Combinatorial Optimization Strategy Research Articles

Shape optimization of autonomous underwater helicopters based on different parameter curves and various optimization algorithms

This study aimed to compare the non-uniform rational B-spline (NURBS) and Bezier curves to design the shape of an autonomous underwater helicopter (AUH). Currently, there is no established method for AUH shape design. The optimal solutions obtained from both curves were compared to evaluate their performance. A parametric model was constructed to determine the design variables of the two curves. Computational fluid dynamics was used to solve the resistance values of different shapes. Sample points were generated using the design of the experimental approach, and an approximate model was established using the radial basis function neural network method. Based on the combinatorial optimization strategy, three intelligent optimization algorithms were used for the global search. Then, the resulting solution was used as the starting point for a local search to obtain the global optimal solution. The resistance optimal solutions achieved by employing the NURBS and Bezier curves were 25.2% and 24.1% smaller than the initial values, respectively. These findings indicate that, within the design parameters of this study, the shape design approach using the NURBS curve yields superior performance for the AUH.

Highly efficient expression of Rasamsonia emersonii lipase in Pichia pastoris: characterization and gastrointestinal simulated digestion in vitro.

Acidic lipases with high catalytic activities under acidic conditions have important application values in the food, feed and pharmaceutical industries. However, the availability of acidic lipases is still the main obstacle to their industrial applications. Although a novel acidic lipase Rasamsonia emersonii (LIPR) was heterologously expressed in Escherichia coli, the expression level was unsatisfactory. To achieve the high-efficiency expression and secretion of LIPR in Pichia pastoris GS115, the combinatorial optimization strategy was adopted including gene codon preference, signal peptide, molecular chaperone co-expression and disruption of vacuolar sorting receptor VPS10. The activity of the combinatorial optimization engineered strain in a shake flask reached 1480 U mL-1, which was 8.13 times greater than the P. pastoris GS115 parental strain. After high-density fermentation in a 5-L bioreactor, the highest enzyme activity reached as high as 11 820 U mL-1. LIPR showed the highest activity at 40 °C and pH 4.0 in the presence of Ca2+ ion. LIPR exhibited strong tolerance to methanol, indicating its potential application in biodiesel biosynthesis. Moreover, the gastrointestinal digestion simulation results demonstrated that LIPR was tolerant to pepsin and trypsin, but its activity was inhibited by sodium taurodeoxycholate. This study provided an effective approach for the high expression of acidic lipase LIPR. LIPR was more appropriate for lipid digestion in the stomach than in intestine according to the gastrointestinal digestion simulation results. © 2024 Society of Chemical Industry.

Optimizing dialysis water treatment based on medical planning requirements

This paper introduces a new approach to improving hemodialysis and peritoneal dialysis processes. This maintains compatibility between the water treatment unit and active dialysis machines. Identification of different deviations in the patient’s requirements to classify the suitable flow rate (FR) of the pure water can be created through an Adaptive Neuro-Fuzzy Inference System (ANFIS). Four types of categories are classified: quarter load, half load, three-quarter load, and full load. This includes two main steps: identifying the dialysis categories with patients and measuring the suitable FR for each category. The effective FR of water was computed to indicate how the number of pumps rose as the number of patients treated within the dialysis unit increased. The introduced prediction method was tested against artificial neural networks to predict alum doses (ANN-AD) method, artificial intelligence as a combinatorial optimization strategy (AN-COS) using peach-palm waste in a solid-state fermentation environment, expert system applications (ESA) in chronic kidney disease management (MDSSs), prescription optimization for automated peritoneal dialysis (BCPH). In this work, pumping system parameter estimation was utilized to assess the performance of the introduced systems based on input voltage, input current, rotor speed, and electromagnetic torque waveforms of the pumping drive. This method uses the ANFIS algorithm to construct a high-performance pumping system to evaluate and predict the flow rate of dialysis machines based on input–output estimates. The flow rate values for each patient in critical care units can be controlled by the presented algorithm according to the time of dialysis. A good predictive model based on integrating the experience and decision making of medical planners was implemented. The proposed method not only improves accuracy but also saves time in the dialysis unit. The presented fuzzy classifier uses the ratio of the number of predicted true positives and false positives to the total number of anticipated positives. The simulation results show that the proposed technique can be successfully used to classify the suitable FR of the water according to the hemodialysis load. One of the advantages of the presented technique is the ability to predict the value of FR in real time with reliability, low computational complexity, and a high accuracy of 98.01%, which simplifies the dialysis process for many patients while at the same time leading to improved performance of hemodialysis. The presented method can be implanted in online FR estimation and analysis hardware for hemodialysis, is easy to calculate and run in real time, minimizes hemodialysis costs, and saves run time.

Design Optimization of Hydraulic Machinery Based on ISIGHT Software: A Review of Methods and Applications

Optimizing hydraulic machinery is a critical research area within the field of fluid mechanics, aiming to enhance product design efficiency and improve performance while reducing development time. The application of intelligent algorithms and combinatorial optimization strategies has become increasingly prevalent in this domain, providing a comprehensive understanding of optimization-related theoretical developments. Recently, the emergence of ISIGHT software as a new technology for software integration platforms has opened new avenues for optimization in hydraulic machinery. By leveraging intelligent algorithms and combinatorial optimization strategies, ISIGHT software provides a comprehensive framework for optimizing hydraulic machinery. This paper serves as an introduction to ISIGHT software, highlighting its advantages in addressing optimization problems. It presents a detailed examination of the process and technology involved in hydraulic machinery optimization based on ISIGHT software, along with its practical application. Furthermore, the paper summarizes the future development trends of ISIGHT software, offering engineers a theoretical foundation and reference for optimizing hydraulic machinery performance. Overall, this paper provides a valuable contribution to the field of hydraulic machinery optimization, showcasing the potential of ISIGHT software.

Functional reconstitution of a steroidal hydroxylase from the fungus Thanatephorus cucumeris in Mycolicibacterium neoaurum for 15α-hydroxylation of progesterone

Hydroxylated steroids are important intermediates in the manufacture of clinically important medicines. Filamentous fungi play a vital role in steroid hydroxylation in industry, but they usually exhibit additional undesired hydroxylation activities. The lack of fungal molecular manipulation techniques hinders the development and application of efficient industrial hydroxylases. Therefore, for industrial production of hydroxylated steroids, it is increasingly necessary to obtain a more efficient biotransformation system with higher regio- and stereo-specific activities. In this study, we successfully developed an expression system for the Thanatephorus cucumeris CYP5150AP2 and its cognate cytochrome P450 reductase TcCPR in the engineered prokaryotic host, Mycolicibacterium neoaurum YS10 for 15α-hydroxylation of progesterone. Combined strategies were used to improve protein expression and product yield. Finally, the titer of 15α-OH progesterone reached 205.6 mg/L (with 500 mg/L progesterone as the substrate) using N-terminal sequence of MnCYP2 and CP6 promoter at 25 ℃. This is the first time a fungal P450 system was successfully functionally reconstituted in Mycolicibacterium neoaurum and significantly optimized to produce hydroxylated progesterone utilizing combinatorial optimization strategies. This work further confirmed the superiority of utilizing Mycolicibacteria as cell factories for hydroxylated steroids production via expression of eukaryotic cytochrome P450s in comparison to E. coli.

A Combinatorial Optimization Strategy for Performance Improvement of Stratum Ventilation Considering Outdoor Weather Changes and Metabolic Rate Differences: Energy Consumption and Sensitivity Analysis

Since occupants spend most of their time indoors, an energy-saving and comfortable indoor environment are particularly important. The differences in the metabolic rate of occupants make them have different requirements for their thermal environment. To save energy under the comprehensive needs of occupants for thermal environment, the combinatorial optimization strategy based on NSGA-II and improved the TOPSIS method is proposed in this study. Firstly, the physical model of the CFD simulation is verified by experiments. Secondly, the specific operation cases corresponding to combinations of different levels of factors are determined via the RSM method, and the ventilation performance prediction model considering the metabolic rate differences and outdoor weather changes is established. Thirdly, supply air velocities and temperatures are optimized by using Pareto-based NSGA-II; the Pareto optimal solution set under different outdoor temperatures is obtained. Finally, based on the Pareto optimal solutions at different outdoor temperatures, the optimal strategy under dynamic outdoor air temperature is obtained by improved TOPSIS by the CRITIC method. The optimization of ventilation parameters significantly improved the ventilation performance, and the results show that the predicted mean vote, energy consumption, vertical air temperature difference between head and ankle levels and the local mean age of air for different metabolic rates decrease by 64.1%, 4.74%, 24.83% and 7.39% on average, respectively. Moreover, the relative energy saving rate increases as the metabolic rate increases, and the strategy facilitates adaptation to outdoor weather changes and meets the individual needs of occupants for the indoor environment. This has important implications for achieving the global goal of energy efficiency and emission reduction.

Simulation-Based Optimization of Transport Efficiency of an Urban Rail Transit Network

It has been proven that developing urban rail transit (URT) is the key to solving the traffic problems in big cities, and transport efficiency is essential for scientific line planning and construction, operation management and sustainable development of urban rail transit. This paper focuses on the transport efficiency of the URT network and adopts shadow efficiency theory creatively to combine factors of passengers, capacity, and efficiency effectively. A quantitative calculation method of system utility based on “double time penalty” is proposed. According to the characteristics of Beijing URT, this paper puts forward a single optimization strategy and combinatorial optimization strategy and analyzes the results with DEA. The simulation of Beijing subway network proves the availability of the method and its optimization effects.

Radionuclide Reduction by Combinatorial Optimization of Microbial Extracellular Electron Transfer with a Physiologically Adapted Regulatory Platform.

Microbial extracellular electron transfer (EET) is the basis for many microbial processes involved in element geochemical recycling, bioenergy harvesting, and bioremediation, including the technique for remediating U(VI)-contaminated environments. However, the low EET rate hinders its full potential from being fulfilled. The main challenge for engineering microbial EET is the difficulty in optimizing cell resource allocation for EET investment and basic metabolism and the optimal coordination of the different EET pathways. Here, we report a novel combinatorial optimization strategy with a physiologically adapted regulatory platform. Through exploring the physiologically adapted regulatory elements, a 271.97-fold strength range, autonomous, and dynamic regulatory platform was established for Shewanella oneidensis, a prominent electrochemically active bacterium. Both direct and mediated EET pathways are modularly reconfigured and tuned at various intensities with the regulatory platform, which were further assembled combinatorically. The optimal combinations exhibit up to 16.12-, 4.51-, and 8.40-fold improvements over the control in the maximum current density (1009.2 mA/m2) of microbial electrolysis cells and the voltage output (413.8 mV) and power density (229.1 mW/m2) of microbial fuel cells. In addition, the optimal strains exhibited up to 6.53-fold improvement in the radionuclide U(VI) removal efficiency. This work provides an effective and feasible approach to boost microbial EET performance for environmental applications.

Optimal design of an autonomous underwater helicopter's shape based on combinatorial optimization strategy

In this study, the shape of the autonomous underwater helicopter (AUH) was optimized. AUH is a new type of disc autonomous underwater vehicle with good maneuverability and flexibility. In this paper, an optimization mathematical model is established, which takes the resistance coefficient as the optimization objective and the internal volume and the actual engineering demand as the constraints. The optimization mathematical model of AUH is solved by using two combinatorial optimization strategies. Two combinatorial optimization strategies use experimental design algorithm and multi Island genetic algorithm (MIGA) as global search algorithm, and sequential quadratic programming algorithm as local search algorithm. And based on the back propagation (BP) neural network, the sample points are trained and fitted, and the cloud map of the resistance coefficient distribution in the design space is obtained. The optimization results show that compared with the initial resistance coefficient, the first optimal solution reduces by 9.14%, and the second optimal solution reduces by 9.53%. So the resistance performance of AUH is significantly improved, which proves the feasibility of the method proposed in this paper. This provides theoretical support for the shape optimization design of AUH, which will improve the motion performance of AUH in practical engineering applications.

QoS-Aware Energy-Efficient MicroBase Station Deployment for 5G-Enabled HetNets

The increasing energy consumption is a legacy of the fast improvement of ICT (Information and Communication Technology). It is also contrary to the current energy conservation and emission reduction concept. There are several reasons for high energy consumption. Among them, we find that the increase in base station density of the 5G heterogeneous network (5G HetNets) is prominent. We present a micro base station deployment strategy in 5G HetNets for obtaining high energy efficiency. It optimizes target values as are trade-offs at different user distribution probabilities to improve adaptation to different user distribution scenarios. An energy deployment algorithm based on high efficiency for micro base stations is considered as jointly optimizing micro base station’s number, deployment location, and power configuration. Simulation results show that the proposed combinatorial optimization strategy effectively improves the system energy efficiency compared to the deployment method, regardless of the power factor. Moreover, it verifies that the micro base station power affects the energy efficiency in HetNets.

Improved Energy Storage Properties Achieved in (K, Na)NbO3‑Based Relaxor Ferroelectric Ceramics via a Combinatorial Optimization Strategy

AbstractAlthough ceramic dielectric materials have been extensively explored owing to their numerous advantages, there are still obstacles in the collaborative enhancement of recoverable energy density (Wrec) and efficiency (η). In this work, a combinatorial optimization strategy is proposed to optimize energy storage properties of (K, Na)NbO3‐based ceramics, that is, drive a specific temperature region between the temperature of maximum dielectric constant and the Burns temperature to room temperature under the guidance of phase field simulation to induce the polar nanoregions, then further improve the breakdown strength by repeated rolling process. As a result, an ultrahigh Wrec of 6.7 J cm−3 and a high η of 92% at 600 kV cm−1 are achieved simultaneously in the 0.85K0.5Na0.5NbO3‐0.15Bi(Zn2/3Ta1/3)O3 ceramic prepared by repeated rolling process, together with excellent temperature stability under 400 kV cm−1 over a temperature range of 25 to 150 °C, outperforming all reported (K, Na)NbO3‐based energy storage ceramics. This approach should also be generalizable for designing high‐performance dielectrics for electrical energy storage applications.

Shape Exploration and Multidisciplinary Optimization Method of Semirigid Nearing Space Airships

This paper describes a shape exploration and multidisciplinary optimization method of unconventional semirigid nearing space airships. The approach includes several improved mutually interacting disciplines, viz., aerodynamics, structure, propulsion, pressure control, energy, avionics, and electric cable. To describe the nonstreamlined envelope shape of the semirigid nearing space airships, a parameterization methodology involving 12 variables is proposed. And an aerodynamics surrogate model is established using the results of computational fluid dynamics simulations. To obtain the configurations of airship, detailed improved subsystem models are proposed. And a combinatorial optimization strategy is established to search for the optimal solutions. The approach can directly obtain the optimal general configurations such as envelope shape and size, as well as parameters of subsystems such as the required number of valve and air blowers. Optimization results show that the payload capacity increases 16.8% for the airship with same volume. Besides, effects of payload capacity of mass and power are investigated. Finally, sensitivity analysis on the subsystem parameters shows that the volume of semirigid airship is most sensitive to five parameters, viz., areal density of outer envelope, areal density of inner envelope, energy density of lithium battery, propulsion efficiency, and density of carbon fiber material, in turn.

Optimal Power Dispatch of Dispersed Sources in Direct-Current Networks with Nonlinear Loads

The problem of the optimal power dispatch of dispersed generators in direct-current networks under the presence of nonlinear loads (constant power terminals) is addressed through a combinatorial optimization strategy by using a master-slave solution methodology. The optimal power generation in the dispersed is solved in the master optimization stage through the application of the vortex-search algorithm. Each combination of the power outputs at the dispersed generation sources is provided to a power flow methodology known as the hyperbolic power flow approach for direct current networks. The main advantage of the proposed optimization method corresponds to the possibility of solving a complex nonlinear programming problem via sequential quadratic programming, which can be easily implemented at any programming language with low computational effort and high-quality results. The computational tests of the master-slave optimization proposal are evaluated in a 21-bus system, and the numerical results are compared with the implementation of the exact nonlinear programming model in the General Algebraic Modeling System (i.e., GAMS). All the computational results are conducted through the MATLAB programming environment licensed by Universidad Tecnologica de Pereira for academic usage.

Energy storage performance of K0.5Na0.5NbO3-based ceramics modified by Bi(Zn2/3(Nb0.85Ta0.15)1/3)O3

The damage of lead-based ceramics to our environment and health completely hindered their industrial applications. K0.5Na0.5NbO3 (KNN) ceramic material is considered as a good substitute for lead-free ceramics because of its high dielectric constant, excellent piezoelectric properties, high Curie temperature and sustainability. However, it is challenging to achieve their high energy-storage performances because of their large energy loss density (Wloss) under an applied electric field. Thus, this work proposes a combinatorial optimization strategy of inducing polar nano-regions and improving breakdown strength (BDS) to enhance the energy-storage performances of the KNN-based ceramics. As a result, we obtained a record high recoverable energy-storage density (Wrec) value of 7.4 J·cm−3 for the Bi(Zn2/3(Nb0.85Ta0.15)1/3)O3 (BZNT)-modified KNN ceramic mainly due to its enhanced BDS. Furthermore, remarkable temperature stability was obtained for the 0.90KNN-0.10BZNT ceramic over a wide temperature range (20–180 °C). Remarkably, the first-order reversal curve (FORC) confirmed the excellent energy-storage performances of the 0.90KNN-0.10BZNT ceramic, which we ascribed to its enhanced relaxation behavior. Additionally, 0.90KNN-0.10BZNT material exhibited excellent pulsed charge/discharge properties of the high power density (184.84 MW·cm−3) and fast discharge time (46.3 ns). Our results demonstrated a novel strategy for improving the energy-storage performances of dielectric ceramics. This strategy could be used to design other or similar materials for various applications.

Artificial Intelligence as a Combinatorial Optimization Strategy for Cellulase Production by Trichoderma stromaticum AM7 Using Peach-Palm Waste Under Solid-State Fermentation

The use of agro-industrial waste as substrate to produce enzymes has been widely studied in order to adjust the high productions of these biocatalysts with a reduction of the final cost in an ecologically correct process in the industry. This is the first work to use peach-palm (Bactris gasipaes Kunth.) waste for the production of carboxymethylcellulases (CMCase) by the Trichoderma stromaticum AM7 under solid-state fermentation (SSF) using the artificial neural network and genetic algorithm (ANN-GA) to optimize the cellulase production. ANN-AG was used to determine the optimal influence of nitrogen source concentration (2.46%), time (12 days), and temperature (26 °C) on the cellulase production with 98% of efficiency for algorithm prediction of endoglucanase activity. The predicted and experimental values of the CMCase activity in U/g of dry initial substrate (gds) were 122.9 and 120.0, respectively. After all optimization processes, the variation of these parameters allowed a final increase of 31.58-fold when compared to the initial fermentation. Moreover, the treatment of peach-palm waste with T. stromaticum AM7 endoglucanase had an effect on the degradation of the fibers analyzed by scanning electron microscopy and the saccharification of the waste by releasing reducing sugar (807.80 mg/g) for 8 h of incubation. These results are very promising to waste valorization and the biotechnological and industrial applications of cellulase of the T. stromaticum AM7 to food processes and biofuel production.

Pyridin-2(1H)-ones as HIV-1 NNRTIs: a combinatorial optimization strategy

Pyridin-2(1H)-ones as HIV-1 NNRTIs: a combinatorial optimization strategy

Superpixel Segmentation Based on Square-Wise Asymmetric Partition and Structural Approximation

Superpixel segmentation aims at grouping discretizing pixels into high-level correlative units and reducing the complexity of subsequent tasks, e.g., saliency detection and object tracking. Existing superpixel segmentation algorithms mainly focus on maintaining the geometrical information, while neglecting the irregular structure of superpixels. In this paper, a superpixel segmentation method is proposed to generate approximately structural superpixels with sharp boundary adherence and comprehensive semantic information. The superpixel segmentation is formulated as a square-wise asymmetric partition problem, where the semantic perceptual superpixels are recorded in a square level to preserve abundant semantic information and save storage simultaneously. Moreover, in order to achieve regular-shape superpixel units to better adhere to image boundaries and contours, a combinatorial optimization strategy is devised to achieve an optimal combination of squares and isolated pixels. Experimental comparisons with some state-of-the-art superpixel segmentation methods on the public benchmarks demonstrate the effectiveness of the proposed method quantitatively and qualitatively. In addition, we have applied the method to brain tissue segmentation to illustrate superior performance.

Online Vehicle Routing With Neural Combinatorial Optimization and Deep Reinforcement Learning

Online vehicle routing is an important task of the modern transportation service provider. Contributed by the ever-increasing real-time demand on the transportation system, especially small-parcel last-mile delivery requests, vehicle route generation is becoming more computationally complex than before. The existing routing algorithms are mostly based on mathematical programming, which requires huge computation time in city-size transportation networks. To develop routes with minimal time, in this paper, we propose a novel deep reinforcement learning-based neural combinatorial optimization strategy. Specifically, we transform the online routing problem to a vehicle tour generation problem, and propose a structural graph embedded pointer network to develop these tours iteratively. Furthermore, since constructing supervised training data for the neural network is impractical due to the high computation complexity, we propose a deep reinforcement learning mechanism with an unsupervised auxiliary network to train the model parameters. A multisampling scheme is also devised to further improve the system performance. Since the parameter training process is offline, the proposed strategy can achieve a superior online route generation speed. To assess the proposed strategy, we conduct comprehensive case studies with a real-world transportation network. The simulation results show that the proposed strategy can significantly outperform conventional strategies with limited computation time in both static and dynamic logistic systems. In addition, the influence of control parameters on the system performance is investigated.

OptRAM: In-silico strain design via integrative regulatory-metabolic network modeling.

The ultimate goal of metabolic engineering is to produce desired compounds on an industrial scale in a cost effective manner. To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. However, most of these strain optimization algorithms utilize a metabolic network alone, with few approaches providing strategies that also include transcriptional regulation. Moreover previous integrated approaches generally require a pre-existing regulatory network. In this study, we developed a novel strain design algorithm, named OptRAM (Optimization of Regulatory And Metabolic Networks), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both metabolic genes and transcription factors. OptRAM is based on our previous IDREAM integrated network framework, which makes it able to deduce a regulatory network from data. OptRAM uses simulated annealing with a novel objective function, which can ensure a favorable coupling between desired chemical and cell growth. The other advance we propose is a systematic evaluation metric of multiple solutions, by considering the essential genes, flux variation, and engineering manipulation cost. We applied OptRAM to generate strain designs for succinate, 2,3-butanediol, and ethanol overproduction in yeast, which predicted high minimum predicted target production rate compared with other methods and previous literature values. Moreover, most of the genes and TFs proposed to be altered by OptRAM in these scenarios have been validated by modification of the exact genes or the target genes regulated by the TFs, for overproduction of these desired compounds by in vivo experiments cataloged in the LASER database. Particularly, we successfully validated the predicted strain optimization strategy for ethanol production by fermentation experiment. In conclusion, OptRAM can provide a useful approach that leverages an integrated transcriptional regulatory network and metabolic network to guide metabolic engineering applications.

Combinatorial optimization of CO2 transport and fixation to improve succinate production by promoter engineering.

To balance the flux of an engineered metabolic pathway to achieve high yield of target product is a major challenge in metabolic engineering. In previous work, the collaborative regulation of CO2 transport and fixation was investigated with co-overexpressing exogenous genes regulating both CO2 transport (sbtA and bicA) and PEP carboxylation (phosphoenolpyruvate (PEP) carboxylase (ppc) and carboxykinase (pck)) under trc promoter in Escherichia coli for succinate biosynthesis. For balancing metabolic flux to maximize succinate titer, a combinatorial optimization strategy to fine-tuning CO2 transport and fixation process was implemented by promoter engineering in this study. Firstly, based on the energy matrix a synthetic promoter library containing 20 rationally designed promoters with strengths ranging from 0.8% to 100% compared with the widely used trc promoter was generated. Evaluations of rfp and cat reporter genes provided evidence that the synthetic promoters were stably and had certain applicability. Secondly, four designed promoters with different strengths were used for combinatorial assembly of single CO2 transport gene (sbtA or bicA) and single CO2 fixation gene (ppc or pck) expression. Three combinations, such as Tang1519 (P4 -bicA + pP19 -pck), Tang1522 (P4 -sbtA + P4 -ppc), Tang1523 (P4 -sbtA + P17 -ppc) with a more than 10% increase in succinate production were screened in bioreactor. Finally, based on the above results, co-expression of the four transport and fixation genes were further investigated. Co-expression of sbtA, bicA, and ppc with weak promoter P4 and pck with strong promoter P19 (AFP111/pT-P4 -bicA-P4 -sbtA + pACYC-P19 -pck-P4 -ppc) provided the best succinate production among all the combinations. The highest succinate production of 89.4 g/L was 37.5% higher than that obtained with empty vector control. This work significantly enhanced succinate production through combinatorial optimization of CO2 transport and fixation. The promoter engineering and combinatorial optimization strategies used herein represents a powerful approach to tailor-making metabolic pathways for the production of other industrially important chemicals. Biotechnol. Bioeng. 2016;113: 1531-1541. © 2016 Wiley Periodicals, Inc.