Continuous Optimization Research Articles

Reducing Measurement Costs by Recycling the Hessian in Adaptive Variational Quantum Algorithms

Abstract Adaptive protocols enable the construction of more efficient state preparation circuits in variational quantum algorithms (VQAs) by utilizing data obtained from the quantum processor during the execution of the algorithm. This idea originated with ADAPT-VQE, an algorithm that iteratively grows the state preparation circuit operator by operator, with each new operator accompanied by a new variational parameter, and where all parameters acquired thus far are optimized in each iteration. In ADAPT-VQE and other adaptive VQAs that followed it, it has been shown that initializing parameters to their optimal values from the previous iteration speeds up convergence and avoids shallow local traps in the parameter landscape. However, no other data from the optimization performed at one iteration is carried over to the next. In this work, we propose an improved quasi-Newton optimization protocol specifically tailored to adaptive VQAs. The distinctive feature in our proposal is that approximate second derivatives of the cost function are recycled across iterations in addition to optimal parameter values. We implement a quasi-Newton optimizer where an approximation to the inverse Hessian matrix is continuously built and grown across the iterations of an adaptive VQA. The resulting algorithm has the flavor of a continuous optimization where the dimension of the search space is augmented when the gradient norm falls below a given threshold. We show that this inter-optimization exchange of second-order information leads the approximate Hessian in the state of the optimizer to be consistently closer to the exact Hessian. As a result, our method achieves a superlinear convergence rate even in situations where the typical implementation of a quasi-Newton optimizer converges only linearly. Our protocol decreases the measurement costs in implementing adaptive VQAs on quantum hardware as well as the runtime of their classical simulation.

Construction of a digital twin model for incremental aggregation of multi type load information in hybrid microgrids under integrity constraints

In the multi type load information of hybrid microgrids, data loss or incompleteness may occur due to network congestion, signal interference, equipment failures, and other reasons. Especially with the continuous generation of new load data, gradually incorporating these new data into the existing aggregation process to achieve continuous updating and optimization of load information. Therefore, this article proposes a digital twin model construction method for incremental aggregation of multi type load information in hybrid microgrids under integrity constraints. The Leida criterion and cubic exponential smoothing method are used to preprocess various load data of hybrid microgrids, remove abnormal data, reduce data fluctuations, and make the data more interpretable. Establish integrity constraints for multiple load data of hybrid microgrids and extract load characteristics of hybrid microgrids. Based on these, establish a digital twin model for the incremental aggregation of multiple load information in a hybrid microgrid, and solve the model using an improved K-means algorithm to achieve continuous updating and optimization of load information. The experimental results show that the data sharing delay of this method is 0.12 s, the load is basically consistent with the actual value, and the relative error of the load data is 4%.

A Heterogeneous Multi-Satellite Dynamic Mission Planning Method Based on Metaheuristic Algorithms

The increasing demand for satellite communication necessitates efficient resource management, especially as next-generation high-throughput satellites (HTSs) face challenges in optimizing user connections. This paper presents a novel method that integrates a discretely improved Particle Swarm Optimization (PSO) algorithm with a dynamic task management framework to enhance satellite resource allocation efficiency. The PSO algorithm is adapted for discrete selection problems to maximize a fitness function based on user priority, user preference, and capacity satisfaction, thus ensuring accurate user–satellite matching. A main loop function iteratively updates user data and connection statuses, thus achieving continuous optimization of satellite connections. Through simulation studies, we validated the effectiveness of this method under dynamically changing user demands, demonstrating that the Discrete PSO algorithm significantly outperforms Simulated Annealing (SA) and the Genetic Algorithm (GA) in user satisfaction, maintaining levels above 0.996 even under high demand. Additionally, it effectively prioritizes high-demand users, ensuring their satisfaction remains above 0.95. Overall, our method enhances the management of daily communication tasks, significantly improving service quality and user satisfaction.

Optimal dimensioning of renewable energy generation and storage systems

With high energy demand and large available area, agricultural farms offer significant potential for renewable energy investments, like photovoltaic systems and electrical energy storages. However, the profitability of such investments depends strongly on self-consumption, so accurate planning requires computation-intensive simulation and optimization considering local consumption. This study presents a novel methodology for the optimal dimensioning and configuration of photovoltaic systems and electrical energy storages using efficient techniques from continuous non-linear optimization. Combining physical and economic models with measured consumption data, an investment’s net present value over 20 years is maximized. Using gradient-based solver WORHP, the simulation, optimal control, and optimal dimensioning of the local energy system are calculated simultaneously, allowing for efficient computation over an entire year of hourly data to capture both daily and seasonal variations.The approach is demonstrated with simple use cases, including an exemplary day of a dairy farm’s consumption, for which optimal systems with and without storage achieve 77% and 43% of autarky, respectively. Saturation effects of optimal plant size can be observed when sizes are large enough for optimal self-consumption but not expanded further for grid export. With energy storage, this saturation is reached at higher values. Optimizing photovoltaic plants with different orientations to match the specific consumption patterns characteristic of the dairy farm achieves similar autarky as a single plant while reducing investment costs by more than 20%. While thorough validation and comparison against heuristic methods predominantly used in the field is part of ongoing research, the presented use cases demonstrate the flexibility and efficiency of the proposed method and highlight its promise as a planning tool in the agricultural domain and beyond.

Changing Welding to Digital and Green - Actions and Communication

The implementation of Industry 4.0 is based, among other things, on the digitization and greening of manufacturing systems. Most traditional manufacturing systems are known for low productivity and efficiency. They are also known for the impact they have on the environment, with relatively high carbon footprints. The paper transfers a series of knowledge on the steps to be taken to transition a traditional manufacturing system to a digitized and green one. Customization of this discussion is done for manufacturing systems that have joining processes (welding and soldering). Each set of measures, whether aimed at the transition to digital or aimed at the transition to green, has two components: a technical component, directly related to the transformation of the hardware system (equipment, devices, measuring instruments) and a component auxiliary which is related to the transformation of the human resource, the modification of the working conditions and the continuous optimization of the newly created system. As no change is possible without removing employee misunderstandings and suspicions, the communication to be implemented must be clear, prompt, cover all levels of staff and continuously provide confidence by presenting successful cases and related beneficial figures.

Analysis of Spatial Agglomeration Characteristics of Logistics Enterprises in Henan Province based on Poi

As an important industry connecting urban and rural areas and serving production and life, logistics enterprises, as the micro-foundation of the development of logistics industry, have a profound impact on the spatial reconstruction of regional economic and social development. National Development and Reform Commission issued the "Fourteenth Five-Year Plan" modern logistics development and "Fourteenth Five-Year Plan" modern circulation system construction planning "put forward new requirements for the development of logistics industry, emphasizing the optimization of logistics spatial layout and speed up the construction of national logistics hub, especially pointed out that to make up for the shortcomings of the central and western regions. As an important base for the development of China's logistics industry, the spatial layout of logistics enterprises in Henan province not only affects the high-quality development of the logistics industry in the province, but also plays an important role in the improvement of the national logistics network. In this paper, based on POI data, the mean nearest neighbor method, local Moran scatter plot analysis and kernel density visualization technology are used to analyze the spatial agglomeration characteristics of logistics enterprises in 157 counties in Henan Province. It is found that the spatial distribution of logistics enterprises in Henan Province has gradually evolved from multi-point dispersion to a single-core and multi-core collaborative model with Zhengzhou as the core, and presents a significant spatial positive correlation and agglomeration trend. This change has improved the logistics efficiency and promoted the strengthening of regional economic ties. At the same time, the agglomeration trend of logistics enterprises in Henan Province continues to strengthen, from the initial agglomeration in the core area to the diversified diffusion pattern, showing the continuous optimization and reconstruction of logistics network in the province.

Effects of Different Dietary Combinations on Blood Biochemical Indicators and Rumen Microbial Ecology in Wenshan Cattle

With the continuous optimization of feed ingredients in livestock production, barley has garnered significant attention as a potential substitute for corn in feed. This study aims to investigate the effects of replacing part of the corn and soybean meal with barley, wheat bran, and rapeseed meal on Wenshan cattle, focusing on the rumen microbial community, blood physiological and biochemical indicators, and growth traits. Through an intensive feeding experiment with two different dietary ratios, we found that adding barley to the diet significantly reduced the host’s blood lipid concentration and significantly increased the height, body length, heart girth, and average daily weight gain of Wenshan cattle. Analysis of the rumen microbial community structure showed that the addition of barley significantly affected the relative abundance of Firmicutes, Proteobacteria, and Bacteroidetes, with the relative abundance of Spirochaetes being significantly lower than that of the control group (p < 0.05). The dominant bacterial groups mainly included Acinetobacter, Solibacillus, and Lysinibacillus. In summary, this study reveals the potential of different feed ingredient ratios involving barley, wheat bran, and rapeseed meal in the production performance of Wenshan cattle. By regulating blood physiology and improving the rumen micro-ecological structure, it provides new scientific evidence for optimizing livestock and poultry feeding management strategies. Future research will further explore the optimal application ratio of barley under different feeding conditions and its long-term impact on animal health and production performance.

Customer-Centric quality management: A framework for organizational excellence in SMEs

In an era where customer expectations are continually evolving, Small and Medium Enterprises (SMEs) must adopt innovative quality management practices to achieve organizational excellence. This review presents a customer-centric quality management framework designed specifically for SMEs, emphasizing the pivotal role of customer satisfaction in enhancing operational efficiency, product quality, and overall business performance. The framework is built on the premise that customer feedback and engagement are integral to quality management processes. By prioritizing customer needs and expectations, SMEs can develop quality assurance strategies that not only meet but exceed customer requirements. This approach involves systematic collection and analysis of customer feedback, enabling organizations to identify areas for improvement and innovate their offerings accordingly. Central to the framework are key components such as continuous improvement, employee involvement, and process optimization. Continuous improvement methodologies, like Lean and Six Sigma, are tailored to suit the unique challenges faced by SMEs, fostering a culture of quality that empowers employees at all levels. Engaging employees in quality initiatives cultivates a sense of ownership and accountability, essential for driving organizational change and enhancing service delivery. Furthermore, the framework emphasizes the importance of aligning quality management practices with customer-centric strategies. This alignment ensures that quality initiatives are not only reactive but also proactive, anticipating customer needs and adapting processes to deliver superior value. Incorporating customer relationship management (CRM) tools enhances communication, allowing SMEs to gather insights that inform quality improvements and strengthen customer loyalty. Implementing a customer-centric quality management framework can significantly impact an SME's competitiveness by enhancing product reliability, reducing defects, and improving customer satisfaction. Ultimately, this approach fosters organizational excellence, leading to increased market share and long-term sustainability. In conclusion, this framework equips SMEs with the necessary tools to integrate customer-centricity into their quality management processes, driving continuous improvement and excellence. Keywords: Customer-Centric Quality Management, Smes, Organizational Excellence, Customer Satisfaction, Continuous Improvement, Lean, Six Sigma, Employee Involvement, Process Optimization, CRM Tools, Competitiveness.

A step function based recursion method for 0/1 deep neural networks

The deep neural network with step function activation (0/1 DNNs) is a fundamental composite model in deep learning which has high efficiency and robustness to outliers. However, due to the discontinuity and lacking subgradient information of the 0/1 DNNs model, prior researches are largely focused on designing continuous functions to approximate the step activation and developing continuous optimization methods. In this paper, by introducing two sets of network node variables into the 0/1 DNNs and by exploring the composite structure of the resulted model, the 0/1 DNNs is decomposed into a unary optimization model associated with the step function and three derivational optimization subproblems associated with the other variables. For the unary optimization model and two derivational optimization subproblems, we present a closed form solution, and for the third derivational optimization subproblem, we propose an efficient proximal method. Based on this, a globally convergent step function based recursion method for the 0/1 DNNs is developed. The efficiency and performance of the proposed algorithm are validated via theoretical analysis as well as some illustrative numerical examples on classifying MNIST, FashionMNIST and Cifar10 datasets.

Machine learning‐aided process design using limited experimental data: A microwave‐assisted ammonia synthesis case study

AbstractAn open research question lies in how machine learning (ML) can accelerate the design optimization of chemical processes which are at very early experimental development stage with limited data availability. As an example, this article investigates the design of an intensified microwave‐assisted ammonia production reactor with 46 experimental data. We present an integrated approach of neural networks and synthetic minority oversampling technique to quantify the nonlinear input‐output relationships of this process. For ammonia concentration predictions at discrete operating conditions, the approach demonstrates 96.1% average accuracy over other ML methods (e.g., support vector regression 84.2%). The approach has also been applied for continuous optimization, identifying the optimal synthesis conditions at 597.37 K, 0.55MPa with feed flow rate of 1.67 ×10−3 m3/s kg and hydrogen to nitrogen ratio of 1 which is consistent with experimental observations. The data‐driven model enables to integrate this reactor with existing ammonia production infrastructure and benchmark with conventional techniques.

The Association of Glycemic Control Medication Regimens and Preoperative Fructosamine Among Total Joint Artrhoplasty Patients.

Although glycated hemoglobin A1C (HbA1c) has classically been used for glycemic control screening before surgery, fructosamine, a short-term glucose variability indicator, has been reported to be a more accurate predictor of postoperative periprosthetic joint infection among patients with diabetes mellitus (DM). Given the variability of diabetic medication management, this study aims to identify the associated effect of glycemic control medication regimen (GCMR) on the incidence rate and associated odds of abnormal preoperative fructosamine levels among diabetic primary total knee arthroplasty or total hip arthroplasty patients. Between 2017 and 2018, consecutive series of total hip arthroplasty and total knee arthroplasty patients were identified, and the final cohort included only diabetic patients. All patients reported preoperative HbA1c and fructosamine levels. GCMR categories included insulin, metformin, and other. Independent risk of GCMR and abnormal fructosamine levels (>293 µmol/L) were identified using multivariable logistic regression, while controlling for preoperative baseline factors including HbA1C. Among 420 patients, 15.7% (66/420) were diabetic, of whom 22.7% (15/66) reported an abnormal fructosamine level. Among patients requiring GCMR, 24.0% (18/75), 56.0% (42/75), and 77.7% (58/75) reported using insulin, other, and metformin, respectively. Multivariable logistic regressions demonstrated that insulin-dependent patients with DM reported a 1.71 (95% confidence interval [CI], 0.096 to 30.213, P = 0.716) increased odds of abnormal fructosamine levels compared with nonactive GCMR patients, whereas patients managed with metformin and other glycemic control medications reported a protective 0.48 (95% CI, 0.418 to 5.407, P = 0.549) and 0.32 (95% CI, 0.216 to 4.508, P = 0.393) decreased odds of abnormal fructosamine levels, respectively. In this study, insulin and other GCMR medications exhibited a trend for increased and decreased odds of having abnormal preoperative fructosamine levels while controlling for baseline HbA1c level compared with patients with DM without active GCMR. This association may be explained by multifactorial short-term glucose variability in insulin users, indicating the continued need and optimization of short-term glycemic variations instead of HbA1c.

A Pilot and Feasibility Study on a Mindfulness-Based Intervention Adapted for LGBTQ+ Adolescents.

(1) Background: Lesbian, gay, bisexual, transgender, queer and other gender and sexual minority-identified (LGBTQ+) adolescents face mental and physical health disparities compared to their heterosexual and cisgender counterparts. Mindfulness-based interventions (MBIs) may be a potential method to intervene upon health disparities in this population. This pilot study explores the initial acceptability and feasibility, along with the descriptive health changes of an online MBI, Learning to Breathe-Queer (L2B-Q), which was adapted to meet the needs of LGBTQ+ adolescents. (2) Methods: Twenty adolescents completed baseline and post-intervention assessments of mental health, stress-related health behaviors, physical stress, and LGBTQ+ identity indicators. In addition, the adolescents participated in a post-intervention focus group providing qualitative feedback regarding the acceptability of L2B-Q. (3) Results: L2B-Q demonstrated feasible recruitment and assessment retention, acceptability of content with areas for improvement in delivery processes, and safety/tolerability. From baseline to post-intervention, adolescents reported decreased depression and anxiety and improved intuitive eating, physical activity, and LGBTQ+ identity self-awareness with moderate-to-large effects. (4) Conclusions: These findings underscore the need and the benefits of adapted interventions among LGBTQ+ youth. L2B-Q warrants continued optimization and testing within the LGBTQ+ adolescent community.

How to expand the thermos cup market through network publicity and product positioning

With the steady growth of the global stainless steel thermos cup market, social platform marketing plays a key role in enhancing brand influence and product sales. Through the study of Standly's case, this paper first describes the strategy of making thermos cups into "fashion accessories", and Stanley has achieved remarkable market expansion by increasing the attention of female consumers; Then, the actual effect of this strategy is analyzed, that is, the sales volume of its star product Quencher series has increased greatly. Finally, suggestions on using new technologies and social media marketing are put forward to meet the needs of consumers and enhance brand influence. It is concluded that continuous innovation and optimization of market positioning are the key to maintain the leading position in the competition of vacuum flask industry. Research on marketing strategy is very important for enterprises to succeed in the rapidly changing market. It can not only help enterprises identify market opportunities and optimize resource allocation, but also meet customer needs, enhance brand influence, and ultimately promote the sustainable development and growth of enterprises.

Operational Excellence: Process optimization for competitive edge

This study explores the concept of operational excellence and the role of process optimization in gaining a competitive edge for organizations. Operational excellence involves the continuous improvement of business processes to increase efficiency, reduce waste, enhance quality, and improve overall performance. The study reviews established methodologies such as Lean, Six Sigma, and Total Quality Management (TQM) that provide structured approaches to achieving operational efficiency. It also examines the impact of emerging technologies, including artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT), in driving data-driven decision-making and enhancing process optimization. Additionally, the study highlights the importance of leadership in fostering a culture of continuous improvement and collaboration across departments, ensuring alignment with strategic objectives. The integration of sustainability into operational strategies is discussed as a means to not only improve efficiency but also meet corporate social responsibility goals. The relationship between process optimization and innovation is also explored, showing how operational excellence can free up resources for research and development (R&D) and foster a culture of innovation. Despite the focus on these established practices and technologies, the study acknowledges limitations, particularly the challenges faced by small and medium-sized enterprises (SMEs) and service oriented sectors in implementing these strategies. The study concludes that operational excellence, supported by technological advancements and a strong organizational culture, is essential for organizations to navigate competitive markets, improve customer satisfaction, and achieve long-term success. This research offers valuable insights for businesses aiming to enhance operational performance and sustain their competitive advantage through continuous process optimization and innovation.

Dynamic production optimization for offshore Electric Submersible Pump oilfields based on oil-water two phase virtual flow metering

Enhancing offshore oilfield production is critical, but optimizing production equipment parameters presents significant challenges due to the difficulty in obtaining accurate fluid properties and the necessity for precise equipment models. Multiphase flow calculations depend on preset fluid parameters, which are particularly challenging to determine in offshore environments. This paper proposes a virtual measurement method tailored for reservoir, well, and pipeline network models in offshore oilfields. The approach involves calibrating the Electric Submersible Pump (ESP) model and calculating the viscosity of oil-water emulsions to assess phase separation and fluid water content within the well. By utilizing revised data and production efficiency indicators, the optimal ESP operating frequency is determined to enhance offshore oilfield production. Adjusting the operating parameters based on these calculations enables continuous production optimization until the discrepancy between real-time data and computed results falls within the acceptable accuracy range. This method conserves substantial capital investment, enhances operational efficiency, and significantly boosts enterprise profitability.

Positive Strategies for Enhancing Elderly Interaction Experience in Smart Healthcare through Optimized Design Methods: An INPD-Based Research Approach

The breakthrough in artificial intelligence technology and the development of smart healthcare models have significantly improved modern healthcare services. However, the elderly population still faces numerous challenges. Therefore, the aim of this study is to enhance the interactive experience of elderly users and to propose effective design strategies through optimized design methods. Based on the INPD research methodology, the design process is divided into four stages. First, in the SET phase, product opportunity gaps are identified, followed by in-depth interviews and surveys to gather user needs. Second, the AHP method is used to establish a hierarchical model and judgment matrix to determine the subjective weights of each need, while the EWM method, based on survey data, determines the objective weights of each need. To ensure the scientific nature of the overall weight, a combined weighting approach is used, followed by a final prioritization of needs. Third, after translating user needs into design requirements, three design schemes are produced, and the TOPSIS method is used to calculate the weights and evaluate the optimal scheme. Fourth, the product opportunities are implemented and tested. The research results indicate that the proposed optimization design method is effective and not only reduces the barriers and challenges elderly users face when interacting with intelligent products but also enhances their overall experience. Moreover, it provides a practical approach to the sustainable development of smart healthcare. As an essential component of future healthcare services, the sustainability of smart healthcare will depend on a deep understanding of user needs and continuous optimization. The design strategy proposed in this study offers practical application value, improving elderly users’ satisfaction while also providing insights that may be useful for other smart services.

Influence of drying method on the shelf life of artisanal pasta

Efforts to optimize pasta drying often face challenges due to the complexity of operational variables. This study aimed to optimize conventional fixed-bed convective drying of artisanal pasta using a desirability function approach and assess the influence of drying methods on shelf life. Three methods were compared: optimized conventional fixed-bed convective drying (O-CFBCD), hot air microwave drying (HAMD), and convective tray drying (CTD). This study underscores the importance of exploring and regulating pasta quality parameters to meet both industry standards and consumer preferences for marketable products with sensory appeal. While all three drying methods show promise for producing quality pasta, there is a need for improved initial moisture control, particularly in the CTD condition, to ensure compliance with regulatory standards and mitigate microbiological risks associated with high water content. Over time, most sensory parameters exhibited enhancement, aligning with consumer expectations for high-quality products. This suggests that continuous monitoring and optimization of drying processes are essential for maintaining and improving pasta quality throughout its shelf life.

Computed Tomography-Driven Analysis of Particle Breakage Using a Coupled FDM-DEM Approach

This study proposes a refined approach for simulating the mechanical behavior of soils under high stress by integrating the Finite Difference Method-Discrete Element Method (FDM-DEM) with in-situ experiment. This novel framework incorporates advanced technologies such as flexible membrane and X-ray computed tomography (CT) to enhance the predictive capabilities of the simulation with physical insight. The FDM-DEM model adeptly simulates irregular particle shapes, capturing their interactions and the dynamics of particle breakage. The use of flexible membrane within the model further enriches this approach by enabling the capture of deformation responses in granular materials during shearing. Moreover, the adoption of CT technology facilitates continuous optimization of the simulation process. Validation through in-situ experiments has confirmed the model's effectiveness. The ability of the model to predict areas of high stress concentration—and their correlation with observed particle breakage patterns—underscores the utility of the enhanced FDM-DEM framework as a robust predictive tool for understanding the behavior of granular materials under shearing.

Review of the facilitators and barriers to adoption of biofortified foods and food products.

Biofortification - the process of increasing the concentrations of essential nutrients in staple crops - is a means of addressing the burden of micronutrient deficiencies at a population level via existing food systems, such as smallholder farms. To realise its potential for global impact, we need to understand the factors that are associated with decisions to adopt biofortified crops and food products. We searched the literature to identify adoption determinants, i.e. barriers to (factors negatively associated) or facilitators of (factors positively associated) adoption, of biofortified crops and food products. We found 41 studies reporting facilitator(s) and/or barrier(s) of adoption. We categorised the factors using the Consolidated Framework of Implementation Research 2.0, resulting in a set of factors that enable or constrain adoption of biofortified foods across twenty-four constructs and five domains of this meta-theoretical determinant framework from implementation science. Facilitators of orange sweet potato adoption included knowledge about importance, relative advantage, efficient production and management practices; barriers included lacking timely access to quality vines and market remoteness (28 studies total). Facilitators of vitamin A cassava adoption included awareness of its benefits and access to information; barriers included poor road networks and scarcity of improved technology including inadequate processing/storage facilities (8). Facilitators of high-iron bean adoption included farmers' networking and high farming experience; barriers included low knowledge of bean biofortification (8). Barriers to vitamin A maize adoption included low awareness and concerns regarding yield, texture and aflatoxin contamination (1). These barriers and facilitators may be a starting point for researchers to move towards testing implementation strategies and/or for policymakers to consider before planning scale-up and continuous optimisation of ongoing projects promoting adoption of biofortified crops and food products.

Design optimization of automotive carbon fiber composite material floor laminate based on PSO-BFO algorithm

In response to the current challenges of low precision and efficiency in the optimization of composite material layups, the insufficient lightweight of automotive body floors, and the high cost of carbon fiber composites, this study introduces an optimized design method for carbon fiber composite flooring layups. Based on implicit parametric technology, a hybrid PSO-BFO (Particle Swarm Optimization-Bacterial Foraging Optimization) algorithm is employed. This approach achieves an integrated optimization of materials, processes, and structures, thereby balancing and reducing costs. The SFE-CONCEPT is utilized to establish an implicit parameterization model for the body floor, which is validated through experiments and finite element simulation analysis. Concept design and modeling of the carbon fiber composite material floor laminate are performed. Continuous variable optimization is employed to determine the thickness, tile shape, and number of layers for the front, middle, and rear floors. A continuous variable discretization rounding strategy is used to obtain the discrete layer numbers for each laminate orientation of the composite material floor. The continuous fiber lamination strategy is applied to create different shared lamination regions. The PSO-BFO hybrid optimization method is proposed to optimize the lamination sequence as a multi-objective optimization, addressing the challenges of discrete lamination sequence, explosive combinations, and multiple variables in the optimization design of carbon fiber composite material floor laminates. The optimization results demonstrate improvements of 34.4% in floor quality M, 6.0% in static bending stiffness BS, and 5.3% in lightweight coefficient QLX using the proposed PSO-BFO method. PSO-BFO and PSO-GA (Particle Swarm Optimization-Genetic Algorithm) methods are more capable of obtaining global optimal solutions for complex optimization problems than single optimization algorithms. Still, the results obtained by the PSO-BFO method are more balanced.