work a subset addressed with only 28 features out of a total of 424 that reached a performance above 80% of F-measure for four of the six evaluated classes, showing that satisfactory classification performance can be achieved with a reduced number of enzymes's characteristics. The datasets and implementations are openly available.

Optimization of endoglucanase-lipase-amylase enzyme consortium from Thermomyces lanuginosus VAPS25 using Multi-Objective genetic algorithm and their bio-deinking applications.

In this study, the enzyme consortium of endoglucanase, lipase, and amylase was obtained and optimized using artificial intelligence-based tools. After optimization using a multi-objective genetic algorithm and artificial neural network, the enzyme activity was 8.8 IU/g, 153.68 U/g, and 19.2 IU/g for endoglucanase, lipase, and amylase, respectively, using Thermomyces lanuginosus VAPS25. The highest enzyme activity was obtained at parameters 77.69% moisture content, 52.7 °C temperature, 98 h, and 3.1 eucalyptus leaves: wheat bran ratio. The endoglucanase-lipase-amylase (END-LIP-AMY) enzyme consortium showed reliable characteristics in terms of catalytic activity at 50-80 °C and pH 6.0-9.0. The increase in deinking efficiency of 27.8% and 11.1% were obtained compared to control for mixed office waste and old newspaper, respectively, using the enzyme consortium. The surface chemical composition and fiber morphology of deinked pulp was investigated using Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and Scanning electron microscopy (SEM).

Application of multi-objective genetic algorithm for optimal combination of resources to achieve sustainable agriculture based on the water-energy-food nexus framework.

Understanding the systemic approach and its potential for decision-making is important for resource management, especially in agriculture in which increasing food demands and environmental and social issues are the main challenges. Therefore, multiple-criteria decision-making methods have a vital role in the optimum combination of resources. Computational models are commonly used to assist resource management decision-making; however, while water-energy-food nexus (WEFN) are increasingly well modeled, the inclusion of social issues has lagged behind. This paper outlines a model based on a multi-objective genetic algorithm (MOGA) that conceptualizes and proceduralizes balancing the goals of sustainable agricultural development highlighting impacts and interactions between social variables and the WEFN index in agriculture. The model was developed using a bottom-up approach, informed through farmer interviews, and secondary data in the Miandarband plain, west Iran. The Compromise Programming (CP) method, which is widely used to solve MOGA models, was applied to optimization algorithms in three-dimensional spaces. The model represents field conditions and provides a tool for policymakers and sustainable resource management. The modeling framework applied to the study area for the comparison of WFEN, life cycle assessment (LCA), and social dimension in current and optimum cultivation patterns. The proposed optimal cultivation pattern in minimum CP will reduce water and energy consumption by 2.56 % and 12.71 % while reducing environmental impacts by 6.82 %, and it will improve the social status of farmers. Results suggest that changes in the basic elements of objective functions will lead to a balance between cultivation patterns that depends on policies and socio-economic conditions. Moreover, proposed cultivation patterns may be sustainable but their viability varies across the periods and also in different human ecologies. However, by analyzing the feedback of the model and interactions between different dimensions, this work highlights that policymakers can decide sustainable agriculture how should be occur by comparing different solutions.

Accurate Reconstruction of Traffic Accident Based on Multiple Optimization Algorithms and Evaluation of Craniocerebral Injury Risk / 医用生物力学

Objective To investigate the effect of different optimization algorithms on accurate reconstruction of traffic accidents. Methods Non-dominated sorting genetic algorithm-II ( NSGA-II), neighborhood cultivation genetic algorithm (NCGA) and multi-objective particle swarm optimization (MOPSO) were used to optimize the multi-rigid body dynamic reconstruction of a real case. The effects of different optimization algorithms on convergence speed and optimal approximate solution were studied. The optimal initial impact parameters were simulated as boundary conditions of finite element method, and the simulated results were compared with the actual injuries. Results NCGA had a faster convergence speed and a better result in optimization process. The kinematic response of pedestrian vehicle collision reconstructed by the optimal approximate solution was consistent with the surveillance video. The prediction of craniocerebral injury was basically consistent with the cadaver examination. Conclusions The combination of optimization algorithm, rigid multibody and finite element method can complete the accurate reconstruction of traffic accidents and reduce the influence of human factors.

Hospital reconversion in response to the COVID-19 pandemic using simulation and multi-objective genetic algorithms.

With the outbreak of the novel coronavirus SARS-CoV2, many countries have faced problems because of their available hospital capacity. Health systems must be prepared to restructure their facilities and meet the requirements of the pandemic while keeping their services and specialties active. This process, known as hospital reconversion, contributes to minimizing the risk of contagion between hospital staff and patients and optimizing the efficient treatment and disposal of healthcare wastes that represent a risk of nosocomial infection contagion. A methodology based upon simulation and mathematical optimization with genetic algorithms is proposed to address the hospital reconversion problem. Firstly, a discrete event simulation model is developed to study the flow of patients within the hospital system. Subsequently, the hospital reconversion problem is formulated through a mathematical model seeking to maximize the proximity relationships between departments and minimize the costs due to the flow of agents within the system. Finally, the results obtained from the optimization process are evaluated through the simulation model. The proposed framework is validated by assessing the hospital reconversion process in a COVID-19 Hospital in Mexico. The results show the mathematical model's effectiveness by incorporating the medical personnel's expertise in decisions regarding the use of elevators, departments' location, structural dimensions, use of corridors, and the floors to which the departments are assigned when facing a pandemic. The contribution of this approach can be replicated during the hospital reconversion process in other hospitals with different characteristics.

Optimization of the driver's seat belt and injury biomechanical analysis in real-world minivan small offset impact accident scenarios.

To minimize injuries and protect the safety of the driver in minivan small offset collisions, an optimized pre-tensioned force-limiting seat belt was proposed herein. An accident with detailed information, such as medical reports, vehicle inspection reports, and accident scene photographs, was reconstructed using HyperMesh software. The effectiveness of both the accident model and the pre-tensioned force-limiting seat belt was evaluated. To obtain the optimal seat belt parameters for driver protection, first, force-limiting A, pre-tensioned force B, and pre-tensioned time C factors were selected in designing an orthogonal test with different factor levels. The influence laws of each factor on the injury biomechanical characteristics of the driver were analyzed via the direct analysis method. Moreover, each kind of critical injury value of the human body was synthesized, and the radial basis function surrogate model was constructed. The three seat belt parameters were optimized using the NSGA-II multi-objective genetic algorithm. The results showed that the optimal balance variable parameter of the seat belt was 4751.618 N-2451.839 N-17.554 ms (A-B-C). Finally, the optimal scheme was verified in a system simulating a minivan small offset collision. The results showed that after optimization, the skull von Mises stress was reduced by 36.9%, and the stress of the cervical vertebra cortical bone and cancellous bone decreased by 29.1% and 30.8%, respectively. In addition, the strains of the ribs and lungs decreased by 31.2% and 30.7%, respectively.

EEG-based spatial elements optimisation design method.

In the field of digital design, a recent hot topic is the study of the interaction between spatial environment design and human factors. Electroencephalogram (EEG) and eye tracking can be used as quantitative analysis methods for architectural space evaluation; however, conclusions from existing studies on improving the quality of spatial environments based on human factors tend to remain qualitative. In order to realise the quantitative optimisation design of spatial elements from human physiological data, this research used the digital space optimisation method and perceptual evaluation research. In this way, it established an optimisation method for built space elements in real-time using human psychological indicators. Firstly, this method used the specific indicators of the Meditation value and Attention value in the human EEG signal, taking the ThinkGear AM (TGAM) module as the optimisation objective, the architectural space colour and the window size as the optimisation object, and the multi-objective genetic algorithm as the optimisation tool. Secondly, this research combined virtual reality scenarios and parametric linkage models to realise this optimisation method to establish a tool platform and workflow. Thirdly, this study took the optimisation of a typical living space as an example and recruited 50 volunteers to participate in an optimisation experiment. The results indicated that with the iterative optimisation of the multi-objective genetic algorithm, the specific EEG index decreases significantly and the standard deviation of the in-dex fluctuates and decreases during the iterative process, which further indicates that the optimisation method established in this study with the specific EEG index as the optimisation objective is effective and feasible. In addition, this study laid the foundation for more EEG indicators and more complex spatial element opti-misation research in the future.

Snowflake Bionic Flow Channel Design to Optimize the Pressure Drop and Flow Uniform of Proton Exchange Membrane Fuel Cells.

The flow channel design of bipolar plates plays a significant role in the proton exchange membrane fuel cells operation, particularly in thermal and water management. The pursuit of low-pressure drop supply and flow field uniformity in PEM fuel cells has not stopped, resulting in numerous new bipolar plate flow channel designs. The biomimetic leaf vein shape-based flow channel and lung flow channel designs can significantly improve gas supply uniformity and reduce pressure drop. Therefore, we propose a snowflake-shaped bionic channel design by integrating the advantages of the leaf vein shape and lung shape channel. A 3D multi-physics fuel cell model is used to verify the feasibility and superiority of the bionic snowflake design in improving fuel cell performance, especially in reducing the pumping work. The local pressure distribution, oxygen distribution, water distribution, and current density distribution are used to reveal the enhancement mechanism of the new snowflake flow channel. The flow uniformity is further enhanced by using multi-objective (13 target parameters) and multi-parameter (18 independent variables) genetic algorithm optimization. The general goal of this work is to provide a new strategy for the thermal and water management of PEM fuel cells.

Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification.

Microarray gene expression data are often accompanied by a large number of genes and a small number of samples. However, only a few of these genes are relevant to cancer, resulting in significant gene selection challenges. Hence, we propose a two-stage gene selection approach by combining extreme gradient boosting (XGBoost) and a multi-objective optimization genetic algorithm (XGBoost-MOGA) for cancer classification in microarray datasets. In the first stage, the genes are ranked using an ensemble-based feature selection using XGBoost. This stage can effectively remove irrelevant genes and yield a group comprising the most relevant genes related to the class. In the second stage, XGBoost-MOGA searches for an optimal gene subset based on the most relevant genes' group using a multi-objective optimization genetic algorithm. We performed comprehensive experiments to compare XGBoost-MOGA with other state-of-the-art feature selection methods using two well-known learning classifiers on 14 publicly available microarray expression datasets. The experimental results show that XGBoost-MOGA yields significantly better results than previous state-of-the-art algorithms in terms of various evaluation criteria, such as accuracy, F-score, precision, and recall.

Enhancement of nutritional value of fried fish using an artificial intelligence approach.

Frying affects the nutritional quality of fish detrimentally. In this study, using Catla catla and mustard oil, experiments were carried out in varying temperatures (140-240 °C), times (5-20 min), and oil amounts (25-100 ml/kg of fish) which established drastic reduction of 44.97% and 99.40% for polyunsaturated fatty acid (PUFA)/saturated fatty acids (SFA) and index of atherogenicity (IA) profile, respectively. Artificial neural network (ANN) was implemented successfully to provide an association between the independent inputs with dependent outputs (values of R2 were 0.99 and 0.98; RMSE were 0.038 and 0.046; and performance were 0.038 and 0.067 for PUFA/SFA and IA, respectively) by exhaustive search of various algorithms and activation functions available in literature. ANN model-based meta-heuristic, stochastic optimization formalisms, genetic algorithm (GA) and particle swarm optimization (PSO), were applied to optimize the combination of cooking parameters for improving the nutritional quality of food which improved the nutritional value by maximizing the PUFA/SFA profile up to 63.05% and minimizing the IA profile to 99.64%. Multi-objective genetic algorithm (MOGA) was also employed to tune the inputs by maintaining a balance between the contrasting outputs and enhance the overall food value simultaneously with multi-objective (beneficial for health, economic, and environment-friendly) proposal. MOGA was able to improve the PUFA/SFA profile up to 44.76% and reduce the IA profile to 92.94% concurrently with the reduction of wastage of culinary media and energy consumption, following the optimized cooking condition (118.92 °C, 6.06 min, 40 ml oil/kg of fish).

Spatial quantification method of grassland utilization intensity on the Qinghai-Tibetan Plateau: A case study on the Selinco basin.

Existing methods for spatial quantification of grassland utilization intensity cannot meet the demand for accurate detection of the spatial distribution of grassland utilization intensity in the Qinghai-Tibetan Plateau with high spatial resolution. In this paper, a method based on remote-sensing observations and simulations of grassland growth dynamics is proposed. The grassland enhanced vegetation index (EVI) time-series curve during the growing season characterizes the growth of grassland in the corresponding pixel; The deviation between the observed and potential EVI curves indicates the disturbance on grassland growth imposed by human activities, and it can characterize the grassland utilization intensity during the growing season. Based on the main idea described above, absolute and relative disturbances are calculated and used as quantitative indicators of grassland utilization intensity defined from different perspectives. Livestock amount at the pixel scale is obtained by pixel-by-pixel calculations based on the function relationship at the township scale between absolute disturbance and livestock density, which is specific quantitative indicator that considers the mode of grassland utilization. In simulating the potential EVI of grassland, the lag and accumulation effects of meteorological factors are investigated at the daily scale using a multi-objective genetic algorithm. Further, the nonlinear functions between multiple environmental factors (e.g., grassland type, topography, soil, meteorology) and the grassland EVI are established using an error back-propagation feedforward artificial neural network (ANN-BP) with parameter optimization. Finally, the potential EVIs of all grassland pixels are simulated on the basis of this model. The method is applied to the Selinco basin on the Qinghai-Tibetan Plateau and validated by examining the spatial consistency of the results with township-scale livestock density and grazing pressure. The final results indicate that the proposed method can accurately detect the spatial distribution of grassland utilization intensity which is appliable in the similar regions.

Optimized Design of Neural Networks for a River Water Level Prediction System.

In this paper, a Multi-Objective Genetic Algorithm (MOGA) framework for the design of Artificial Neural Network (ANN) models is used to design 1-step-ahead prediction models of river water levels. The design procedure is a near-automatic method that, given the data at hand, can partition it into datasets and is able to determine a near-optimal model with the right topology and inputs, offering a good performance on unseen data, i.e., data not used for model design. An example using more than 11 years of water level data (593,178 samples) of the Carrión river collected at Villoldo gauge station shows that the MOGA framework can obtain low-complex models with excellent performance on unseen data, achieving an RMSE of 2.5 × 10-3, which compares favorably with results obtained by alternative design.

Multi-objective Genetic Algorithm Based Deep Learning Model for Automated COVID-19 Detection Using Medical Image Data.

Purpose: In early 2020, the world is amid a significant pandemic due to the novel coronavirus disease outbreak, commonly called the COVID-19. Coronavirus is a lung infection disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus (SARS-CoV-2). Because of its high transmission rate, it is crucial to detect cases as soon as possible to effectively control the spread of this pandemic and treat patients in the early stages. RT-PCR-based kits are the current standard kits used for COVID-19 diagnosis, but these tests take much time despite their high precision. A faster automated diagnostic tool is required for the effective screening of COVID-19. Methods: In this study, a new semi-supervised feature learning technique is proposed to screen COVID-19 patients using chest CT scans. The model proposed in this study uses a three-step architecture, consisting of a convolutional autoencoder based unsupervised feature extractor, a multi-objective genetic algorithm (MOGA) based feature selector, and a Bagging Ensemble of support vector machines based binary classifier. The proposed architecture has been designed to provide precise and robust diagnostics for binary classification (COVID vs.nonCOVID). A dataset of 1252 COVID-19 CT scan images, collected from 60 patients, has been used to train and evaluate the model. Results: The best performing classifier within 127 ms per image achieved an accuracy of 98.79%, the precision of 98.47%, area under curve of 0.998, and an F1 score of 98.85% on 497 test images. The proposed model outperforms the current state of the art COVID-19 diagnostic techniques in terms of speed and accuracy. Conclusion: The experimental results prove the superiority of the proposed methodology in comparison to existing methods.The study also comprehensively compares various feature selection techniques and highlights the importance of feature selection in medical image data problems.

Low-cost media engineering for phosphate and IAA production by Kosakonia pseudosacchari TCPS-4 using Multi-objective Genetic Algorithm (MOGA) statistical tool.

The plant growth-promoting rhizobacteria (PGPR) can improve the biotic or abiotic stress condition by exploiting the productivity and plant growth of the plants under stressful conditions. This study examines the role of a rhizospheric bacterial isolate Kosakonia pseudosacchari TCPS-4 isolated from cluster bean plant (Cyamopsis tetragonoloba) under dryland condition. The low-cost media engineering was evaluated, and the phosphate-solubilizing and IAA-producing abilities of Kosakonia pseudosacchari TCPS-4 were improved using a hybrid statistical tool viz. Multi-objective Genetic Algorithm (MOGA). Further, the effect of carbon and nitrogen media constituents and their interactions on IAA production and phosphate solubilization were also confirmed by a single-factor experiment assay. This revealed that MOGA-based model depicted 47.5 mg/L inorganic phosphate as the highest phosphate concentration in media containing 45 g/L carbon source, 12 g/L nitrogen source and 0.20 g/L MgSO4. The highest IAA production was 18.74 mg/L in media containing 45 g/L carbon source, 12 g/L nitrogen source and 0.2 g/L MgSO4. These values were also confirmed and measured by the experiments with phosphate solubilization of 45.71 mg/L and IAA production of 18.71 mg/L with 1012 cfu/mL. This concludes that effective media engineering using these statistical tools can enhance the phosphate and IAA production by each model. A good correlation between measured and predicted values of each model confirms the validity of both responses. The present study gives an insight on media engineering for phosphate and IAA production by Kosakonia pseudosacchari TCPS-4.

Day surgery appointment scheduling with patient preferences and stochastic operation duration.

BACKGROUND: Due to its fast service and high utilization, day surgery is becoming more and more important in the medical system. As a result, an effective day surgery scheduling can reasonably release the supply and demand pressure. OBJECTIVE: This paper aims to investigate the day surgery scheduling problem with patient preferences and limited operation room for the sake of increasing operation efficiency and further decreasing surgery costs. METHODS: A multiple objective stochastic programming model is constructed to seek a satisfactory surgical scheduling for both patients and hospitals under different scenarios. Multi-objective genetic algorithm is designed to solve the model and different scales of scenarios are utilized to test the effectiveness of the algorithm and modeling process. RESULTS: Results show that the proposed model and algorithm can provide a feasible solution for maximizing individual preference of surgeons with surgery date and operation room utilization as well. CONCLUSIONS: Patient preference is proposed to be incorporated into day surgery scheduling, and the variability of surgery duration considered to seek a satisfactory surgery scheduling scheme for both patients and hospitals is more in line with the actual hospital situation.

Performance Optimization of a Condenser in Ocean Thermal Energy Conversion (OTEC) System Based on Constructal Theory and a Multi-Objective Genetic Algorithm.

Constructal optimization of a plate condenser with fixed heat transfer rate and effective volume in ocean thermal energy conversion (OTEC) system is performed based on constructal theory. Optimizations of entropy generation rate ( S ˙ g ) in heat transfer process and total pumping power ( P sum ) due to friction loss are two conflicting objectives for a plate condenser. With the conventional optimization method, the plate condenser is designed by taking a composite function (CF) considering both S ˙ g and P sum as optimization objectives, and employing effective length, width, and effective number of heat transfer plates as design variables. Effects of structural parameters of the plate condenser and weighting coefficient of CF on design results are investigated. With a multi-objective genetic algorithm, the plate condenser is designed by simultaneously optimizing S ˙ g and P sum , and the Pareto optimal set is obtained. The results demonstrate that CFs after primary and twice-constructal optimizations are respectively reduced by 7.8% and 9.9% compared with the initial CF, and the effective volume of the plate condenser has a positive impact on the twice minimum CF. Furthermore, the Pareto optimal set can provide better selections for performance optimizations of plate condensers.

COSMO: A dynamic programming algorithm for multicriteria codon optimization.

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-and-bound 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.

Optimal policies for control of the novel coronavirus disease (COVID-19) outbreak.

Understanding the early transmission dynamics of diseases and estimating the effectiveness of control policies play inevitable roles in the prevention of epidemic diseases. To this end, this paper is concerned with the design of optimal control strategies for the novel coronavirus disease (COVID-19). A mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission based on Wuhan's data is considered. To solve the problem effectively and efficiently, a multi-objective genetic algorithm is proposed to achieve high-quality schedules for various factors including contact rate and transition rate of symptomatic infected individuals to the quarantined infected class. By changing these factors, two optimal policies are successfully designed. This study has two main scientific contributions that are: (1) This is pioneer research that proposes policies regarding COVID-19, (2) This is also the first research that addresses COVID-19 and considers its economic consequences through a multi-objective evolutionary algorithm. Numerical simulations conspicuously demonstrate that by applying the proposed optimal policies, governments could find useful and practical ways for control of the disease.