Search Results (607)

Multi-objective optimization plays an important role in modern design and complex engineering applications. However, achieving an effective balance between the convergence and diversity of Pareto-optimal solutions remains challenging. This paper proposes a Sobol-driven Multi-objective Whale Migration Algorithm (SMOWMA), which extends the Whale Migration Algorithm within a non-dominated sorting and elite-selection framework. A maximin scrambled Sobol initialization scheme is first employed to improve the distribution of the initial population. An archive-guided adaptive Student-t flight mechanism is then incorporated into the leader-whale position update to dynamically balance global exploration and local exploitation. In addition, archive crowding information and archive-entry success feedback are jointly used to adjust the search behavior according to both environmental diversity and recent search performance. SMOWMA is evaluated on five widely used multi-objective benchmark suites, namely ZDT, DTLZ, WFG, UF, and CF, using four performance indicators: generational distance (GD), inverted generational distance (IGD), spacing (SP), and hypervolume (HV). The results, together with Friedman tests and Holm-adjusted Wilcoxon tests, demonstrate that SMOWMA achieves competitive overall performance in terms of convergence, diversity, and objective-space coverage, although its relative advantage remains problem-dependent. The practical applicability of SMOWMA is further examined using multi-objective welded-beam design formulations, a bi-objective four-bar truss design problem, and a five-objective car side-impact design problem. The engineering results show that SMOWMA can obtain competitive and stable approximation sets for constrained design problems with different numbers of objectives, supporting its effectiveness and applicability in multi-objective engineering optimization. Full article

Designing Low Earth Orbit (LEO) constellations for applications like Positioning, Navigation, and Timing (PNT) is a challenging multi-objective optimization challenge. Conventional metaheuristics often suffer from premature convergence due to their reliance on static adaptive rules, limiting their effectiveness in complex search space. To address this limitation, we proposed a hybrid framework where a Double Deep Q-learning Network (DDQN) agent learns a policy to adaptively control the key parameters of a Particle Swarm Optimization (PSO) algorithm. The proposed framework formulates Walker constellation optimization as an sequential parameter control problem. Based on constellation performance feedback, the DDQN controller jointly selects the inertia weight and acceleration coefficients of PSO, guiding the PSO to more effectively balance exploitation and exploration. In a regional design case for China, our algorithm demonstrated superior performance. Compared to a 120 satellites benchmark constellation, the optimized constellation achieved a 27% reduction in Geometric Dilution of Precision (GDOP), a 27.6% enhancement in navigation accuracy, and a 5% increase in coverage multiplicity. This work establishes a robust methodology for the automated and intelligent design of LEO systems, validating the potential of deep reinforcement learning methods for complex aerospace optimization problems. Full article

To address slow convergence, local optimum stagnation, and multi-objective imbalance problems for unmanned aerial vehicle (UAV) three-dimensional (3D) path planning in complex obstacle environments, an improved adaptive two-stage pigeon swarm optimization (IPIO) algorithm is proposed. Firstly, a hybrid initialization strategy integrating Latin hypercube sampling and obstacle avoidance constraints is adopted to improve initial population diversity and the quality of feasible solutions. Secondly, in the map compass stage, a linearly decreasing adaptive map factor and population diversity-based dynamic perturbation strategy are introduced to balance global exploration and local exploitation while preventing premature convergence. In the landmark stage, an inverse fitness weighting elite center updating mechanism and linearly decreasing elite quantity strategy are designed to enhance the guidance of high-quality individuals and accelerate convergence. A multi-objective fitness function integrating path length, obstacle avoidance safety, and flight smoothness is constructed, whose weight coefficients (${\omega }_{\mathrm{L}}=0.3$, ${\omega }_{\mathrm{C}}=0.5$, ${\omega }_{\mathrm{S}}=0.2$) are calibrated through parameter-sensitivity analysis and Pareto frontier comparison across six representative weight combinations. Combining ablation validation for each improved module, single-UAV multi-scenario tests, and preliminary multi-UAV trials, these coordinated improvements realize targeted optimization for UAV 3D flight characteristics. Specifically, the preliminary multi-UAV trials involve three UAVs performing independent trajectory planning in shared obstacle environments without explicit inter-UAV collision avoidance constraints, and the reported improvements are based on single-UAV experiments. Finally, comparative experiments are conducted with a standard 100 × 100 × 50 m space, and varying obstacle densities are demonstrated in six diverse 3D test scenarios, where the proposed IPIO achieves an average path length reduction of 12.8% and 15.3% compared to the standard PIO and PSO, respectively. The average fitness improvement is 14.2% over PIO, 16.8% over PSO, 19.5% over GWO, 24.1% over CO, and 38.7% over CS. Key path-quality metrics include a minimum obstacle clearance of 2.37 m, average smoothness cost of 0.34, average convergence time of 0.60 s, and computational cost of O(N*D*MaxIter). Statistical tests confirm that these improvements are significant (p < 0.05) in all tested scenarios. This study presents an efficient and robust algorithm for autonomous three-dimensional path planning of UAVs in complex obstacle environments. Full article

In advanced manufacturing, designing multi-pulse composite driving waveforms for piezoelectric inkjet (PIJ) printing presents a constrained, high-dimensional, physical black-box optimization challenge. The feasible jetting region within the 12-dimensional parameter space is highly sparse; furthermore, traditional unconstrained optimization algorithms are prone to triggering nozzle flooding or actuator fatigue damage. To overcome this bottleneck, this paper proposes CA-TuRBO-m, a closed-loop collaborative architecture based on classifier-assisted multi-trust region Bayesian optimization. This architecture reconstructs the deposition morphology features on the substrate into a composite visual feedback source that implicitly incorporates fluid dynamics. Furthermore, it repurposes a Random Forest classifier into a dynamically iterating physical safety topological gating mechanism to actively intercept high-risk parameter combinations. Simultaneously, a multi-trust-region parallel exploration mechanism is introduced to balance global exploration and local exploitation. Experimental results demonstrate that over 200 online physical printing iterations, the proposed architecture reduces the number of invalid prints leading to system failures to an average of 3.8, achieving a high effective sampling rate of 98.1%. Without relying on complex fluid dynamic models, this approach enables precise morphological control over droplets of varying sizes and mitigates printing defects, successfully achieving multi-target adaptive regulation within a limited budget on a single physical platform. Full article

The Anatolian Caracal Optimization Algorithm (ACOA), a swarm-based optimization approach inspired by nature, approximately describes the characteristic hunting strategy of the Anatolian caracal, shaped by its superior auditory–visual perception and extraordinary agility, using mathematical models. The algorithm structures this process into three main stages by integrating and modifying established search mechanisms within a sequential framework: (i) wide-area scanning (exploration), which supports a broad exploration of the solution space through prey-focused movements; (ii) tracking–approach, which uses Brownian (continuous, small steps) and Lévy (occasional long jumps) movements to support the transition from exploration to exploitation; and (iii) predatory leap (exploitation), which represents the Anatolian caracal’s jump with a dimensionless projectile-inspired formulation and supports the refinement of candidate solutions. ACOA has been benchmarked across the CEC 2017, CEC 2019, CEC 2020, and CEC 2022 test suites, including unimodal and multimodal functions with fixed and varying dimensions, as well as real-world engineering design problems, for a total of 131 functions. It has been compared against 11 recent and well-known optimization algorithms. The best mean performance is achieved by ACOA in 111 out of 131 cases. In terms of stability, consistently low standard deviation is maintained by ACOA across the majority of the evaluated functions, indicating a strong reproducibility of the obtained solutions across independent runs. These findings indicate that the proposed three-stage framework provides a competitive exploration–exploitation behavior and can offer effective and stable solutions for a wide range of benchmark and engineering optimization problems. Full article

Off-highway vehicle electrification requires traction motors combining high torque density with reliable performance across demanding duty cycles, yet finite-element-based optimization remains computationally demanding for broad design-space exploration. This study addresses the gap with a sensitivity-analysis-based, surrogate-assisted multi-objective optimization framework for a 12-pole/72-slot, 120 kW Interior Permanent-Magnet Synchronous Motor (IPMSM) for a compact wheel-loader drivetrain, coupling Ansys Motor-CAD with Ansys OptiSLang. A Latin Hypercube sensitivity study of thirteen geometric parameters identifies the dominant design drivers, and an evolutionary algorithm operating on the validated surrogate produces a Pareto-optimal set, from which the final design is selected using the CRITIC–TOPSIS method applied to finite-element-validated feasible designs. Relative to the baseline, the validated performance shows a 7.4% increase in continuous torque, a 4.0% increase in peak torque, a 4.0% increase in efficiency, and a 53.5% reduction in torque ripple, with mass essentially unchanged, while also revealing that surrogate predictions were markedly optimistic relative to the finite-element results. These findings demonstrate an efficient, reliable route to high-performance IPMSM design for off-highway applications. Full article

Background/Objectives: Oral solid dosage forms of traditional Chinese and ethnic medicines are currently undergoing modernisation. The objective of this study is to explore the scope for formulation variation arising from batch-to-batch fluctuations in intermediates, and to identify the factors influencing key quality attributes of honey-containing tablets. In this regard, a machine-learning-based predictive model is being formulated that will integrate and analyse formulation factors and the results characterised by the SeDeM expert system. Utilising the SeDeM index as a mediating variable, the study endeavours to establish a comprehensible and predictable stepwise research pathway to provide a foundation for industrial-scale upscaling. Methods: Twelve SeDeM expert systems were utilised to characterise honey-containing granules for formulation screening, to evaluate their suitability for use in traditional Chinese medicine honey-containing tablet systems, and to identify key limiting factors and the feasibility space affecting the quality of the final product; Based on the QBD philosophy, a TriAD (Tri-criterion Adaptive Design) design scheme was proposed, integrating the horizontal balance of orthogonal designs, the spatial coverage of uniform designs, and the parameter estimation efficiency of D-optimal designs into the experimental layout of the formulation feasibility space; Through further data aggregation, a multi-layer feature set comprising four formulation factors, six SeDeM indicators, and three critical quality attributes (CQAs) was constructed. The mediating effects of the SeDeM indicators were revealed through different pathways involving 37 combinations of simple, linear, and Bootstrap models. Furthermore, 180 linear and non-linear machine learning models (comprising 12 categories of algorithms) were trained to predict formulation and CQA outcomes, ultimately completing the design space mapping and validation. Results: The results show that the SeDeM parameters effectively bridge the CQA results of different honey formulations, with these indicators acting as selective mediators between formulation factors and CQAs. Compared with a pure data model relying solely on raw formulation variables, the introduction of SeDeM knowledge, combined with high-information-content samples obtained via TriAD, improved the predictive performance and robustness of the SeDeM–ML hybrid model in terms of disintegration time and hardness; its R²_LOO increased by 0.267 and 0.510, respectively, and the overall predictive space was significantly expanded. Experimental validation was conducted using formulations within the design space predicted by the optimal model; the results showed that both the prediction bias and the relative standard deviation were less than 5 percent. Conclusions: The present study demonstrates that SeDeM can not only be used to evaluate formulations of honey-containing TCM tablets but also serves as an intermediary bridge linking formulation factors, granule-mechanism variables, and tablet quality outcomes. TriAD, in turn, further translates the QbD philosophy into an actionable formulation space design, thereby providing a development pathway for honey-containing tablets that combines interpretability, predictability, and QbD consistency, and offers new insights for the industrial application of oral TCM preparations. Full article

Software-defined satellite networks (SDSNs) enhance resource utilization and flexibility in space-based networks by leveraging a global view and programmability. A highly reliable control plane is essential to sustain network operations. However, the highly dynamic topology and physical failures in Low Earth Orbit (LEO) environments can cause satellite node outages or inter-satellite link disruptions, leading to control plane interruptions and local load imbalances. To address this, we propose a switch migration mechanism for failure recovery and establish a multi-objective migration model that jointly optimizes control link delay, controller load variance, and normalized migration ratio. To accommodate distinct dynamic characteristics such as frequent topology changes, failure-intensive periods, and stable periods, we design two algorithms: a robust migration algorithm, DNSGA-II, which features population diversity maintenance and environmental awareness, and an efficient migration algorithm, IHAOAVOA, which integrates strong global exploration with powerful local exploitation. Simulation results show that IHAOAVOA rapidly converges under large-scale failures, achieving millisecond-level delay recovery and low normalized migration ratio overhead during failure-intensive periods, while DNSGA-II focuses on long-term load balancing and system stability during stable periods, effectively suppressing localized controller overload. By adopting IHAOAVOA during topology fluctuations or high-failure phases to reduce delay, and switching to DNSGA-II during stable phases to optimize load distribution, the overall network robustness can be improved under the evaluated failure scenarios. This work provides effective support for achieving highly reliable control in SDSNs under failure scenarios. Full article

The optimization of the serpentine belt drive system (SBDS) is characterized by conflicting objectives, strong nonlinearity and a complicated search space. Traditional multi-objective evolutionary algorithms tend to suffer from insufficient convergence, uneven solution distribution and limited local search capability when tackling problems with complex, degenerated and discontinuous Pareto fronts. To address these drawbacks, this paper proposes a decomposition-based multi-objective piranha predation optimization algorithm (MOPPOA/D). Taking MOEA/D as the basic framework, the proposed algorithm decomposes the multi-objective optimization problem into a set of scalar subproblems with diverse search directions. An evolutionary operator inspired by piranha predation behavior is introduced, which dynamically switches between global exploration and local exploitation according to population satiety. Candidate solutions are generated via straight-line search and spiral search. A total of sixteen three-objective benchmark problems from the DTLZ and WFG test suites are selected to compare MOPPOA/D with NSGA-II, MOEA/D, MOEA/D-DQN and MOEA/D-AWA. Experimental results reveal that MOPPOA/D achieves the optimal mean HV and IGD values on 11 and 8 test problems, respectively. Furthermore, MOPPOA/D is applied to parameter optimization of the tensioner in the SBDS. Engineering calculations show that the maximum ratio of dynamic tension amplitude to installation tension for belt spans decreases by 18.63%, while the maximum average pulley slip rate declines by 9.07%. Despite a 5.88% increase in the maximum tensioner swing amplitude, the value is still within the allowable engineering range. The results verify the effectiveness and engineering application potential of MOPPOA/D on complex multi-objective benchmarks and the design optimization of the SBDS. Full article

Palm-vein recognition is an attractive biometric modality for secure access control because its subcutaneous vascular patterns are difficult to observe and reproduce externally. However, existing studies optimize the recognition algorithm and the hardware accelerator in isolation, and rarely satisfy the on-chip memory and energy constraints of edge devices. This paper presents a hardware-aware co-design of a lightweight FPGA accelerator for palm-vein recognition, in which the network is shaped by the cost structure of the target fabric and the inference engine is organized around the resulting layer shapes. On the algorithm side, a hardware-aware neural architecture search with deployment cost terms is combined with divisor-aligned structured pruning and INT8 quantization-aware training. Structured pruning reduces the model parameters to 0.32 M and the MACs to 87.2 M while preserving recognition accuracy. On the hardware side, a task-specific design space exploration selects a $14\times 12$ systolic array and an output-stationary dataflow that keeps all feature maps and weights on chip and reduces the modeled buffer-access count by 34.6% relative to the best alternative stationary dataflow. Implemented on a Xilinx Zynq-7100 at 100 MHz, the deployed INT8 checkpoint attains an accuracy of 99.50%, with a PL inference latency of 33.03 ms and an energy efficiency of 33.27 FPS/W. Full article

Hardware–software co-design is a balanced strategy for computationally intensive algorithms such as decimal computing. It can provide several Pareto points for the development of embedded systems in terms of hardware cost and performance. In this study, we propose an efficient and accurate evaluation framework for decimal computing. The framework was designed and developed for hardware–software co-design decimal arithmetic using the RISC-V ecosystem. New binary and decimal-oriented instructions supported by an accelerator were developed. The framework can perform cycle-accurate analysis for performance and assess hardware overhead for co-design-based decimal arithmetic. Unlike previous studies that focused primarily on implementing and evaluating individual co-design methods, the proposed framework enables exhaustive hardware–software partition analysis at the building-block level, facilitating systematic exploration of the design space. We also evaluated the decimal floating-point multiplication Pareto points and identified a new Pareto point for hardware–software co-design-based decimal multiplication (Method-A) through an analysis with the proposed framework. Full article

Visual art and graphic design tasks, such as generating a harmonious color palette or arranging the elements of a page, can be naturally formulated as mathematical optimization problems whose objective functions are non-convex, multimodal, and non-differentiable. Metaheuristic algorithms are well-suited to such problems. The secretary bird optimization algorithm (SBOA) is a recently proposed bio-inspired metaheuristic that mimics the hunting and predator-escaping behaviors of secretary birds, and it has shown competitive performance. However, SBOA still suffers from insufficient population diversity, premature convergence, and an unbalanced transition between exploration and exploitation, which limits its accuracy on complex design problems. To overcome these limitations, this paper proposes a multi-strategy secretary bird optimization algorithm (MSSBOA) that integrates three improvement strategies. First, a good point set initialization is employed to generate a low-discrepancy initial population that covers the search space more uniformly and enriches population diversity. Second, a lens opposition-based learning strategy is applied to the inferior individuals to help the population escape local optima while preserving the elite. Third, an adaptive Cauchy–Gaussian mutation is imposed on the best individual to balance global exploration and local exploitation throughout the search. The performance of MSSBOA is comprehensively examined on the CEC2017 benchmark suite in 10, 30, 50, and 100 dimensions and compared with the basic SBOA and nine state-of-the-art algorithms; the results are analyzed by the Friedman test, the Nemenyi post hoc test and the Wilcoxon rank-sum test. MSSBOA is then applied to two representative visual-design optimization problems: aesthetic color-harmony palette generation and graphic-layout aesthetics optimization. In all cases, MSSBOA outperforms the basic SBOA and the competing algorithms in terms of convergence speed, stability, and solution quality, confirming its effectiveness for computational-aesthetics applications in art design. Full article

A cooperative guidance law based on the experience-guided multi-agent proximal policy optimization (E-MAPPO) algorithm is proposed for multiple unmanned aerial vehicles (UAVs) to track dynamic points of interest in civilian applications, such as collaborative search and rescue and environmental monitoring. In multi-UAV cooperative tracking, accurate arrival-time coordination is important for improving collaborative task execution, but it remains challenging because of continuous action spaces, target maneuvering, uncertain time-to-go estimation, and inefficient exploration in multi-agent reinforcement learning. Specifically, a multi-UAV cooperative guidance environment is formulated, and the problem is modeled as a Markov decision process. To address the challenges of large action spaces and poor convergence in multi-agent reinforcement learning, an experience-guided MAPPO framework is introduced to enhance training efficiency and policy stability. Different from standard MAPPO, the proposed E-MAPPO introduces proportional-navigation-guided experience only during the early training stage to guide exploration, while the final policy is still optimized through the MAPPO objective. Subsequently, a composite reward function is designed by integrating distance-based heuristic terms with auxiliary guidance signals, thereby improving exploration efficiency and facilitating coordinated rendezvous and tracking of dynamic references. Comparative simulations with cooperative proportional navigation guidance (CPNG), sliding mode control (SMC), and standard MAPPO are conducted under different target motion scenarios. The results show that E-MAPPO reduces the average convergence step by 17.07% compared with MAPPO. In the straight-moving target scenario, E-MAPPO reduces the cooperative time error by 55.10% compared with CPNG and by 8.33% compared with MAPPO. In the S-type maneuvering target scenario, E-MAPPO reduces the cooperative time error by 55.81% compared with CPNG and by 9.52% compared with MAPPO. Monte Carlo experiments further verify its effectiveness and robustness. Additional robustness tests under Gaussian measurement noise, observation bias, and communication delay show that the proposed method maintains acceptable tracking accuracy and cooperative timing performance under different uncertainty conditions. In addition, the results indicate that the proposed method generalizes well to different types of maneuvering targets. Full article

Aiming at the engineering problems of composite roll, pitch, and yaw pose errors of the roadheader body induced by floor undulation and geological variation, as well as insufficient anchoring accuracy caused by the incapability of traditional 1–3-degree-of-freedom (DOF) drilling–anchoring arms in dynamic error compensation during coal mine roadway excavation and bolting, this paper proposes an inverse kinematics solving method for a 5-DOF drilling–anchoring arm based on an improved Deep Deterministic Policy Gradient (DDPG) algorithm. Firstly, the modified Denavit–Hartenberg (MDH) approach is adopted to establish a full-link kinematic model incorporating body pose errors, where the drill rod length, mounting offset, and world coordinate transformation are fully considered. Secondly, an Actor–Critic dual-network architecture tailored for drilling and anchoring tasks is constructed with an 11-dimensional state space and a 5-dimensional action space. The coupling optimization between body pose errors and joint adjustments is realized by designing a hierarchical gradient reward function, a dynamic noise decay exploration strategy, and an optimal state restart mechanism. Finally, 1000 episodes of training and verification are carried out on a Python 3.10 simulation platform. The simulation results reveal that the average end-effector position error of the improved algorithm reaches 0.71 mm, and the deflection angle toward the roof is less than 1°, which outperforms the specified industrial standard. The proposed method realizes real-time compensation for dynamic body pose errors and provides crucial technical support for intelligent excavation and anchoring in underground coal mines. Full article

In engineering practice, different requirements often give rise to distinct product designs. For the specific case of multi-UUVs, small-scale vehicles are typically designed with a rotational body shape to ensure superior hydrodynamic performance, whereas large-scale vehicles are often configured with a near-rectangular body shape to satisfy the demands of substantial payload capacity. These two tasks share a portion of common variables, while each also maintains its own task-specific variables. When each task is optimized independently, redundant computational efforts are incurred and inherent similarities among tasks remain unexploited, which frequently leads to suboptimal solutions. Typical multitask optimization algorithms assume completely heterogeneous tasks and therefore become inefficient when applied to this kind of partially heterogeneous problem. To address this, a manifold alignment and hierarchical surrogate-assisted transfer optimization algorithm (MAHSTO) is proposed in this work. In MAHSTO, an implicit knowledge transfer strategy is developed via manifold alignment. The design variables of both tasks are mapped onto a common low-dimensional latent space via manifold alignment, which enables implicit knowledge transfer across tasks. In addition, a hierarchical multisurrogate model with adaptive sampling is established. It comprises one shared global surrogate model that captures common trends across tasks and two task-specific surrogate models that focus on accurately fitting their respective tasks. Furthermore, an adaptive sampling criterion is adopted for different surrogate models to balance exploration and exploitation. Experiments on benchmark cases demonstrate that the proposed MAHSTO outperforms four state-of-the-art optimization algorithms, achieving the best performance in 58.3% of cases. Finally, MAHSTO is applied to the shape optimization of multi-UUVs. The results further verify its competitiveness in handling computationally expensive engineering problems. Full article