Non-dominated Sorting Genetic Algorithm II Research Articles

Multi-objective optimization design of NPR protection shell for hydrogen storage tank

In order to improve the safety of on-board hydrogen storage tanks during collision, a protective shell based on a negative Poisson’s ratio (NPR) core is designed in this paper. After analyzing and comparing the crashworthiness of three typical honeycomb structures, the concave hexagonal negative Poisson’s ratio honeycomb is selected as the energy-absorbing inner core of the hydrogen storage tank protection structure. The sensitivity analysis of the structural parameters of the NPR shell is conducted through orthogonal tests to identify parameters with a significant impact on crash performance. These parameters are then used as experimental variables for subsequent optimization design. Subsequently, a response surface approximation model between the optimization objective and the structural parameters is established based on the response surface method. Finally, the adaptive simulated annealing algorithm (ASA), neighborhood cultivation genetic algorithm (NCGA), and non-dominated sorting genetic algorithm-II (NSGA-II) are used to optimize the structural parameters, and the optimization results are verified by the whole-vehicle crash simulation. The simulation results demonstrate that all three algorithms achieve better optimization results, among which NCGA is more advantageous in improving the overall performance of the protective shell. After NCGA optimization, the specific energy absorption of the protective shell increases by 19.75%, the maximum collision force of the rigid wall decreases by 23.63%, and the maximum stress of the hydrogen storage tank body decreases by 22.77%. These findings indicate that the designed protection shell effectively improves the crashworthiness of the hydrogen storage tank during collisions.

Multi-objective optimization design of a circular core paper sandwich panel

Abstract Ensuring sufficient mechanical performance while enabling lightweight design is critical for utilizing paper sandwich panels in the furniture industry. To design lightweight sandwich panels that balance mechanical properties and cost, this study developed a circular core paper sandwich panel (CCPSP) and investigated its structural efficiency using multi-objective optimization. The response surface method (RSM) based on Box–Behnken design was utilized to establish mathematical models relating the paper tube spacing, inner diameter, and height to the out-of-plane compressive strength, density, and cost. The resulting models effectively revealed the coupled effects of the parameters on the responses. Subsequently, the models were optimized using the non-dominated sorting genetic algorithm II (NSGA-II) to find the Pareto optimal trade-offs between maximizing compressive strength while minimizing its density and cost. The optimization solution resulted in an optimal set of paper tube geometries that maximized the structural efficiency of CCPSP. Overall, lower tube height conferred superior structural efficiency, while tube spacing and diameter were constrained. The results highlight the potential of CCPSP as an efficient and sustainable material for furniture manufacturing, enabled by multi-objective optimization of its structure.

A Sustainable Dynamic Capacity Estimation Method Based on Bike-Sharing E-Fences

Increasing urban traffic congestion and environmental pollution have led to the embrace of bike-sharing for its low-carbon convenience. This study enhances the operational efficiency and environmental benefits of bike-sharing systems by optimizing electronic fences (e-fences). Using bike-sharing order data from Shenzhen, China, a data-driven multi-objective optimization approach is proposed to design the sustainable dynamic capacity of e-fences. A dynamic planning model, solved with an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II), adjusts e-fence capacities to match fluctuating user demand, optimizing resource utilization. The results show that an initial placement of 20 bicycles per e-fence provided a balance between cost efficiency and user convenience, with the enterprise cost being approximately 76,000 CNY and an extra walking distance for users of 15.1 m. The optimal number of e-fence sites was determined to be 40 based on the solution algorithm constructed in the study. These sites are strategically located in high-demand areas, such as residential zones, commercial districts, educational institutions, subway stations, and parks. This strategic placement enhances urban mobility and reduces disorderly parking.

An OGSM-based multi-objective optimization model for partner selection in fresh produce supply chain considering carbon emissions

Incorporating partner selection with environmental commitment is recognized as a crucial aspect of green development in fresh produce supply chains. This paper presents a multi-objective programming (MOP) model for partner selection considering carbon emissions based on the ‘Objectives, Goals, Strategies, Measures’ (OGSM) framework. The OGSM serves as a strategic scheme to identify the long-term green development vision of a supply chain, ensuring that the activities of supply chain partners are aligned with common goals. Driven by the OGSM design, the MOP model is formulated with three conflicting objective functions to maximize profit, minimize carbon emission, and maximize the quality of fresh produce. The model helps decision-makers address decisions on partner selection with determined logistics volumes, transportation modes and packaging types under carbon emission constraints. To tackle the NP-hard problem, we develop an effective hybrid meta-heuristic algorithm called ɛ-NSGA, which complements the advantages of the ɛ-constraint method and NSGA-II (Non-dominated Sorting Genetic Algorithm-II). The experimental results based on parameter calibration of the Taguchi method and the Kruskal–Wallis test demonstrate that the proposed ɛ-NSGA outperforms other four heuristic algorithms in terms of the performance indices of GD (Generational Distance), spacing, IGD (Inverted Generational Distance), and HV (Hyper Volume). The implementation of the OGSM-MOP together with ɛ-NSGA in a real-world apple supply chain illustrates the effectiveness and practicality of the model. Finally, managerial insights into the utilization of the model are discussed and suggestions for decision-making to fulfill corporate environmental obligations are provided.

Numerical algorithms for generating an almost even approximation of the Pareto front in nonlinear multi-objective optimization problems

A multiobjective optimization problem (MOP) returns a set of non-dominated points, the so-called Pareto front. Since this set is usually infinite, it is impossible to generate it completely in practice. Therefore, a discrete approximation of the Pareto front is created. One of the most important features of this approximation is a uniform distribution of points on the full Pareto front in order to present a wide variety of solutions to the decision maker who chooses a final solution. While a few algorithms consider this property, two algorithms based on the Pascoletti-Serafini (PS) scalarization approach are proposed. In addition, six well-known test problems with convex and non-convex Pareto fronts are considered to show the effectiveness of the proposed algorithms. Their results are compared with some algorithms including Normal Constraint (NC), Benson type, Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), S-Metric Selection Evolutionary Multiobjective Algorithm (SMS-EMOA), Differential Evolution (DE) with Binomial Crossover and MOEA/D-DE. The computational results on CPU time and reasonable distribution of points obtained on the Pareto front show that the presented algorithms perform better than other algorithms on these criteria. In addition, although the proposed algorithms compete closely with some algorithms in terms of CPU time, they have more non-dominated solutions and more appropriate distribution than they do in most problems.

A novel integrated optimization method of micrositing and cable routing for offshore wind farms

In traditional wind farm planning, the design of wind turbine locations and cable layouts is usually undertaken sequentially. However, this approach may potentially result in suboptimal solutions. While the increased spacing between wind turbines enhances power output by reducing wake losses, it also imposes a negative impact by raising cable costs. Addressing this challenge, we propose a novel multi-objective optimization model that simultaneously considers the micrositing of wind turbines and cable routing. A joint framework of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Mixed-Integer Linear Programming (MILP) is established to optimize the layout of wind turbine locations, points of connection, and cable paths. The results indicate that, compared to the traditional sequential optimization, our integrated optimization exhibits significant economic advantages since improved the balance between micro siting and cable routing. By strategically sacrificing a portion of power generation to reduce cable costs, the overall investment profitability can be remarkably improved, with a maximum gain equivalent to 10.02 % of the cable costs.

Mesh antenna cable net form finding optimization by an improved NSGA-II

The tension uniformity and surface accuracy of mesh antenna cable net are two well-accepted evaluation criteria for form finding. Traditional form finding techniques often rely on single-objective optimization and weighting factors. Although the Non-dominated Sorting Genetic Algorithm II (NSGA-II) can address the limitation, there are still challenges of wide distribution and insufficient precision of solutions. In the present work, a novel multi-objective optimization method based on an improved NSGA-II is introduced to conduct mesh antenna cable net form finding optimization. Based on the preference region (PR), a self-adaptive penalty function is proposed. The function is designed to steer the optimization process toward the PR, and enhancing the precision of the solutions. The extent of penalization for an individual solution depends on its positioning relative to the PR and is modified by a penalty coefficient that adapts dynamically to the population number within the PR. Additionally, a form finding method considering flexible frames is developed. The initial population for the flexible frame optimization is sourced from results of rigid frames, with the maximum truss node position error as the convergence criterion. Benchmark tests and case studies confirm that the improved NSGA-II enhances distributivity and convergence, while also providing form-finding results with higher surface accuracy and more uniform tension distribution.

Energy absorption of foam-filled TPMS-based tubular lattice structures subjected to quasi-static lateral crushing

Aluminium foam and TPMS-based tubular lattice structures (T-TLS) were of interest due to their superior energy absorption capabilities and inherent lightweight characteristics. However, their synergistic functionality in a composite form remained unexplored. In pursuit of augmenting the crashworthiness characteristics, aluminum foam was filled into T-TLS to form foam-filled T-TLS. FE models were established and validated using lateral crushing experiments. Foam-filled T-TLS exhibited higher energy absorption (EA) compared to the sum of empty T-TLS and foam filler, primarily attributed to the interaction between T-TLS and foam filler. Compared to single circular filled tubes (SCFT) with the same outer sizes and mass, foam-filled T-TLS revealed markedly enhanced crashworthiness, with EA and SEA values being 3.5–3.9 times and 4.0–4.7 times that of SCFT, respectively. Subsequently, parametric investigations underscored the evident influences of relative densities of T-TLS and foam filler, tube thickness and diameter on crashworthiness of foam-filled T-TLS. Finally, a multi-objective optimization was performed to derive optimized configurations by using non-dominated sorting genetic algorithm II (NSGA-II) and radial basis function (RBF) metamodels. In comparison with the baseline designs, the optimal results demonstrated significant enhancements in crashworthiness, with SEA increasing by 173.3 to 206.6 %. The present work provided a new paradigm in the design and development of advanced energy absorbers characterized by high-efficiency energy dissipation under lateral impact scenarios.

Optimal coordination of overcurrent relays in microgrids using meta-heuristic algorithms NSGA-II and harmony search

The emergence of distributed generation from renewable energy sources has led to the adoption microgrids as an alternative energy solution. However, implementing microgrids presents challenges, particularly in coordinating relay protection, due to factors like distributed generation sources, bidirectional power flow, variable short-circuit levels, and changes in network behavior. Although overcurrent relays (OCR) are frequently utilized in microgrid protection, a more adaptable strategy is needed as grid architectures transition from radial to non-radial. This paper proposes a new method to optimize the coordination of OCRs in microgrids by adjusting parameters like time multiplier settings (TMS), plug settings (PS), and characteristic curve selection. The study utilizes meta-heuristic techniques such as the harmony search algorithm (HSA) and the non-dominated sorting genetic algorithm-II (NSAGA-II) for optimal coordination. Simulations on a microgrid and bus test system demonstrate the effectiveness of the proposed approach in enhancing protection indicators like sensitivity, speed, selectivity, and reliability in microgrid operations. The results also indicate that the computation time of HSA is less than NSGA-II, but with an increase in DGs capacity, there is a continuous tendency to reduce the relay operation time.

An integrative approach to protein sequence design through multiobjective optimization.

With recent methodological advances in the field of computational protein design, in particular those based on deep learning, there is an increasing need for frameworks that allow for coherent, direct integration of different models and objective functions into the generative design process. Here we demonstrate how evolutionary multiobjective optimization techniques can be adapted to provide such an approach. With the established Non-dominated Sorting Genetic Algorithm II (NSGA-II) as the optimization framework, we use AlphaFold2 and ProteinMPNN confidence metrics to define the objective space, and a mutation operator composed of ESM-1v and ProteinMPNN to rank and then redesign the least favorable positions. Using the two-state design problem of the foldswitching protein RfaH as an in-depth case study, and PapD and calmodulin as examples of higher-dimensional design problems, we show that the evolutionary multiobjective optimization approach leads to significant reduction in the bias and variance in RfaH native sequence recovery, compared to a direct application of ProteinMPNN. We suggest that this improvement is due to three factors: (i) the use of an informative mutation operator that accelerates the sequence space exploration, (ii) the parallel, iterative design process inherent to the genetic algorithm that improves upon the ProteinMPNN autoregressive sequence decoding scheme, and (iii) the explicit approximation of the Pareto front that leads to optimal design candidates representing diverse tradeoff conditions. We anticipate this approach to be readily adaptable to different models and broadly relevant for protein design tasks with complex specifications.

A machine learning strategy for enhancing the strength and toughness in metal matrix composites

Particle-reinforced metal matrix composites (MMCs) are highly sought after for various applications due to their robust mechanical properties containing high strength and high elastic modulus. However, the introduction of highly rigid particles inevitably causes drastic degradation of toughness, resulting in the trade-off between strength and toughness in MMCs. This work proposed a multi-structure, multi-objective collaborative enhancement cyclic strategy that seamlessly integrates machine learning (ML) and genetic algorithms for realizing synergistic improvement of strength and toughness with the forward prediction and inverse exploration. By leveraging diverse configurations in MMCs such as laminate, network, and micro/nano hybrid structures, robust datasets were initially constructed. Random forest regression (RFR) was employed as surrogate model for forward prediction, accurately estimating the elastic modulus, yield strength, ultimate tensile strength, and toughness. Inverse exploration was also conducted using the combination of RFR and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to identify optimized structures with favorable strength-toughness matching. Utilizing this strategy, this work explores the microstructures with synergistic enhancement of strength and toughness, being confirmed by finite element analysis (FEA). The proposed optimization strategy, demonstrated in particle-reinforced MMCs, offers a promising solution to the trade-off between strength and toughness and is highly transferrable to other material systems.

Multi-Objective Optimization Design of Dynamic Performance of Hydrofoil with Gurney Flap

The horizontal axis tidal turbine, as a crucial device for capturing tidal energy, has gained significant attention because it has better energy efficiency performance. Enhancing the performance of foils, a vital part of tidal turbine blades, can significantly improve tidal turbine performance. Among numerous methods to enhance the foil performance, the Gurney flap has gained significant attention due to its avoidance of complex structural design. Currently, there is limited research on optimizing the design of Gurney flaps while considering the dynamic performance of foils. In this study, the S809 foil with a blade cross-section was selected as the research subject, a multi-objective optimization design platform was created by integrating a multi-objective optimization algorithm with Computational Fluid Dy-namics (CFD) numerical simulation techniques. The objective of this platform is to enhance the dynamic performance of the hydrofoil by optimizing the geometric structure of the Gurney flap. The improvement of dynamic lift and the size of the dynamic stall hysteresis loop are used as objective variables in this study to evaluate the hydrofoil’s dynamic performance. The optimal Latin hypercube design method is used in the optimization process to choose sample locations, and the Kriging approximation model is used to determine the relationship between the design variables and the objective variables. Meanwhile, the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is used to create a multi-objective optimization platform for solving the optimization problem, and the optimized results are validated using CFD. Comparative validation results show that quantifying the dynamic performance during hydrofoil pitching oscillation and using the optimal Latin hypercube design method and Kriging approximation model for optimizing the Gurney flap structure is rational and accurate. This study explores the mechanism of the Gurney flap through in-depth CFD numerical simulations and finds that the Gurney flap affects the flow characteristics at the hydrofoil’s trailing edge, thereby influencing the performance. It increases the pressure difference between the pressure and suction surfaces, thus enhancing the hydrofoil’s lift. Finally, this article provides three recommended parameters to improve the dynamic performance of the hydrofoil. This research can serve as a reference for the application of Gurney flaps in tidal turbine blade design.

DoFA: Adversarial examples detection for SAR images by dual-objective feature attribution

Synthetic aperture radar (SAR) classification models based on convolutional neural networks have high accuracy, but the models’ security is still threatened by adversarial examples. The high threat of adversarial examples derives from the invisible noise that can cause feature changes within the model. Among many adversarial examples detection methods, feature attribution that is sensitive to feature changes performs well in feature analysis. Unfortunately, the existing feature attribution-based detection methods cannot balance the computational efficiency and detection performance well due to the size and speckle noise of the images in SAR adversarial examples detection. In this work, we propose the Dual-objective Feature Attribution (DoFA) method by using the feature attribution scan block to find the suitable scan granularity. The DoFA method formulates the SAR adversarial examples detection issue as a dual-objective optimization problem and takes the number of subsamples generated by feature analysis and the area under curve (AUC) value of the logistic regression model as the objective functions while the feature scan block’s size, stride, padding, and the number of selected model layers are the decision variables. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to search for the scan block parameters with Pareto optimality so that the DoFA method can automatically obtain the best feature analysis granularity in different scenarios. The experimental results on the FUSAR-Ship dataset have shown that the proposed DoFA method has a higher AUC value under five adversarial attacks and a smaller number of subsamples than the existing adversarial examples detection method.

Multi- objective modeling and optimization of dissimilar laser welding by integrating an artificial intelligence predictive model with NSGA-II algorithm

This study presents a pioneering approach in laser welding of two dissimilar materials by integrating an Artificial Intelligence (AI)-based on predictive model with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimizing key performance characteristics of Nickel-based alloy and duplex 2205 stainless steel. Focusing on four primary input variables including laser power, welding speed, focal distance and deviation, the research aims to predict and optimize the responses including temperature field adjacent to the melt pool, penetration depth, and tensile strength of the joint according to the experimental results. The developed AI model first accurately forecasts these characteristics based on the inputs. This predictive accuracy is critical in defining the optimal target values. Considering the multidimensional nature of the problem, where enhancing one characteristic could compromise another, the study employs a Multi-Objective Optimization (MOO) strategy. This is where the innovative integration with NSGA-II becomes pivotal. Renowned for its efficiency in navigating multiple, potentially conflicting objectives, NSGA-II assists in achieving a balanced optimization of all target parameters. This method is adept at considering the complex interdependencies among various characteristics. The novelty of this work lies in its unique combination of AI for prediction and MOO for optimization and it is for the first time that machine learning is applied on this kind of alloys with four features and four targets.

Non-dominated sorting simplified swarm optimization for multi-objective omni-channel of pollution-routing problem

Abstract The activities of the traffic department mainly contribute to the generation of greenhouse gas (GHG) emissions. The swift expansion of the traffic department results in a significant increase in global pollution levels, adversely affecting human health. To address GHG emissions and propose impactful solutions for reducing fuel consumption in vehicles, environmental considerations are integrated with the core principles of the vehicle routing problem. This integration gives rise to the pollution-routing problem (PRP), which aims to optimize routing decisions with a focus on minimizing environmental impact. At the same time, the retail distribution system explores the use of an omni-channel approach as a transportation mode adopted in this study. The objectives of this research include minimizing total travel costs and fuel consumption while aiming to reduce GHG emissions, promoting environmental sustainability, and enhancing the convenience of shopping and pickup for customers through the integration of online and offline modes. This problem is NP-hard; therefore, the Non-dominated Sorting Simplified Swarm Optimization (NSSO) algorithm is employed. NSSO combines the non-dominated technique of Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the update mechanism of Simplified Swarm Optimization to obtain a set of Pareto-optimal solutions. Moreover, the NSSO, a multi-objective evolutionary algorithm, is adopted to address multi-objective problems. The PRP benchmark dataset is utilized, and the results are compared with two other multi-objective evolutionary algorithms: NSGA-II and Non-dominated Sorting Particle Swarm Optimization (NSPSO). The findings of the study confirm that NSSO exhibits feasibility, provides good solutions, and achieves faster convergence compared with the other two algorithms, NSGA-II and NSPSO.

Multi-Objective Optimization for Thrust Allocation of Dynamic Positioning Ship

Thrust allocation (TA) plays a critical role in the dynamic positioning system (DPS). The task of TA is to allocate the rotational speed and angle of each thruster to generate the generalized control forces. Most studies take TA as a single-objective optimization problem; however, TA is a multi-objective optimization problem (MOP), which needs to satisfy multiple conflicting allocation objectives simultaneously. This study proposes an improved multi-objective particle swarm optimization (IMOPSO) method to deal with the non-convex MOP of TA. The objective functions of reducing the allocation error, and minimizing the power consumption and the tear-and-wear of thrusters under physical constraints, are established and solved via MOPSO. To enhance the global seeking ability, the improved mutation strategy combined with the roulette wheel mechanism is adopted. It is shown through test data that IMOPSO converges better than multi-objective algorithms such as MOPSO and nondominated sorting genetic algorithm II (NSGA-II). Simulations are conducted for a DP ship with two propeller–rudder combinations. The simulation results with the single-objective PSO algorithm show that the proposed IMOPSO algorithm reduces thrust allocation errors in the three directions of surge, sway, and yaw by 48.48%, 39.64%, and 15.02%, respectively, and reduces power consumption by 44.53%, which demonstrates the feasibility and effectiveness of the proposed method.

Design of an aperiodic Cr/C reflective multilayer quarter-wave plate at the carbon K-edge

An aperiodic Cr/C reflective multilayer quarter-wave plate is designed and optimized at the carbon K-edge, by implementing both the genetic algorithm (GA) and the non-dominated sorting genetic algorithm II (NSGA-II). The performance is demonstrated using test free electron laser (FEL) pulses simulated by GENESIS to convert the imperfect linearly polarized light to circular polarization; the characteristics are verified by a numerical analysis of the Stokes parameter of the FEL pulses before and after penetration through the multilayer.

Strategic tree placement for urban cooling: A novel optimisation approach for desired microclimate outcomes

Trees are crucial elements for improving urban microclimates by providing cooling through shading, evapotranspiration, and windbreaks. To maximise their cooling effects, it is essential to strategically position the trees in optimal locations. However, research on optimising tree location and its impact on microclimates is limited owing to computational challenges and costs. This study introduces a novel method that employs three optimisation algorithms—i.e., Non-dominated Sorting Genetic Algorithm II (NSGA-II), Particle Swarm Optimisation (PSO), and Ant Colony Optimisation (ACO)—to identify the optimal locations for trees in urban environments to enhance urban thermal comfort. The research methodology involves simulating microclimate responses to tree placements optimised by each algorithm and assessing the results based on urban thermal comfort. The results underscore the efficacy of optimised tree locations, demonstrating that optimising tree locations can significantly reduce the Universal Thermal Comfort Index (UTCI) in urban areas. Furthermore, the findings suggest that the clustering of tree canopies has a compounding impact on these cooling benefits in urban areas. Notably, all three algorithms significantly improved UTCI. PSO demonstrated the rapid identification of effective tree configurations. However, ACO provided the most substantial reduction in air temperature, highlighting its potential as an effective tool for urban cooling. While efficient, NSGA-II plateaued earlier, suggesting its utility in scenarios where timely solutions are crucial.

Study on the Optimal Configuration of Battery Energy Storage System in Distribution Networks Considering Carbon Capture Units

Background: The implementation of Battery Energy Storage Systems (BESSs) and carbon capture units can effectively reduce the total carbon emissions of distribution networks. However, their widespread adoption has been hindered by the high investment costs associated with the BESSs and power generation costs of carbon capture units. Objective: The objective of this paper is to optimize the location and sizing of BESSs in distribution networks that comprise renewable power plants and coal-fired power units with carbon capture systems. The optimization process aims to minimize the grid’s impact from the configuration while maximizing economic cost savings and the benefits of reducing carbon emissions. Methods: A bi-layer optimization model is proposed to determine the configuration of BESSs. The upper layer of the model optimizes the size and operation strategy of the BESSs to minimize the configuration and power generation costs, using YALMIP and CPLEX optimization tools. Carbon emission reduction benefits are considered through deep peak-shaving and carbon tax. The lower layer of the model aims to optimizes the placement of the BESSs to minimize voltage fluctuation and network loss in the power grid. To achieve this, we improved the efficiency of the Nondominated Sorting Genetic Algorithm II (NSGA-II) to update the BESS’s placement. Results: The IEEE33-bus and IEEE118-bus systems were utilized for simulation and comparison in various scenarios. The findings demonstrate that the proposed configuration method can decrease the cost of investment and power generation. Furthermore, it reduces the degree of node voltage fluctuation and network loss in the distribution network. Conclusion: The study reveals that determining the optimal scale of BESSs can mitigate high energy consumption in carbon capture systems and improve the overall performance of power systems that integrate carbon capture technology and renewable power plants.

Research on Multi-Objective Optimization Scheduling Strategy for Cascaded Small Hydropower Stations Based on Improved HBA

Abstract Effective optimization scheduling strategy is the premise and key to improving the power generation and capacity benefits of cascade small hydropower stations (CSHS). However, the power generation of CSHS is significantly affected by complex hydraulic and electrical constraints. To effectively solve this problem, an improved honey badger algorithm (HBA) is proposed by updating the mutation strategy and introducing non-dominated sorting to achieve the multi-objective optimization scheduling solution of CSHS. The following improvements have been made to the standard HBA: Firstly, the Tent chaotic mapping is applied to the population initialization stage, its strong ergodicity and randomness ensure the randomness of the initialization stage and improve the global search ability of CSHS scheduling. Secondly, the powerful optimization ability and fast convergence speed of the Golden-Sine strategy make updating and mutation more efficient, greatly enhancing the local search ability of CSCH scheduling. And then combining the non-dominated sorting of the non-dominated sorting genetic algorithm-II (NSGA-II), an improved multi-objective Honey Badger Algorithm (IMOHBA) is further proposed to achieve multi-objective solutions for CSCH scheduling. Finally, abundant field experiments were tested for validation. The results expressed that compared to other algorithms, the effect of IMOHBA in CSHS scheduling can further increase power generation, while also improving the peak shaving ability of CSHS.