High-quality Solutions Research Articles

Graph coarsening for fugitive interception

The police control room determines where to send available police units to intercept a fleeing fugitive. Models can support the police with decision-making for fugitive interception. The police have, at most, a few minutes to determine an interception strategy. Therefore, a timely calculation of the interception positions is essential to support police interception operations. The number of nodes in the network, each being a crossing where routes of the fleeing suspect can split, greatly contributes to the computation time. Graph coarsening is a promising approach to reduce the complexity of the network, and therefore the computation time. We compare four graph coarsening algorithms on five road networks and assess their impact on computation time and solution quality for the fugitive interception problem. Based on the comparison, we propose and test a new method specifically for fugitive interception. This method, Search Space Representation, improves the quality of the best solutions obtained by the optimization algorithm with up to 12%, improves the reliability of the optimization to find high-quality solutions, and decreases the number of function evaluations required to obtain high-quality solutions to 5000–10,000 depending on the size and complexity of the road network, which is feasible for real-time decision-making. Search Space Representation can be applied to reduce the computation time of other network-based optimization problems.

An Evolutionary Learning Whale Optimization Algorithm for Disassembly and Assembly Hybrid Line Balancing Problems

In order to protect the environment, an increasing number of people are paying attention to the recycling and remanufacturing of EOL (End-of-Life) products. Furthermore, many companies aim to establish their own closed-loop supply chains, encouraging the integration of disassembly and assembly lines into a unified closed-loop production system. In this work, a hybrid production line that combines disassembly and assembly processes, incorporating human–machine collaboration, is designed based on the traditional disassembly line. A mathematical model is proposed to address the human–machine collaboration disassembly and assembly hybrid line balancing problem in this layout. To solve the model, an evolutionary learning-based whale optimization algorithm is developed. The experimental results show that the proposed algorithm is significantly faster than CPLEX, particularly for large-scale disassembly instances. Moreover, it outperforms CPLEX and other swarm intelligence algorithms in solving large-scale optimization problems while maintaining high solution quality.

Enhancing Quantum Algorithms for Quadratic Unconstrained Binary Optimization via Integer Programming

To date, research in quantum computation promises potential for outperforming classical heuristics in combinatorial optimization. However, when aiming at provable optimality, one has to rely on classical exact methods like integer programming. State-of-the-art integer programming algorithms can compute strong relaxation bounds even for hard instances, but may have to enumerate a large number of subproblems for determining an optimum solution. If the potential of quantum computing realizes, it can be expected that in particular finding high-quality solutions for hard problems can be done fast. Still, near-future quantum hardware considerably limits the size of treatable problems. In this work, we go one step into integrating the potentials of quantum and classical techniques for combinatorial optimization. We propose a hybrid heuristic for the weighted maximum-cut problem and for quadratic unconstrained binary optimization. The heuristic employs a linear programming relaxation, rendering it well-suited for integration into exact branch-and-cut algorithms. For large instances, we reduce the problem size according to a linear relaxation such that the reduced problem can be handled by quantum machines of limited size. Moreover, we improve the applicability of depth-1 QAOA, a parameterized quantum algorithm, by deriving a parameter estimate for arbitrary instances. We present numerous computational results from real quantum hardware.

Cooperative Multi-Agent Planning Framework for Fuel Constrained UAV-UGV Routing Problem

Unmanned Aerial Vehicles (UAVs), adept at aerial surveillance, are often constrained by their limited battery capacity. Refueling on slow-moving Unmanned Ground Vehicles (UGVs) can significantly enhance UAVs’ operational endurance. This paper explores the computationally complex problem of cooperative UAV-UGV routing for vast area surveillance, considering speed and fuel constraints. It presents a sequential multi-agent planning framework aimed at achieving feasible and optimally satisfactory solutions. By considering the UAV fuel limit and utilizing a minimum set cover algorithm, we determine UGV refueling stops. This, in turn, facilitates UGV route planning as the first step. Through a task allocation technique and energy-constrained vehicle routing problem modeling with time windows (E-VRPTW), we then achieve the UAV route in the second step of the framework. The effectiveness of our multi-agent strategy is demonstrated through the implementation on 30 different task scenarios across three different scales. This work provides significant insight into the collaborative advantages of UAV-UGV systems and introduces heuristic approaches to bypass computational challenges and swiftly reach high-quality solutions.

MOANA: Multi-Objective Ant Nesting Algorithm for Optimization Problems

This paper presents the Multi-Objective Ant Nesting Algorithm (MOANA), a novel extension of the Ant Nesting Algorithm (ANA), specifically designed to address multi-objective optimization problems (MOPs). MOANA incorporates adaptive mechanisms, such as deposition weight parameters, to balance exploration and exploitation, while a polynomial mutation strategy ensures diverse and high-quality solutions. The algorithm is evaluated on standard benchmark datasets, including ZDT functions and the IEEE Congress on Evolutionary Computation (CEC) 2019 multi-modal benchmarks. Comparative analysis against state-of-the-art algorithms like MOPSO, MOFDO, MODA, and NSGA-III demonstrates MOANA's superior performance in terms of convergence speed and Pareto front coverage. Furthermore, MOANA's applicability to real-world engineering optimization, such as welded beam design, showcases its ability to generate a broad range of optimal solutions, making it a practical tool for decision-makers. MOANA addresses key limitations of traditional evolutionary algorithms by improving scalability and diversity in multi-objective scenarios, positioning it as a robust solution for complex optimization tasks.

Enhanced NSGA-II algorithm based on novel hybrid crossover operator to optimise water supply and ecology of Fenhe reservoir operation

Reservoir-operation optimisation is a crucial aspect of water-resource development and sustainable water process management. This study addresses bi-objective optimisation problems by proposing a novel crossover evolution operator, known as the hybrid simulated binary and improved arithmetic crossover (SBAX) operator, based on the simulated binary cross (SBX) and arithmetic crossover operators, and applies it to the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) algorithm to improve the algorithm. In particular, the arithmetic crossover operator can obtain an optimal solution more precisely within the solution space, whereas the SBX operator can explore a broader range of potential high-quality solutions. Considering the advantages of both operators, this study introduces an improved arithmetic operator to reduce the risk of local convergence inherent in conventional arithmetic operators. Subsequently, two strategies for the SBAX operator are discussed: SBX operator + new arithmetic operator and new arithmetic operator + SBX operator. The convergence of the bi-objective Pareto solution set is evaluated based on the generation and inverted generational distances. This method is used for the collaborative optimisation of the water supply and ecological operation of the Fenhe Reservoir, where its effectiveness is demonstrated. A comparative analysis of the bi-objective optimisation schemes obtained using different crossover operators indicates the following: (1) the NSGA-II algorithm based on the SBAX operator achieves a convergence efficiency that is 14.25–41.95% higher than that of the conventional NSGA-II algorithm; (2) the reservoir operation indices of the scheduling scheme derived from the NSGA-II algorithm based on the SBAX operator significantly outperform those obtained using the conventional NSGA-II algorithm. The optimal strategy reduces the annual average water abandonment by 11.2–14.52 million m3. This study provides a novel approach for bi-objective optimisation and sustainable reservoir management.

Hyperplane-Assisted Multi-objective Particle Swarm Optimization with Twofold Proportional Assignment Strategy

In the simultaneous optimization of multiple objectives, how to balance convergence promotion and diversity preservation in the evolutionary process is a key and challenging problem. In this research, a hyperplane-assisted multi-objective particle swarm optimization with a twofold proportional assignment strategy (tpahaMOPSO) is suggested to ameliorate the optimization performance of MOPSO. First, the external archive is maintained in combination with hyperplane-based convergence evaluation and shift-based density estimation to retain high-quality candidate solutions. Second, a twofold proportional assignment scheme is designed to search the surrounding region of candidate solutions with better potential to emphasize convergence and diversity, respectively. Third, the domination relationship and convergence difference are combined to select a more reasonable individual historical best and reduce the risk of particle aggregation. Finally, the proposed tpahaMOPSO was compared with ten representative and advanced multi-objective optimization algorithms on 22 widely used test functions with different characteristics. The simulation results present that the developed tpahaMOPSO got the best result in 11 benchmark functions for both IGD and HV criteria. Concurrently, the Friedman test was applied for ranking analysis and the proposed algorithm also obtained excellent statistical analysis results. The promising performance and strong competitiveness of the proposed tpahaMOPSO have been verified by different experimental studies.

Performance Comparison of Ant Colony Optimization and Artificial Bee Colony in Solving the Capacitated Vehicle Routing Problem

The Capacitated Vehicle Routing Problem (CVRP) is a combinatorial optimization problem widely applied in logistics and supply chain management. It involves determining the optimal routes for a fleet of vehicles with limited capacity to serve a set of customers with specific demands while minimizing travel costs. This study compares the performance of two popular metaheuristic algorithms, Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC), in solving the CVRP. The research implements both algorithms on standard benchmark datasets, evaluating solution accuracy and computational efficiency. Simulation results indicate that ACO tends to excel in finding high-quality solutions, particularly for problems with high complexity, whereas ABC demonstrates superior computational efficiency on small- to medium-scale datasets. A detailed analysis of algorithm parameters was also conducted to understand their impact on the performance of both methods. This study provides valuable insights into the strengths and limitations of each algorithm in the context of CVRP and paves the way for the development of hybrid approaches in the future.

Computational Framework for Target Tracking Information Fusion Problems

In this work, we propose computationally tractable techniques for extracting valuable information from diverse data sources collected by multiple sensors in a variety of formats (visual, sonar, quantitative, qualitative, social information, etc.). More specifically, we develop an integrated approach consisting of two algorithms for extracting information and achieving a consensus-based, robust solution. The first algorithm extracts solutions from sensors within each data source, whereas the second algorithm reaches a compromise among the generated solutions from the previous algorithm across all data sources. To accomplish these goals, we initially transform the multisensor multitarget tracking problem (MSMTT) problem into a multidimensional assignment problem. Subsequently, we introduce a decomposition-based multisensor recursive approach referred to as a revised multisensor recursive algorithm, which can efficiently deliver a robust solution for each single data source MSMTT problem. In the second algorithm, we extend our methodology to the multisource MSMTT problem by introducing a connection-based symmetric nonnegative matrix factorization technique, which is shown to be computationally feasible and efficient in obtaining high-quality solutions. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the Army Research Laboratory [Grant G00006831]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0016 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0016 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Unlocking Modern VLSI Placement with Cutting-Edge GPU Acceleration Integrated with Deep Learning Tool Kit

Objectives: In VLSI, cell placement is critical in determining the overall performance, area, runtime efficiency, and power consumption of integrated circuits. The objective of this work is to find the best possible locations for the cells to meet the mentioned constraints. Methods: The proposed method utilizes a deep reinforcement learning strategy with heuristics to address the complexities of modern VLSI design challenges. A multi-objective deep reinforcement learning approach fused with GPU acceleration is explored to optimize placement metrics like wirelength, congestion, and runtime to gain a globally optimal placement solution. The suggested method dynamically selects appropriate parameters, a process known as Parameter Tuning, to produce high-quality placement solutions. The strategy's potency is demonstrated using open-source benchmarks sourced from storage, with BlackParrot and MemPool. Findings: The trial outcomes of the strategy on benchmark data show considerable improvements in placement quality. It reduces wire length by up to 4% and congestion by about 10%. Moreover, it is highly scalable and reliable in providing global placement solutions. The reduced aspect ratio indicates lower chip area utilization and reduced power consumption. Novelty: The integration of GPU acceleration and deep learning methodology as a strategy for VLSI global placement has not been reported in prior placement work; however, similar approaches are available for legalization and detailed placement. Keywords: Placement, VLSI, Deep Learning, GPU Acceleration, Parameter Tuning

Metro train rescheduling approach during the transitional period

ABSTRACT Metro systems will inevitably be affected by train delays. Train delays during the peak to off-peak transitional period potentially lead to passengers being stranded. To address this problem, this study develops a train rescheduling model that comprehensively considers train delays, service cancellations, and the passenger service quality. The rescheduling strategy of adding temporary train services is particularly considered based on operational characteristics during the transitional period. Next, the model is transformed into a mixed-integer linear programming (MILP) model using linearization methods. To improve computational efficiency, a two-stage approach is proposed. Finally, numerical examples based on practical data from the Beijing Metro Yizhuang line are conducted to verify the effectiveness and efficiency of the proposed approach. The computational results show that the model proposed in this study can reduce the number of stranded passengers during the transitional period, and the two-stage approach can obtain high-quality feasible solutions in a short time.

Silver Chemical Mechanical Polishing with the Synergy of Hydrogen Peroxide and 5-methyl-1H-benzotriazole

Silver is a promising interconnect material. Silver chemical mechanical polishing (CMP) requires further research. This study investigated the effects of hydrogen peroxide (H2O2) and 5-methyl-1H-benzotriazole (5-MBTA) on silver CMP. The results indicate that H2O2 and 5-MBTA exhibit synergy in the silver CMP, greatly improving surface quality and material removal rate (MRR). After adding 10 mmol l−1 5-MBTA to 1 wt% H2O2, the surface roughness S a drops from 27 nm to as low as 1.16 nm, while the MRR increases from 26 nm min−1 to 220 nm min−1. The silver substrate shows no processing damage caused by CMP. For the mechanism, H2O2 oxidizes silver to form soluble Ag[(OH)2]− and Ag2O. Consequently, this enhances corrosion and causes corrosion holes on the polished silver surface. Upon adding 5-MBTA to H2O2, 5-MBTA reacts with silver ions to form Ag-5-MBTA based on the X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy results. For one thing, Ag-5-MBTA inhibits corrosion according to the electrochemical experimental results, preventing corrosion holes. Hence, the surface quality is improved. In addition, the silver surface’s hardness becomes lower according to the atomic force microscopy results, presumably due to incoordination between Ag2O and Ag-5-MBTA. Therefore, the MRR increases. This study presents a high-quality CMP solution for silver interconnects.

Fuzzy Self-tuning Bees Algorithm for designing optimal product lines

The Product Line Design (PLD) problem is an NP-hard combinatorial optimization problem in marketing that aims at determining an optimal product line through which a firm can optimize a desired objective, like its profits or market share. Since the PLD problem has been proved to have high complexity in real-life applications, high-quality solutions have been detected by researchers who develop various optimization methods and test their performance. The Bees Algorithm (BA) is a successful swarm intelligent optimization algorithm which is based on the behavior of bees. The aim of this research is to develop and assess BA in the optimal PLD problem. In this effort, a set of fuzzy rules has been developed to autonomously compute parameters for each individual solution throughout the optimization process. The performance of two BA variants is compared with those of popular previous approaches, using both real and simulated data of customer preferences. The findings reveal that BA constitutes an enhanced alternative approach for designing optimal product lines.

Efficient Neural Collaborative Search for Pickup and Delivery Problems.

In this paper, we introduce Neural Collaborative Search (NCS), a novel learning-based framework for efficiently solving pickup and delivery problems (PDPs). NCS pioneers the collaboration between the latest prevalent neural construction and neural improvement models, establishing a collaborative framework where an improvement model iteratively refines solutions initiated by a construction model. Our NCS collaboratively trains the two models via reinforcement learning with an effective shared-critic mechanism. In addition, the construction model enhances the improvement model with high-quality initial solutions via curriculum learning, while the improvement model accelerates the convergence of the construction model through imitation learning. Besides the new framework design, we also propose the efficient Neural Neighborhood Search (N2S), an efficient improvement model employed within the NCS framework. N2S exploits a tailored Markov decision process formulation and two customized decoders for removing and then reinserting a pair of pickup-delivery nodes, thereby learning a ruin-repair search process for addressing the precedence constraints in PDPs efficiently. To balance the computation cost between encoders and decoders, N2S streamlines the existing encoder design through a light Synthesis Attention mechanism that allows the vanilla self-attention to synthesize various features regarding a route solution. Moreover, a diversity enhancement scheme is further leveraged to ameliorate the performance during the inference of N2S. Our NCS and N2S are both generic, and extensive experiments on two canonical PDP variants show that they can produce state-of-the-art results among existing neural methods. Remarkably, our NCS and N2S could surpass the well-known LKH3 solver especially on the more constrained PDP variant.

A Customized Augmented Lagrangian Method for Block-Structured Integer Programming.

Integer programming with block structures has received considerable attention recently and is widely used in many practical applications such as train timetabling and vehicle routing problems. It is known to be NP-hard due to the presence of integer variables. We define a novel augmented Lagrangian function by directly penalizing the inequality constraints and establish the strong duality between the primal problem and the augmented Lagrangian dual problem. Then, a customized augmented Lagrangian method is proposed to address the block-structures. In particular, the minimization of the augmented Lagrangian function is decomposed into multiple subproblems by decoupling the linking constraints and these subproblems can be efficiently solved using the block coordinate descent method. We also establish the convergence property of the proposed method. To make the algorithm more practical, we further introduce several refinement techniques to identify high-quality feasible solutions. Numerical experiments on a few interesting scenarios show that our proposed algorithm often achieves a satisfactory solution and is quite effective.

Cubic Chaos Preference Multi-Objective Optimization Algorithm with Adaptive Dual-Mode Mutation

<p>In the study of preference-based multi-objective optimization algorithms, the performance significantly depends on the preference information provided by the decision-maker. Over-reliance on this preference information can lead the algorithms to become trapped in locally optimal solutions, potentially overlooking high-quality solutions in other regions. Therefore, this paper proposes a Cubic Chaos Preference Multi-Objective Optimization Algorithm with Adaptive Dual-mode Mutation (CPMOP-DM). Firstly, this algorithm utilizes the cubic chaos strategy to initialize population. This strategy possesses better chaos traversal and optimization speed and then, helps enhance the search breadth and global convergence of the optimization algorithm. Secondly, a dynamic focused preference exploration strategy is proposed to enhance the quality and satisfaction of the selected solutions. This strategy can gradually refine the search scope via, constructing dynamically shrinking exploration circles. Compared to traditional preference-based algorithms, experimental results demonstrate the competitiveness of the proposed algorithm. It effectively balances exploration and exploitation searching of the algorithm, thereby enhancing the algorithm’s diversity and distribution. It also avoids the local optimum problem caused by over-reliance on preference information.</p> <p> </p>

An Experimental Study of Polynomial Time Algorithms for Minimum 2-Edge Connected Subgraph Problem

In this paper, we focus on the problem of finding a minimum-sized directed 2-edge-connected subgraph, a problem classified as NP-Complete [8], which plays a critical role in various practical applications. We present approximation algorithms aimed at finding efficient, high-quality solutions within polynomial time. These algorithms are based on a comprehensive analysis of the problem of finding a directed 2-edge-connected subgraph, with performance evaluated in terms of the number of edges. The results of our experiments demonstrate that the proposed algorithms effectively reduce the number of remaining edges across different graph scenarios, particularly in high-density graphs. Moreover, they maintain strong connectivity even in the event of edge failures, ensuring the continuity of network operations in the face of faults and disasters.

Automated optimisation design of barge structures using integrated knowledge-based approaches

ABSTRACT An integrated automatic optimisation simulation system for barge structure design has been developed to enhance efficiency and quality. This system incorporates a knowledge-based geometrical modelling method for barge structure design, allowing for the automatic construction of computational models of barge sections through direct transfer of geometric models. Furthermore, a barge structure optimisation simulation system integrated with direct calculation has been established. The effectiveness of this approach is demonstrated through the successful application of structural optimisation to a 65 m barge section, resulting in higher quality design solutions. This research offers an efficient and effective method for achieving high-quality design solutions in the field of barge structure design.

Two-step optimization algorithm operated by heuristic and machine learning methods

Heuristics aim to provide approximate solutions for NP-hard combinatorial optimization (CO) problems in a reasonable time. They are faster than deterministic methods, where the solving process may take days. This paper presents an attempt to combine a heuristic algorithm and some machine learning (ML) methods for solving the Multidimensional Knapsack Problem (MKP), which is a well-known CO case. The approach named Genetic Algorithm and Machine Learning (GAML) is a two-step algorithm that first uses four machine methods to produce four solutions and then integrates the best one within the GA. For this purpose, (1) This approach builds models by training four machine learning (ML) methods using the decision variables obtained from the optimal solutions of simple MKP instances. The decision variable of an item is equal to one if it is selected in the knapsack and zero otherwise. (2) The four models are applied to predict the decision variables for complex MKP instances. (3) A dummy algorithm constructs feasible solutions from the predictions by adding the selected items randomly while the constraints are not violated. (4) For each MKP instance, the best feasible solution among the four built is integrated with the initialization and the crossing operators of a genetic algorithm. An experiment has been conducted on well-known complex MKP benchmarks. The results proved that the models were able to provide high-quality solutions. Also, the proposed algorithm was faster than other approaches in the literature.

Optimal power flow solution via noise-resilient quantum interior-point methods

This paper presents quantum interior-point methods (QIPMs) tailored to tackle the DC optimal power flow (OPF) problem using noisy intermediate-scale quantum devices. The optimization model is redefined as a linearly constrained quadratic optimization. By incorporating the Harrow–Hassidim–Lloyd (HHL) quantum algorithm into the IPM framework, Newton’s direction is determined through the resolution of linear equation systems. To mitigate the impact of HHL error and quantum noise on Newton’s direction, we utilized a noise-tolerant quantum IPM. This approach provides high-quality OPF solutions even in scenarios where inexact solutions to the linear equation systems result in approximated Newton’s direction. To enhance performance in cases of slow convergence and uphold the feasibility of OPF upon convergence, we propose a classically augmented noise-tolerant QIPM. This technique is designed to expedite convergence relative to classical IPM while maintaining the accuracy of the solution. The proposed QIPM variants are studied through comprehensive simulations and error analyses on 3-bus, 5-bus, 118-bus, and 300-bus systems. By modeling the errors and incorporating quantum computer noise, we simulate the algorithms on Qiskit and classical computers to better understand their effectiveness and feasibility under realistic conditions.