Search Results (206)

Balancing convergence accuracy and population diversity remains a fundamental challenge in multi-objective optimization, particularly for complex and constrained engineering problems. To address this issue, this paper proposes a novel Multi-Objective Crested Porcupine Optimizer (MOCPO), inspired by the hierarchical defensive behaviors of crested porcupines. The proposed algorithm integrates four biologically motivated defense strategies—vision, hearing, scent diffusion, and physical attack—into a unified optimization framework, where global exploration and local exploitation are dynamically coordinated. To effectively extend the original optimizer to multi-objective scenarios, MOCPO incorporates a reference-point guided external archiving mechanism to preserve a well-distributed set of non-dominated solutions, along with an environmental selection strategy that adaptively partitions the objective space and enhances solution quality. Furthermore, a multi-level leadership mechanism based on Euclidean distance is introduced to provide region-specific guidance, enabling precise and uniform coverage of the Pareto front. The performance of MOCPO is comprehensively evaluated on 18 benchmark problems from the WFG and CF test suites. Experimental results demonstrate that MOCPO consistently outperforms several state-of-the-art multi-objective algorithms, including MOPSO and NSGA-III, in terms of IGD, GD, HV, and Spread metrics, achieving the best overall ranking in Friedman statistical tests. Notably, the proposed algorithm exhibits strong robustness on discontinuous, multimodal, and constrained Pareto fronts. In addition, MOCPO is applied to UAV path planning in four complex terrain scenarios constructed from real digital elevation data. The results show that MOCPO generates shorter, smoother, and more stable flight paths while effectively balancing route length, threat avoidance, flight altitude, and trajectory smoothness. These findings confirm the effectiveness, robustness, and practical applicability of MOCPO for solving complex real-world multi-objective optimization problems. Full article

Wood supply chains are complex, involving many stakeholders, intricate processes, and logistical challenges to ensure the timely and accurate delivery of wood products to customers. Weather-related variations in forest road accessibility further complicate operations. This paper explores the challenges faced by forest managers in targeting many delivery requirements—four or more. To address this, simulation-based optimization, using NSGA-III, a many-objective optimization algorithm, is proposed to simultaneously optimize often conflicting objectives primarily by minimizing delivery lead time, delivery deviations in backlogs, and delivery variation. NSGA-III enables the exploration of a diverse set of Pareto-optimal solutions that show trade-offs across a flexible set of four, or more, delivery objectives. A Discrete Event Simulation model is integrated to evaluate objectives in a complex wood supply chain. The implementation of NSGA-III within the framework allows forestry decision-makers to navigate between different harvest schedules and evaluate how they target a set of preference-based delivery objectives. The simulation can also provide detailed insights into how a specific harvest schedule affects the supply chain when post-processing possible solutions, facilitating decision making. This study shows that NSGA-III could substitute NSGA-II to optimize the wood supply chain for more than three objective functions. Full article

The rapidly evolving business landscape, driven by stringent energy conservation policies, compels construction firms to adopt energy-efficient project-centric structures, particularly in modern construction projects. These firms face a complex, multi-mode, resource-constrained, multi-project scheduling problem characterized by dynamic project arrivals and multiple resource constraints, including global, local, and non-renewable capacities. This environment pressures managers to simultaneously optimize the conflicting objectives of minimizing total project duration and total energy consumption. To address this challenge, we propose a novel multi-objective Smart Raccoon Family Optimization (SRFO) algorithm. The SRFO, a hybrid evolutionary approach, is designed to enhance global exploration and local exploitation. Its performance is boosted by integrating a non-dominated sorting mechanism, a dedicated energy-efficient search strategy, and enhanced genetic operators. The SRFO simultaneously optimizes two conflicting objectives: minimizing the total project duration and total energy consumption. This approach effectively integrates the unique constraint of off-site component production and on-site assembly within an intelligent scheduling framework. Empirical validation across benchmark problems and a real-world case study is conducted, comparing the SRFO with existing multi-objective approaches, such as NSGA-III, MOABC, and MOSMO. Performance is assessed using convergence and distribution metrics, augmented by TOPSIS-based multi-criteria decision-making. Results conclusively demonstrate that the proposed SRFO significantly outperforms existing approaches and offers a robust, high-quality solution for project management in energy-constrained environments. Full article

With the advancement of the “dual carbon” goal, the power system is accelerating its transition towards a clean and low-carbon structure, with a continuous increase in the penetration rate of renewable energy generation (REG). However, the volatility and uncertainty of REG output pose severe challenges to power grid operation. Traditional distribution networks face immense pressure in terms of scheduling flexibility and power supply reliability. Active distribution networks (ADNs), by integrating energy storage systems (ESSs), soft open points (SOPs), and demand response (DR), have become key to enhancing the system’s adaptability to high-penetration renewable energy. This work proposes a DR-aware scheduling strategy for ESS-integrated flexible distribution networks, constructing a bi-level optimization model: the upper-level introduces a price-based DR mechanism, comprehensively considering net load fluctuation, user satisfaction with electricity purchase cost, and power consumption comfort; the lower-level coordinates SOP and ESS scheduling to achieve the dual goals of grid stability and economic efficiency. The non-dominated sorting genetic algorithm III (NSGA-III) is adopted to solve the model, and case verification is conducted on the standard 33-node system. The results show that the proposed method not only improves the economic efficiency of grid operation but also effectively reduces net load fluctuation (peak–valley difference decreases from 2.020 MW to 1.377 MW, a reduction of 31.8%) and enhances voltage stability (voltage deviation drops from 0.254 p.u. to 0.082 p.u., a reduction of 67.7%). This demonstrates the effectiveness of the scheduling strategy in scenarios with renewable energy integration, providing a theoretical basis for the optimal operation of ADNs. Full article

In the construction of grouting holes in high-mud-content layers, high-pressure jet cleaning technology effectively cuts and removes soil and sediments from the strata. This research designs the structure of a high-pressure jet cleaning device and establishes a numerical simulation model for the high-pressure jet cleaning nozzle, conducting orthogonal simulation tests. Based on the data from these tests, a Backpropagation (BP) Neural Network-based numerical prediction model for the high-pressure jet cleaning flow field is developed, enabling the prediction of cleaning flow rates and pressures for different nozzle channel structure parameters. Targeting jet fluid velocity and cleaning pressure, parametric shape optimization is performed on the nozzle channel structure: key parameters are identified via Analysis of Variance (ANOVA) and sensitivity analysis; an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to establish a multi-objective optimization model, which exhibits superior convergence speed and solution diversity compared to the traditional algorithm. The optimal jet fluid velocity, cleaning pressure, and fluid structure parameter solution space for the high-pressure jet cleaning nozzle are obtained. Through simulation and experimental verification, it is found that with the same number of nozzles, the optimized design significantly enhances both the average cleaning flow rate and the cleaning pressure. Finally, a high-pressure jet cleaning nozzle and device are prototyped based on the simulation and optimization results and tested in the grouting test area A2W-2-III-6 of the South-to-North Water Diversion Project Xiong’an Storage Reservoir Project. This study provides a scientific basis and technical support for the application of high-pressure jet cleaning technology in complex geological formations. Full article

Accurate representation of structural geometry, physical properties, and boundary conditions remains a major challenge in the finite element (FE) modeling of jacket structures. To address these difficulties, this study proposes a multi-source data synchronous updating framework for FE models based on the Genetic Aggregated Response Surface (GARS) and the Non-dominated Sorting Genetic Algorithm III (NSGA-III). First, vibration and strain tests were simultaneously conducted on an indoor jacket platform structure to obtain its natural frequencies and local dynamic strain responses. The measured data were processed to extract the first three natural frequencies and dynamic strain time histories at two critical locations, which served as reference data for model updating. An initial FE model of the jacket platform structure was then established, and sensitivity analysis was performed to identify the parameters requiring updating. Based on the simulation results, GARS was employed to construct response surface models describing the relationship between structural responses (natural frequencies and local strains) and the parameters to be updated, replacing FE analyses during optimization. Finally, NSGA-III was utilized to achieve synchronous updating of the FE model using multi-source data, and the updated geometric parameters were experimentally validated. The results demonstrate that errors in the first three natural frequencies of the FE model were reduced from 3.44%, −7.31%, and 5.88% to −0.02%, −0.43%, and 0.08%, respectively. Strain errors in the local region decreased from 12.96% and 10.33% to 1.4% and 2.1%. The corrected geometric parameters showed errors less than 1.85% when compared with actual measurements. These findings verify the accuracy and applicability of the proposed method for updating jacket platform FE models, providing an effective reference for model updating of in-service offshore structures. Full article

Integrating connected and autonomous vehicle dedicated lanes (CAVDLs) into existing road networks under mixed traffic conditions presents a complex challenge, often requiring a balance of multiple conflicting objectives. This study develops a dynamic multi-objective optimization framework, formulated as a mixed-integer nonlinear programming problem, to determine the optimal network-wide deployment of CAVDLs. The framework integrates three core components: an endogenous demand model capturing connected and autonomous vehicle (CAV)/human-driven vehicle (HDV) mode choice, a multi-class dynamic traffic assignment model that adjusts lane capacity based on CAV-HDV interactions, and an NSGA-III algorithm that minimizes total system travel time, total emissions, and construction costs. Results of a case study indicate the following: (i) sensitivity analysis confirms that user value of time is the most critical factor affecting CAV adoption; the model’s endogenous consideration of this variable ensures alignment between CAVDL layouts and actual demand; (ii) the proposed Pareto-optimal solution reduces total travel time and emissions by approximately 31% compared to a no-CAVDL scenario, while cutting construction costs by 23.5% against a single-objective optimization; (iii) CAVDLs alleviate congestion by reducing bottleneck duration and peak density by 36.4% and 16.3%, respectively. The developed framework provides a novel and practical decision-support tool that explicitly quantifies the trade-offs among traffic efficiency, environmental impact, and infrastructure cost for sustainable transportation planning. Full article

The fourth industrial revolution, driven by the Energy Internet (EI), is having a profound impact on economic development and way of life. With the growth of EI networks, the integration of numerous energy devices poses challenges across different domains. To address this, we propose a self-adaptive NSGA-III algorithm (SA-NSGA-III) for multi-objective optimization of the EI topology, accounting for connectivity, robustness, and operational efficiency. We construct an initial scale-free topology based on real-world EI characteristics and optimize it while preserving its scale-free nature. The method incorporates an adaptive dynamic reference point generation strategy and an adaptive population selection mechanism. Experimental results demonstrate that SA-NSGA-III achieves a 29.5% fitness improvement, outperforming other multi-objective optimization algorithms in both optimization performance and convergence efficiency across various network scales and densities. Full article

This work presents a unified framework for multiobjective analog circuit optimization that combines surrogate modeling, uncertainty-aware evolutionary search, and adaptive high-fidelity verification. The approach integrates ensemble regressors and graph-based surrogate models with a closed-loop multi-fidelity controller that selectively invokes SPICE evaluations based on predictive uncertainty and diversity criteria. The framework includes reproducible caching, metadata tracking, and process- and Dask-based parallelism to reduce redundant simulations and improve throughput. The methodology is evaluated on four CMOS operational-amplifier topologies using NSGA-II, NSGA-III, SPEA2, and MOEA/D under a uniform configuration to ensure fair comparison. Surrogate-Guided Optimization (SGO) replaces approximately 96.5% of SPICE calls with fast model predictions, achieving about a 20× reduction in total simulation time while maintaining close agreement with ground-truth Pareto fronts. Multi-Fidelity Optimization (MFO) further improves robustness through adaptive verification, reducing SPICE usage by roughly 90%. The results show that the proposed workflow provides substantial computational savings with consistent Pareto-front quality across circuit families and algorithms. The framework is modular and extensible, enabling quantitative evaluation of analog circuits with significantly reduced simulation cost. Full article

In arid regions, rainfall is scarce, summer-concentrated, and prone to extreme events, while evaporation exceeds precipitation, creating fragile ecosystems that need scientific stormwater management for flood resilience. Sponge cities, through the implementation of green infrastructure, can alleviate urban flooding, improve rainwater utilization, and enhance the urban ecological environment. Under the “dual carbon” target, sponge city construction has gained new developmental significance. It must not only ensure core functions and minimize construction costs but also fully leverage its carbon reduction potential, thereby serving as a crucial pathway for promoting urban green and low-carbon development. Therefore, this study focused on Xining, a typical arid city in Northwest China, and couples the Non-dominated Sorting Genetic Algorithm-III (NSGA-III) with the Storm Water Management Model (SWMM) to construct a multi-objective optimization model for Low Impact Development (LID) facilities. The layout optimization design of LID facilities is conducted from three dimensions: life cycle cost (LCC), rainwater utilization rate (K), and carbon emission intensity (CI). Hydrological simulations and scheme optimizations were performed under different design rainfall events. Subsequently, the entropy-weighted TOPSIS method was utilized to evaluate and compare these optimized schemes. It is shown by the results that: (1) The optimized LID schemes achieved a K of 76.2–80.43%, an LCC of 2.413–3.019 billion yuan, and a CI of −2.8 to 0.19 kg/m²; (2) Compared with the no-LID scenario, the optimized scheme significantly enhanced hydrological regulation, flood mitigation, and pollutant removal. Under different rainfall return periods, the annual runoff control rate increased from 64.97% to 80.66–82.23%, with total runoff reduction rates reaching 46.41–49.26% and peak flow reductions of 45–47.62%. Under the rainfall event with a 10-year return period, the total number of waterlogging nodes decreased from 108 to 82, and the number of nodes with a ponding duration exceeding 1 h was reduced by 62.5%. The removal efficiency of total suspended solids (TSS) under the optimized scheme remained stable above 60%. The optimized scheme is highly adaptable to the rainwater management needs of arid areas by prioritizing “infiltration and retention”. Vegetative swales emerge as the primary facility due to their low cost and high carbon sink capacity. This study provides a feasible pathway and decision-making support for the low-carbon layout of LID facilities in arid regions. Full article

In this work, three novel optimization algorithms—collectively referred to as the BxR algorithms—and their multi-objective versions, referred to as the MO-BxR algorithms, are applied to diverse heat transfer systems. Five representative case studies are presented: two single-objective problems involving a heat exchanger network and a jet-plate solar air heater; a two-objective optimization of Y-type fins in phase-change thermal energy storage units; and two three-objective problems involving TPMS–fin three-fluid heat exchangers and Tesla-valve evaporative cold plates for LiFePO₄ battery modules. The proposed algorithms are compared with leading evolutionary optimizers, including IUDE, εMAgES, iL-SHADEε, COLSHADE, and EnMODE, as well as NSGA-II, NSGA-III, and NSWOA. The results demonstrated improved convergence characteristics, better Pareto front diversity, and reduced computational burden. A decision-making framework is also incorporated to identify balanced, practically feasible, and engineering-preferred solutions from the Pareto sets. Overall, the results demonstrated that the BxR and MO-BxR algorithms are capable of effectively handling diverse thermal system designs and enhancing heat transfer performance. Full article

This paper introduces a novel quantum-inspired algorithm for numerical multiobjective optimization, uniquely integrating the multilevel structure of qudits with principles of controlled thermonuclear fusion. Moving beyond conventional qubit-based approaches, the algorithm leverages the qudit’s higher-dimensional state space to enhance search capabilities. Fusion-inspired dynamics—modeling particle interaction, energy release, and plasma cooling—provide a powerful metaheuristic framework for navigating complex, high-dimensional Pareto fronts. A hybrid quantum-classical version of the algorithm is presented, designed to exploit the complementary strengths of both computational paradigms for improved efficiency in solving dynamic multiobjective problems. Experimental evaluation on standard dynamic multiobjective benchmarks demonstrates clear performance advantages. Both the quantum-inspired and hybrid variants consistently outperform leading classical algorithms such as NSGA-III, MOEA/D and GDE3, as well as the quantum-inspired NSGA-III, in key metrics: identifying a greater number of unique non-dominated solutions, ensuring superior uniformity along the Pareto front, maintaining stable convergence across generations, and achieving higher accuracy in approximating the ideal solution. Full article

High-nitrogen austenitic stainless steels (HNASS) require compositional strategies that simultaneously maximize corrosion resistance and microstructural stability while suppressing delta (δ) ferrite and deleterious precipitates. Here, an explainable multi-objective design workflow is developed that couples thermodynamic descriptors from the Calculation of Phase Diagrams (CALPHAD) approach—using both equilibrium and Scheil solidification calculations—with machine learning surrogate models, random forest (RF) and Extreme Gradient Boosting (XGBoost), trained on 60,480 compositions in the Fe–C–N–Cr–Mn–Mo–Ni–Si space. The physics-informed feature set comprises phase fractions; transformation and precipitation temperatures for δ-ferrite, chromium nitride (Cr₂N), sigma (σ) phase and M₂₃C₆ carbides; liquidus and solidus temperatures; and the pitting-resistance equivalent number (PREN). The RF model achieves consistently low prediction errors, with a PREN root-mean-square error (RMSE) of ≈0.004, and exhibits strong generalization. Shapley additive explanations (SHAP) reveal metallurgically consistent trends: increasing nitrogen (N) suppresses δ-ferrite and promotes Cr₂N; carbon (C) promotes M₂₃C₆; molybdenum (Mo) promotes the σ-phase; and C and silicon (Si) widen the freezing range. Using the trained surrogate as the objective evaluator, the non-dominated sorting genetic algorithm III (NSGA-III) builds Pareto fronts that minimize the δ-ferrite range, Cr₂N, σ-phase, M₂₃C₆ and the freezing range (ΔT) while maximizing PREN. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is then applied to rank the Pareto-optimal candidates and to select compositions that combine elevated PREN with controlled precipitation windows. This workflow is efficient, reproducible and interpretable and provides actionable composition candidates together with a transferable methodology for data-driven stainless steel design. Full article

This paper proposes a novel optimization framework for Magnetic Gear Compound Machines (MGCMs) that integrates parameter grouping and surrogate modeling to address challenges of high-dimensional design spaces and conflicting objectives. The core methodological contribution is a new parameter grouping strategy employing sensitivity analysis and partial correlation coefficients, which systematically classifies design parameters into high-, medium-, and low-impact groups. This approach achieves a 60% reduction in optimization dimensionality while preserving essential electromagnetic relationships. Latin Hypercube Sampling (LHS) is coupled with high-fidelity Maxwell 2D transient simulations to construct an accurate Kriging surrogate model, which is then integrated with the NSGA-III algorithm for efficient Pareto front identification. Comprehensive simulations demonstrate the framework’s exceptional performance. The sensitivity-based optimized design achieves an 85.5% reduction in inner rotor torque ripple (0.091), maintains 90.3% of the original torque output (475.100 N·m), and preserves 94.8% of the induced electromotive force (399.578 V), yielding an optimal objective function value of −0.901 that indicates superior overall performance improvement. In comparison, the correlation-based approach provides an 84.5% torque ripple reduction (0.097) with 97.7% torque retention (514.166 N·m) and 86.0% voltage preservation (362.739 V), corresponding to an objective function value of −0.841. Both grouping strategies significantly reduce computational cost by approximately 60% compared to conventional single-stage optimization methods. This research establishes an effective optimization paradigm for MGCMs, successfully resolving the fundamental trade-off between power density maximization and operational stability, with promising applications in electric propulsion and renewable energy systems. Full article

Climate variability and growing competition for limited water resources have made the operation of cascade reservoirs increasingly complex. This study develops a comprehensive system-based multi-objective optimization and evaluation framework that simultaneously integrates five goals: power generation, water supply, ecological protection, navigation reliability, and flood control as a constraint. The framework employs the NSGA-III evolutionary algorithm to address the high-dimensional optimization problem and combines Analytic Hierarchy Process (AHP), Entropy Weight Method, and TOPSIS to integrate subjective expertise with objective data in the evaluation of alternatives. Applied to the lower Jinsha River cascade under wet, normal, and dry hydrological scenarios, the model reveals distinct conflicts between hydropower and ecological or navigational requirements, partial synergies between hydropower and water supply, and tension between ecological and supply demands. Hydrological variability alters these relationships, with wet years intensifying conflicts and dry years heightening supply and ecological pressures. Functional differentiation among reservoirs is also evident, with Baihetan and Xiluodu showing pronounced power–ecology tensions, while Xiangjiaba primarily supports supply and navigation. The study not only advances the theory of multi-objective decision-making in water resources systems but also offers actionable guidance for sustainable reservoir governance and regional development. Full article