Discovering novel molecules within the vast chemical space is a central scientific challenge, increasingly delegated to deep generative models. However, the prevailing "black box" paradigm, built on continuous latent spaces, faces a fundamental mismatch between smooth optimization landscapes and inherently discrete molecular structures, often limiting global exploration. To overcome these limitations, we introduce Janus, a framework that recasts molecular design as a transparent, physics-inspired combinatorial optimization problem. At its core, Janus employs a Transformer-based autoencoder with a regularized binary bottleneck to map molecules into a compact discrete latent space. This representation enables the reformulation of molecular generation and optimization as a Quadratic Unconstrained Binary Optimization (QUBO) problem. This approach unlocks synergistic capabilities. For molecular generation, Janus leverages classical and quantum annealers to efficiently traverse the structured energy landscape, enabling the global discovery of diverse chemical scaffolds. Crucially, for molecular optimization, it moves beyond blind search by utilizing quantifiable feature interactions as machine-discovered SAR rules. This allows for rational, interpretable optimization─selectively modifying latent bits to enhance properties. Benchmarking against state-of-the-art methods reveals that this approach achieves superior multiobjective performance while preserving scaffold integrity, avoiding the structural fragmentation common in heuristic baselines. We validate the feasibility of the workflow on a quantum annealer and demonstrate its efficacy in drug-like property optimization. By unifying powerful combinatorial exploration with deep model interpretability, Janus establishes a robust framework for rational, quantum-assisted molecular design.

Recent advancements in machine learning (ML) algorithms and applications have made it possible for ML models to solve complex problems, such as reactor core loading optimization, which represents a multiobjective optimization problem with a high degree of freedom. This study aims to provide a proof of concept for an ML-based core loading optimization scheme aimed at research reactors. As a case study we selected the High Flux Reactor in Petten, the Netherlands. Two optimization algorithms are used in this study: genetic algorithm (GA) and reinforcement learning (RL). The goal is to increase the thermal neutron flux at specific locations in the reactor core while adhering to established safety constraints. The optimization schemes also utilized neural network–based surrogate models to substitute for the computationally intensive core calculations. The surrogate models are used to predict core parameters (such as the neutron flux, control rod position, and heat flux) for any given loading pattern. Our results show that ML-based core loading optimization has the potential to become a viable alternative to the traditional core optimization methods. Both the GA and RL methods were able to generate core loading patterns where the neutron flux was similar in most target locations to the results obtained with the traditional method.

Connected Banking System Optimizer (CBSO) is a recently proposed meta-heuristic inspired by inter-bank financial transactions. It models inter-bank transaction behaviors across four sequential stages, collectively balancing exploration and exploitation. When confronted with complex landscapes, however, CBSO exposes three critical weaknesses: limited global-search capacity, an abrupt phase switch that disrupts the exploitation-exploration balance, and a pronounced tendency toward premature stagnation. These shortcomings become more conspicuous as problem complexity rises, undermining the algorithm's ability to locate the true optimum. To overcome these deficiencies, this paper presents an enhanced variant-ECBSO-which incorporates three complementary mechanisms: dominant group guidance strategy, guided learning strategy, and hybrid elite strategy. The ECBSO algorithm is comprehensively evaluated on the CEC 2017 benchmark suite and on real-world constrained engineering problems, outperforming CBSO, ISGTOA, EMTLBO, LSHADE, APSM-jSO, GLS-MPA, ESLPSO, ACGRIME, RDGMVO in all comparisons. Statistically, ECBSO secures first place across every test case, delivering Friedman ranks of 2.069, 2.138, 2.690, and 2.759, thereby confirming its superior convergence accuracy, search reliability, and optimization precision across diverse landscapes.

As deep neural networks (DNNs) become integral to critical applications, protecting their intellectual property (IP) has become paramount. Neural network watermarking is a technique that embeds unique identifiers into models, asserting ownership and deterring unauthorized use. However, sophisticated attacks can deactivate or remove these watermarks without significantly compromising model performance, undermining current protection strategies. In this paper, we introduce the first method for reactivating deactivated neural network watermarks in altered DNN models without requiring access to the original model parameters or training data. By formulating the reactivation process as an optimization problem, we employ projected gradient descent to identify new trigger inputs that restore the embedded watermark. Regularization techniques are incorporated to ensure these triggers resemble legitimate inputs, enhancing both stealth and practicality. Through experiments on various benchmark datasets and model architectures, we demonstrate the effectiveness of our method against common model alterations, including fine-tuning, pruning, and surrogate model attacks. Our work addresses a critical gap in DNN IP protection, offering a robust and practical solution for watermark reactivation. This empowers model owners to assert their rights even in the face of advanced adversarial tactics.

In recent years, several quantum algorithms have been proposed for addressing combinatorial optimization problems. Among them, the Quantum Approximate Optimization Algorithm (QAOA) has become a widely used approach. However, reported limitations of QAOA have motivated the development of multiple algorithmic variants, including recursive hybrid methods such as the Recursive Quantum Approximate Optimization Algorithm (RQAOA), as well as the Quantum-Informed Recursive Optimization (QIRO) framework. In this work, we integrate the Quantum Alternating Operator Ansatz within the QIRO framework in order to improve its quantum inference stage. Both the original and the enhanced versions of QIRO are applied to the Minimum Vertex Cover problem, an NP-complete problem of practical relevance. Performance is evaluated on a benchmark of Erdös-Rényi graph instances with varying sizes, densities, and random seeds. The results show that the proposed modification leads to a higher number of successfully solved instances across the considered benchmark, indicating that refinements of the variational layer can improve the effectiveness of the QIRO framework.

Abstract An innovative Reheat Brayton–Regenerative Kalina–Vapor Absorption Refrigeration integrated system was investigated from energy, exergy, and environmental perspectives. The system model, developed in MATLAB with temperature-dependent thermophysical properties and considering combustion dissociation, was subjected to parametric analysis and multi-objective optimization. The influence of Brayton cycle pressure ratio (rp), turbine inlet temperature (BTIT), and Kalina turbine inlet temperature (KTIT) on power output, energy utilization factor (EUF), power density (PD), exergy efficiency, dissociation effects, and specific carbon emission rates (SCER) was assessed. The results show that increasing rp from 3 to 46 at BTIT 1100°C and KTIT 340°C, enhances EUF from 19.8% to 49.98%, exergy efficiency from 14% to 42.41% and PD from 64.9 to 1838.4 kW-s/m3. Higher rp suppresses dissociation, as reflected by an increase in the CO2/CO ratio from 0.78 to 2.27. Increasing BTIT enhances efficiency and PD, but promotes dissociation. Compared to Brayton cycle, the integrated system shows a substantial reduction in SCER from 2.04 to 0.65 kg/kWh at a rp of 3 and BTIT of 1100°C. Finally, two multi-objective optimization problems were formulated and solved using genetic algorithm. The Pareto frontiers were evaluated using three decision-making approaches to identify the optimal solution. The study concludes that optimized combinations of pressure ratio and BTIT not only maximize energy utilization but also suppress dissociation, establishing the system as a unique pathway for efficient and eco-friendly power–cooling cogeneration.

Texture image retrieval based on subjective visual descriptions remains a significant challenge due to the “semantic gap”, where conventional Content-Based Image Retrieval (CBIR) methods rely on low-level features or reference images that often diverge from human perception. To bridge this gap, this paper proposes a reference-free, perception-driven retrieval framework that enables users to query textures directly via abstract perceptual attributes. First, we constructed a human-centric perceptual feature space through controlled psychophysical experiments, quantifying 12 explicit texture attributes (e.g., granularity, directionality) using a 9-point Likert scale. Second, addressing the variability in visual sensitivity across user demographics, we developed a user-adaptive mechanism incorporating dual perceptual libraries tailored for art-major and non-art-major groups. Retrieval is formulated as a perception-aligned similarity optimization problem within this normalized space. Experimental evaluations on the Describable Textures Dataset (DTD) demonstrate that our method achieves superior perceptual consistency compared to both handcrafted descriptors (GLCM, LBP, HOG) and deep learning baselines (VGG16, ResNet50). Notably, the framework attained high PAP@3 performance across both user groups, validating its effectiveness in decoding fuzzy human intent without the need for query images. This work provides a robust solution for semantic-based texture retrieval in human–computer interaction scenarios.

The multi-objective multi-item transportation problem is a challenging issue in the context of supply chain management which deals with optimizing several conflicting objectives, considering the allocation of different products departing from many source nodes to multiple demand destinations. In this paper we propose a systematic mathematical approach based on linear programming to solve this challenging optimization problem. Based on these assumptions the study designs a multi-objective linear programming (MOLP) model with cost, delivery time and environment as the main objectives. The model is developed under clear-cut restrictions that consider supply avai lability, demand requirements, vehicle capacity and multi-product allocation rules. A practical example is considered with real operational data of a regional distribution network for optimal transportation planning and WinQSB software is used to find the best routes. Results show that the proposed model can effectively compromise conflicting multi-objectives, reducing total cost by 18.5%, delivery time by 12.3% and CO2 emissions by 15.2%. The research uses the weighted sum-constraint method for Pareto optimization based decisiontrade-offs, and results into a full tradeoffs analysis and possible transportation planning solutions to decision-making people.

Currently, the integration of satellite navigation systems (SNS) and correlation-extremal navigation systems (CENS) for unmanned vehicles (UVs) is implemented based on principles of separate or, at best, weakly coupled integration, where their measurements are processed by different navigation algorithms (stochastic filters) with subsequent correction of inevitable discrepancies using various optimization methods. This approach is characterized by both high computational costs due to the need for parallel implementation of SNS and CENS measurement processing algorithms and subsequent optimization problem solving, as well as critical dependence of positioning accuracy on increasing levels of radio measurement interference. In this regard, a solution is proposed to improve the positioning accuracy of UVs based on the principle of tightly coupled integration, which involves representing the UV’s coordinate vector and the terrain elevation of the underlying surface as a single navigation vector, estimated by a common stochastic filter. Such measurement processing, in addition to significantly reducing computational costs, ensures robust and high-precision estimation of UV navigation parameters under conditions of intense interference of both natural and artificial origin. The results of a numerical experiment illustrating the effectiveness of the proposed approach are presented.

The classical linear programming transportation problem of minimizing the cost of transportation between production and consumption points has many applications, one of which is the problem of efficient fire control. The article considers a modified formulation of this problem. It includes main and auxiliary batteries, each of which can fire a limited number of shots at targets with a given efficiency. Auxiliary batteries can fire only at predetermined targets. The goal is to distribute targets between batteries in such a way that the total efficiency of destruction is maximized. The decomposition method is used to solve the proposed problem. The original problem of large dimension is divided into many simpler one-dimensional and two-dimensional subproblems. At the first stage, the initial pseudo-solution is found as a set of solutions to these subproblems. If it is admissible for the original problem, then it is also optimal. Otherwise, an iterative process of sequentially coordinating the solutions of the subproblems is launched by cyclically recalculating the coefficients of the objective function in two dimensional problems. This process guarantees a monotonic approximation to the optimal solution. The article examines in detail possible cases arising during the algorithm operation, including a special degenerate case, for the resolution of which it is proposed to introduce additional constraints. The possibility of replacing inequality constraints with equality constraints for the main batteries within the framework of the decomposition approach without loss of generality is theoretically substantiated. The efficiency of the proposed algorithm is confirmed by the results of computational experiments. Approximation of the dependence of the running time on the problem dimension demonstrates the polynomial complexity of the method. The obtained results open up prospects for applying this approach to other non classical formulations of transport-type optimization problems.

The actual implementation of fifth-generation (5G) and beyond networks faces persistent challenges, including environmental interference and limited coverage, which compromise transmission stability and network feasibility. Reconfigurable Intelligent Surfaces (RISs) have emerged as a promising technology to dynamically reconfigure wireless propagation environments and enhance communication quality. To fully unlock the potential of RIS, this paper proposes a novel deployment strategy based on Double Deep Q-Networks (DDQNs) that jointly optimizes the RIS placement and phase shift configuration to maximize the system sum-rate. Specifically, the coverage area is discretized into a grid, and at each candidate location, a DDQN-based method is developed to solve the corresponding non-convex phase optimization problem. Simulation results reveal that our proposed strategy significantly surpasses conventional benchmark schemes, resulting in a sum-rate improvement of up to 38.41%. The study provides a practical and efficient pre-deployment framework for RIS-enhanced wireless networks.

Pursuit–evasion involves coupled, antagonistic decision-making and is prone to local-optimal behaviors when solved online under nonlinear dynamics and constraints. This study investigates a dual-AUV pursuit–evasion problem in ocean-current environments by integrating game theory with model predictive control (MPC). We formulated a game-theoretic MPC scheme that optimizes pursuit and evasion actions over a finite receding horizon, producing Nash-like responses. To solve the resulting nonconvex and multi-modal optimization problems reliably, we developed an Enhanced Adaptive Quantum Particle Swarm Optimization (EA-QPSO) method that incorporates chaos-based initialization and adaptive diversity-aware exploration with stagnation-escape perturbations. EA-QPSO is benchmarked against representative solvers, including fmincon, Differential Evolution (DE), and the Marine Predator Algorithm (MPA). Extensive 2D and 3D simulations demonstrate that EA-QPSO mitigates local-optimum trapping and yields more effective closed-loop behaviors, achieving longer escaping trajectories and more persistent pursuit until capture under the game formulation. In 3D scenarios, EA-QPSO better preserves high-speed motion while coordinating agile angular-rate adjustments, outperforming competing methods that exhibit premature deceleration or degraded maneuvering. These results validate the proposed framework for computing reliable competitive strategies in constrained underwater pursuit–evasion games.

Significant developments made in quantum hardware and error correction recently have been driving quantum computing toward practical utility. However, gaps remain between abstract quantum algorithmic development and practical applications in computational sciences. In this perspective article, we propose several properties that scalable quantum computational science methods should possess. We further discuss how block-encodings and polynomial transformations can potentially serve as a unified framework with the desired properties. Recent advancements on these topics are presented, including the construction and assembly of block-encodings, and various generalizations of quantum signal processing (QSP) algorithms to perform polynomial transformations. The scalability of QSP methods on parallel and distributed quantum architectures is also highlighted. Promising applications in simulation and observable estimation in chemistry, physics, and optimization problems are presented. We hope this perspective serves as a gentle introduction to state-of-the-art quantum algorithms for the computational science community and inspires future development of scalable quantum computational science methodologies that bridge theory and practice.

Recently gradient-free optimization methods have become a major tool in reinforcement learning and memory-efficient LLM fine-tuning. Under the standard setting of uniformly bounded noise variance an optimal accelerated algorithm has been derived. However, the assumption of bounded variance is strict and usually is not fulfilled in practice. Therefore, we will relax it, allowing the noise distribution to be heavy-tailed and, thus, broadening the class of problems to be solved. We propose gradient-free algorithms with zeroth-order oracle under adversarial noise with unbounded variance, for non-smooth convex and convex-concave optimization problems. We apply clipping operator to deal with heavy-tailedness and batching to allow efficient computation via parallelization. Our analysis provides asymptotic bounds for such key parameters as iteration complexity, oracle complexity and maximal adversarial noise level.

Abstract This paper addresses a graph optimization problem, called the Witness Tree problem, which seeks a spanning tree of a graph minimizing a certain non-linear objective function. This problem is of interest because it plays a crucial role in the analysis of the best approximation algorithms for two fundamental network design problems: Steiner Tree and Node-Tree Augmentation. We will show how a wiser choice of witness trees leads to an improved approximation for Node-Tree Augmentation, and for Steiner Tree in special classes of graphs.

ABSTRACT This paper proposes a hybrid‐clamped three‐level series‐resonant dual‐active‐bridge (HC3L‐SRDAB) converter with an asymmetric pulse‐width modulation (APWM) strategy to improve efficiency. The HC3L‐SRDAB topology reduces the voltage stress of switches –, while the APWM strategy involves only two control variables, ensuring simple implementation. Furthermore, the APWM strategy extends the zero‐voltage switching (ZVS) range, achieving full‐range ZVS at and and maintaining ZVS above 16.7% of the normalized power at . To reduce conduction losses, the root mean square (RMS) current is minimized by solving an optimization problem with the Lagrange multiplier method. Converter operation and steady‐state behavior are analyzed using the fundamental harmonic approximation (FHA). A 400‐W prototype validates the proposed approach, confirming wide‐range ZVS, reduced current stress, and higher efficiency compared with the conventional EPS strategy.

Abstract Gray‐box optimization, where parts of optimization problems are represented by algebraic models while others are treated as black‐box models lacking analytic derivatives, remains a challenge. Trust‐region (TR) methods provide a robust framework for gray‐box problems through local reduced models (RMs) for black‐box components, but they are complex and require extensive parameter tuning. Motivated by recent advances in funnel‐based convergence theory for nonlinear optimization, we propose a novel TR funnel algorithm for gray‐box optimization, replacing the filter acceptance criterion with a uni‐dimensional funnel, maintaining a monotonically decreasing upper bound on approximation error of local black‐box RMs. A global convergence proof to a first‐order critical point is established. The algorithm, implemented open‐source in Pyomo, supports multiple RM forms and globalization strategies (filter or funnel). Benchmark tests show the TR funnel algorithm achieves comparable and often improved performance relative to the classical TR filter method, thus providing a simpler, effective alternative for gray‐box optimization.

In this paper we discuss the extension of an RLT-based algorithm for continuous polynomial optimization problems to handle mixed-integer variables. The chosen approach is a direct one, in which the LP relaxations to be solved at the nodes of the branch-and-bound tree are replaced with MILP relaxations and, therefore, the additional burden caused by the discrete variables is taken care of by the auxiliary MILP solver. One of the main advantages of this approach is that the resulting algorithm inherits all the strengths of the auxiliary MILP solver. We conduct a computational analysis in which we focus on the impact of a number of choices that must be made for the resulting algorithm to be effective at providing good lower and upper bounds upon termination. All the analyses are carried out within RAPOSa, a state-of-the-art global solver for polynomial optimization.

Swarm trajectory optimization problems are a well-recognized class of multi-agent optimal control problems with strong nonlinearity. However, the heuristic nature of needing to set the final time for agents beforehand and the time-consuming limitation of the significant number of iterations prohibit the application of existing methods to large-scale swarms of unmanned aerial vehicles (UAVs) in practice. In this paper, we propose a spatial-temporal trajectory optimization framework that accomplishes multi-UAV consensus based on the alternating direction multiplier method (ADMM) and uses differential dynamic programming (DDP) for fast local planning of individual UAVs. The introduced framework is a two-level architecture that employs parameterized DDP as the trajectory optimizer for each UAV and ADMM to satisfy the local constraints and accomplish the spatial-temporal parameter consensus among all UAVs. This results in a fully distributed algorithm called distributed parameterized DDP. In addition, an adaptive tuning criterion based on the spectral gradient method for the penalty parameter is proposed to reduce the number of algorithmic iterations. Several simulation examples are presented to verify the effectiveness of the proposed algorithm.

End-to-end or Deep Reinforcement Learning-based control of autonomous vehicles generally leverages a sequence of decoupled perception-action protocols. One main limitation of such frameworks is the required backpropagation algorithm to optimize the underlying mapping function or policy network. This is because although the learning goal usually involves several objectives, they must be aggregated to realize a single objective loss utilized by the backpropagation algorithm. This also limits the preference-based driving behavior from a user perspective. To overcome these challenges, we present NeuroAction-a multi-objective neuroevolutionary method designed for reinforcement learning-based autonomous driving where several goals or objectives can be optimized simultaneously. Specifically, we propose a formulation of reinforcement learning-based control of autonomous vehicles as a multiobjective optimization problem. Consequently, any multiobjective evolutionary algorithm can be used to solve the resulting problem with the aim of generating a Pareto-front of optimal policy networks. In other words, the resulting framework is capable of generating policies that are suitable for providing users with different trade-offs based on their desired driving preferences. We investigated the proposed framework on a benchmark DRL-based autonomous driving task and presented performance evolution based on three different EMO algorithms.