Simulated Annealing Research Articles (Page 1)

Abstract The formation of energy communities is pivotal for advancing decentralized and sustainable energy management. Within this context, Coalition Structure Generation (CSG) emerges as a promising framework. The complexity of CSG grows rapidly with the number of agents, making classical solvers impractical for even moderate sizes. This suggests CSG as an ideal candidate for benchmarking quantum algorithms against classical ones. Facing ongoing challenges in attaining computational quantum advantage for exact optimization, we pivot our focus to benchmarking quantum and classical solvers for approximate optimization. Approximate optimization is particularly critical for industrial use cases requiring real-time optimization, where finding high-quality solutions quickly is often more valuable than achieving exact solutions more slowly. Our findings indicate that quantum annealing (QA) on DWave can achieve solutions of comparable quality to our best classical solver, but with more favorable runtime scaling, showcasing an advantage. This advantage is observed when compared to solvers, such as Tabu search, simulated annealing, and the state-of-the-art solver Gurobi in finding approximate solutions for energy community formation involving over 100 agents. DWave also surpasses 1-round QAOA on IBM hardware. 
Our findings represent the largest benchmark of quantum approximate optimizations for a real-world dense model beyond the hardware's native topology, where D-Wave demonstrates a scaling advantage.

Renewable wind energy systems widely employ doubly fed induction generators (DFIGs), where efficient converter control ensures grid-integrated power system stability and reliability. Conventional proportional–integral (PI) controller tuning methods often encounter challenges with nonlinear dynamics and parameter variations, resulting in reduced adaptability and efficiency. To address this, we present an owl search optimization (OSO)-based tuning strategy for PI controllers in DFIG back-to-back converters. Inspired by the hunting behavior of owls, OSO provides robust global search capabilities and resilience against premature convergence. The proposed method is evaluated in MATLAB/Simulink and benchmarked against particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA) under step wind variations, turbulence, and grid disturbances. Simulation results demonstrate that OSO achieves superior performance, with 96.4% efficiency, reduced power losses (~40 kW), faster convergence (<400 ms), shorter settling time (<345 ms), and minimal oscillations (0.002). These findings establish OSO as a robust and efficient optimization approach for DFIG-based wind energy systems, delivering enhanced dynamic response and improved grid stability.

The increasing reliance on artificial intelligence (AI) for high-stakes decision-making has heightened the need for systems that prioritize not only accuracy but also interpretability and transparency. Although optimization techniques—such as metaheuristics, mathematical programming, and reinforcement learning—have significantly propelled the development of intelligent systems, their inherent black-box characteristics often hinder trust, accountability, and effective human-AI interaction. This article presents a comprehensive systematic review of the emerging intersection between explainable AI (XAI) and optimization. We explore how interpretability is being systematically incorporated into optimization-driven decision-making pipelines across a variety of application domains. The study offers a critical analysis and classification of existing research, focusing on the integration of XAI methods (e.g., SHAP, LIME, saliency maps) with optimization strategies (e.g., genetic algorithms, simulated annealing, mixed-integer linear programming, and reinforcement learning-based methods). These integrations are examined across sectors such as healthcare, finance, logistics, and energy systems. A structured taxonomy is introduced to categorize hybrid approaches according to their level of explainability, optimization complexity, and domain specificity. In addition, the review highlights key challenges in the field, including the trade-off between performance and interpretability, the absence of standardized benchmarks, and issues related to model scalability. Finally, we outline promising research directions such as the development of explainable hyper-heuristics, domain-adaptable interpretable solvers, and AI frameworks aligned with regulatory standards. By synthesizing this evolving body of knowledge, the article aims to serve as a foundational resource for researchers and practitioners striving to build transparent, trustworthy, and effective optimization-based AI systems

Abstract The results of deformation analysis are crucial for the safety of engineering infrastructure and people’s lives. A key element of this analysis is the identification of stable points of the monitoring network, which will constitute the reference for further calculated displacements. This paper proposes a new method of stability analysis aimed at identifying stable groups of reference points in a displacement monitoring network. It is based on coordinate transformation and involves searching for the optimal set of transformation parameters identified with the optimal point in the transformation parameters space. A hybrid algorithm, which combines two optimization algorithms – Hooke–Jeeves and Simulated Annealing – is used to search for the solution. Two variants of the objective function were tested as the elements of the algorithm. Multiple solutions (groups of congruent reference points) can be detected in case they exist. The simulated 2D network with two congruent point groups was used as an example to illustrate the performance of the proposed algorithm. The proposed hybrid algorithm appeared to overperform the individual Hooke–Jeeves and Simulated Annealing algorithms.

This survey traces the evolution of simulated anneal- ing (SA) based algorithms for VLSI floorplanning and placement. It begins with the foundational TimberWolf package, which established SA as a state-of-the-art method. It then examines two distinct paths of improvement that address the limitations of the original approach. The first path focuses on achieving scalability and routability for modern, large-scale designs by integrating a multilevel framework and direct congestion modeling. The second path re-engineers the core optimization engine itself, introducing a novel three-stage annealing schedule for faster convergence while handling complex geometric constraints.

Breast cancer poses a significant threat to women's health. Dynamic Optical Breast Imaging (DOBI) is a medical imaging method based on the theory of early neovascularization in breast tumors. This technique is fast, non-invasive, and radiation-free, with the potential for early diagnosis of breast cancer, thereby helping to enhance patients’ survival rate and treatment outcomes. However, due to limitations such as limited data volume and class imbalance, existing medical image classification methods often suffer from low classification accuracy, poor generalization ability, and low sensitivity to malignant samples when applied to DOBI. To address these issues, this paper proposes the Bayesian Dynamic Ensemble Selection (BDES) method. In the BDES method, the K-Nearest Neighbor Dynamic Classifier Selection (KNND-CS) method is designed to construct specific classifiers pool based on all available base classifiers for each test sample. Subsequently, the simulated annealing algorithm is utilized to dynamically select classifiers from this pool for inclusion in the ensemble. Finally, the selected classifiers are ensembled by Bayesian probability fusion function to generate the final diagnosis result of benign or malignant breast tumors. The BDES method dynamically selects and integrates appropriate classifiers for each sample, enhancing DOBI's accuracy in diagnosing benign and malignant breast tumors while ensuring robustness and generalization. To validate the effectiveness of BDES, extensive experiments were conducted. Cross-validation experiment proved the generalization and robustness of the DBES method. And the comparation experiment in breast cancer diagnosing for the DOBI dataset shows that the accuracy and sensitivity of the BDES method are 83% and 78% respectively, which is significantly better than many comparative methods, proving the effectiveness of the new method in early diagnosis of breast cancer.

To overcome the limitations of traditional multi-objective evolutionary algorithms—which often become trapped in local optima when addressing complex optimization problems and face challenges in balancing convergence efficiency with population diversity—this study proposes an enhanced NSGA-II algorithm that incorporates Lévy flight and simulated annealing strategies. The proposed algorithm enhances global exploration via Lévy flight mutation, improves local search precision through simulated annealing, and dynamically coordinates the search process using adaptive parameter strategies. Experiments conducted on the ZDT and DTLZ test function series demonstrated that the proposed algorithm achieves performance comparable to or better than that of NSGA-II and other benchmark algorithms, as measured by inverted generational distance and hypervolume metrics. It also exhibited superior convergence, distribution uniformity, and robustness. Furthermore, the algorithm was applied to the multi-objective optimization of electric winch trajectories for oil drilling rigs, which employed trajectory planning based on quintic polynomials. The simulation results demonstrated, compared to the pre-optimization baseline data, reductions of 6% in total operation time, 17.99% in energy consumption, and 27.4% in impact severity, thereby validating the method’s effectiveness and applicability in practical engineering scenarios. The comprehensive results demonstrate that the improved algorithm exhibits robust performance and excellent adaptability when addressing complex multi-objective optimization problems.

This paper presents a real-time handover and link assignment framework for low-Earth-orbit (LEO) satellite networks operating in dense urban canyons. The proposed Markov chain-guided simulated annealing (MCSA) algorithm optimizes user-to-satellite assignments under dynamic channel and capacity constraints. By incorporating Markov chains to guide state transitions, MCSA achieves faster convergence and more effective exploration than conventional simulated annealing. Simulations conducted in Ku-band urban canyon environments show that the framework achieves an average user satisfaction of about 97%, providing an approximately 10% improvement over genetic algorithm (GA) results. It also delivers 10–15% higher resource utilization, lower blocking rates comparable to integer linear programming (ILP), and superior runtime scalability with linear complexity O(k·|U|·|S|). These results confirm that MCSA provides a scalable and robust real-time mobility management solution for next-generation LEO satellite systems.

Precise control of critical quality attributes, including titer and glycosylation, is essential in bioprocessing, yet conventional design‑of‑experiments methods are challenged by the high-dimensional, nonlinear design space for media and process parameters. We assemble a comprehensive glycan‑focused Chinese hamster ovary (CHO) fed‑batch dataset and develop a computational workflow (i) to train machine learning (ML) models to predict key CQAs, (ii) apply a hybrid ML+knowledge-based strategy to select potentially impactful features, and (iii) generate combinatorial media designs. The resulting models predict final titer (R2≈0.93) and major glycan metrics-mannosylation, fucosylation, galactosylation (R2≈0.79-0.95)-directly from initial media composition and process parameters without requiring spent media analysis. Feature selection shortlisted 20 features out of 76 for a second-tier validation, from which 15 were confirmed as actionable levers impacting titer and glycosylation, uncovering glycan effects independent of nucleotide sugar supplementation. Finally, we incorporated our workflow, utilizing a ML surrogate model coupled with simulated annealing, in a proof‑of‑concept active learning step, successfully proposing a media composition and process parameter combination that reduced mannosylation by 10% while increasing titer. Together, these results underscore how ML‑enhanced DOE can accelerate CHO process development and explore complex biomanufacturing spaces with greater efficiency.

Full-coverage path planning for multi-machinery anti-subsidence in partially sinkage-risky fields based on an improved seagull optimization algorithm with simulated annealing

ABSTRACT This study proposes an Ant Colony Optimization (ACO) approach for detecting covariance in 6G-enabled Internet of Things (IoT) networks, which is crucial for optimizing performance and resource allocation. Traditional methods struggle with the dynamic, high-speed demands of 6G. Inspired by ant behavior, ACO uses dynamic agents to explore covariance structures, with pheromone-based guidance enhancing solution quality. The method involves three steps: constructing initial solutions, refining paths through local search, and updating pheromones to achieve convergence. A smart city simulation compares Ant Colony Optimization (ACO) to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Hill Climbing (HC). Results show that ACO outperforms these models, achieving a precision of 0.6, a recall of 70.10%, a specificity of 98.01%, an accuracy of 87.50%, and an F1 score of 65.96%. These findings highlight ACO’s effectiveness in optimizing resource management and decision-making for smart cities.

Abstract In the current noisy intermediate-scale quantum (NISQ) era, the limited number of high-fidelity qubits and restricted circuit depth pose significant challenges for large-scale quantum computation. Fortunately, distributed quantum computing (DQC) provides a feasible solution by dividing large quantum circuits into smaller subcircuits that can be executed on existing quantum processors. In this work, we propose a generalized model of circuit reconstruction (GMCR), which is capable of handling complex cutting patterns such as U-type structures to recover the output of the original circuit from the subcircuit results. We also improve the existing multi-objective simulated annealing MOSA-based cutting algorithm by introducing a new objective function that considers the number of required SWAP operations in the following qubit mapping in addition to the number of nonlocal gates and execution rounds.We verified the GMCR model by cutting five circuits: encoding circuit for the Steane 7-qubit code, circuit of Shor’s algorithm, quantum supremacy circuit, quantum circuit of Bernstein–Vazirani (BV) algorithm, and circuit of approximate quantum Fourier transform (AQFT). In the case of the Steane 7-qubit code, the number of reconstruction rounds was reduced from 337 to 156 under a fixed nonlocal gate count of two, while the number of SWAP operations was also reduced from 10 to 7 compared with the earlier MOSA-based algorithm. For the U-type subcircuits, using the GMCR model, the original results can be obtained, but cannot be obtained by the dynamic definition (DD), approximate reconstruction algorithm(ARA), and fast reconstruction algorithm(FRA). This work plays an important role in implementing large-scale DQC, a typical application of future quantum Internet.

Efficient irregular texture nesting, which is necessary for improving the efficiency of texture mapping and 3D model rendering, especially for large-scale 3D reconstruction tasks, has emerged as a critical research topic in the fields of photogrammetry, computer graphics, and computer vision. However, persistent inefficiencies and high computational costs in existing texture nesting algorithms pose significant challenges when dealing with vast quantities of irregularly shaped texture patches. To solve this problem, this work presents an efficient and well structured texture nesting for reorganizing irregular textures in a space efficient and time efficient way. More specifically, a hybrid optimization approach that integrates an enhanced no fit polygon (NFP) method with an improved simplified atavistic differential evolution (SADE) algorithm is proposed. The canonical SADE is reformulated, tailored for texture nesting optimization, and a novel self-adaptive container resizing strategy is used to surpass traditional NFP approaches in polygon processing efficiency. The experimental results demonstrate that the proposed method significantly improves irregular texture nesting efficiency, achieving speed improvements of up to 5.44 times compared with the common genetic algorithm–based method and 5.21 times over the simulated annealing–based method. Furthermore, it consistently improves space use by approximately 6.56%, indicating a more effective layout strategy and optimized resource use. Code is available at https:// github. com/louliyuan/NFP-SADE-With-Adaptive-Container-Resizing.

Automatic optimization of BIM masonry layout combining simulated annealing and tabu search

Machine learning for predicting and optimizing the performance of a commercial-scale anaerobic digester with diverse feedstocks and operating conditions.

An oscillation based simulated annealing algorithm for the single row facility layout problem

Scenario-based simulation of ecosystem service supply and demand in China's Yangtze River Economic Belt.

Purpose This paper aims to address the technician routing and scheduling problem (TRSP), a daily operational challenge faced by telecommunication service providers. The study is motivated by a real-world application in Saskatchewan, Canada, and aims to develop an effective and scalable model for technician assignment and routing under practical constraints. The design of the problem is unique because of the vast working areas in Saskatchewan. Design/methodology/approach A mixed-integer programming model is formulated to model the TRSP, capturing realistic constraints such as soft time windows, variable working hours, lunch breaks (LBs) and overnight shifts. Because of the NP-hard nature of the problem, the authors propose two metaheuristic algorithms – simulated annealing (SA) and genetic algorithm (GA) – to solve large-scale instances. Computational experiments are conducted using real data, and the metaheuristics’ performance is benchmarked against a commercial exact solver. Findings Results indicate that both SA and GA produce high-quality solutions within significantly reduced computation times compared to the exact solver. The GA consistently outperforms SA in terms of optimality gaps and solution robustness. The findings highlight the practical viability of using metaheuristics in large-scale technician scheduling problems. Practical implications The proposed approach offers telecom service providers a flexible and scalable solution for managing technician assignments efficiently while accommodating operational constraints. The metaheuristic algorithms can be integrated into decision-support systems to improve customer service and reduce scheduling inefficiencies. Originality/value This research makes two main contributions. From a modeling perspective, it incorporates various available technician working hours as well as LBs into the overnight TRSPTW model. From a solution methodology perspective, it develops two metaheuristic algorithms – an SA and a GA – to solve the overnight TRSPTW with the LB model. The effectiveness of these two metaheuristics is analyzed via computational experiments using real-world scenarios.

Effective key quality characteristic (KQC) selection is essential for follow-up quality improvement. Customers’ demands for different product QCs affect product popularity. However, little research has integrated customer attention into KQC selection under imbalanced data in e-commerce, which can lead to a follow-up product with good quality but no popularity. This study, therefore, investigated KQC selection incorporating customer attention in the scenario of imbalanced data for popular products. First, KQC selection incorporating customer attention was defined as a multi-objective problem, aiming to minimise the percentage of selected QCs and maximise the importance of QCs, as well as cumulative attention to selected QCs. A collaborative filtering algorithm-based method was applied to extract customer attention from historical data when filtering key QCs. Second, an adaptive hybrid whale optimisation algorithm (AHWOA) was proposed to solve KQC selection. Here, simulated annealing was incorporated into the WOA agent search, and an adaptive convergence-acceleration mechanism and a fast non-dominated sorting algorithm with an improved crowding-distance measure were integrated into WOA. Third, the proposed AHWOA was evaluated on five datasets from the UCI repository, and the results show AHWOA’s advantages over five existing benchmark algorithms.

We present Learning-Driven Annealing (LDA), a framework that links individual quantum annealing evolutions into a global solution strategy to mitigate hardware constraints such as short annealing times and integrated control errors. Unlike other iterative methods, LDA does not tune the annealing procedure (e.g. annealing time or annealing schedule), but instead learns about the problem structure to adaptively modify the problem Hamiltonian. By deforming the instantaneous energy spectrum, LDA suppresses transitions into high-energy states and focuses the evolution into low-energy regions of the Hilbert space. We demonstrate the efficacy of LDA by developing a hybrid quantum-classical solver for large-scale spin glasses. The hybrid solver is based on a comprehensive study of the internal structure of spin glasses, outperforming other quantum and classical algorithms (e.g., reverse annealing, cyclic annealing, simulated annealing, Gurobi, Toshiba's SBM, VeloxQ and D-Wave hybrid) on 5580-qubit problem instances in both runtime and lowest energy. LDA is a step towards practical quantum computation that enables today's quantum devices to compete with classical solvers.