Combinatorial Optimization Research Articles

An effective deep learning fusion method for predicting the TVB-N and TVC contents of chicken breasts using dual hyperspectral imaging systems

Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are important freshness indicators of meat. Hyperspectral imaging combined with chemometrics has been proven to be effective in meat detection. However, a challenge with chemometrics is the lack of a universally applicable processing combination, requiring trial-and-error experiments with different datasets. This study proposes an end-to-end deep learning model, pyramid attention features fusion model (PAFFM), integrating CNN, attention mechanism and pyramid structure. PAFFM fuses the raw visible and near-infrared range (VNIR) and shortwave near-infrared range (SWIR) spectral data for predicting TVB-N and TVC in chicken breasts. Compared with the CNN and chemometric models, PAFFM obtains excellent results without a complicated processing combinatorial optimization process. Important wavelengths that contributed significantly to PAFFM performance are visualized and interpreted. This study offers valuable references and technical support for the market application of spectral detection, benefiting related research and practical fields.

A Dynamic Multi-modal deep Reinforcement Learning framework for 3D Bin Packing Problem

The 3D bin packing problem, a notorious NP-hard combinatorial optimization challenge with wide-ranging practical implications, focuses on optimizing spatial allocation within a bin through the arrangement of boxes. Previous studies have shown promise in employing Neural Combinatorial Optimization to tackle such intractable problems. However, due to the inherent diversity in observations and the sparse rewards, current learning-based methods for addressing the 3D bin packing problem have yielded less-than-ideal outcomes. In response to this shortfall, we propose a novel Dynamic Multi-modal deep Reinforcement Learning framework tailored specifically for the 3D Bin Packing Problem, coined as DMRL-BPP. This framework stands apart from existing learning-based bin-packing approaches in two pivotal respects. Firstly, in order to capture the range of observations, we introduce an innovative dynamic multi-modal encoder, comprising dynamic boxes state and gradient height map sub-encoders, effectively modeling multi-modal information. Secondly, to mitigate the challenge of sparse rewards, we put forth a novel reward function, offering an efficient and adaptable approach for addressing the 3D bin packing problem. Extensive experimental results validate the superiority of our method over all baseline approaches, across instances of varying scales.

A Knowledge-Guided Competitive Co-Evolutionary Algorithm for Feature Selection

In real-world applications, feature selection is crucial for enhancing the performance of data science and machine learning models. Typically, feature selection is a complex combinatorial optimization problem and a multi-objective optimization problem. Its primary goals are to reduce the dimensionality of the dataset and enhance the performance of machine learning algorithms. The selection of features in high-dimensional datasets is challenging due to the intricate relationships between features, which pose significant challenges to the performance and computational efficiency of algorithms. This paper introduces a Knowledge-Guided Competitive Co-Evolutionary Algorithm (KCCEA) for feature selection, especially for high-dimensional features. In the proposed algorithm, we make improvements to the foundational dominance-based multi-objective evolutionary algorithm in two aspects. First, the use of feature correlation as knowledge to guide evolution enhances the search speed and quality of traditional multi-objective evolutionary algorithm solutions. Second, a dynamically allocated competitive–cooperative evolutionary mechanism is proposed, integrating the improved knowledge-guided evolution with traditional evolutionary algorithms, further enhancing the search efficiency and diversity of solutions. Through rigorous empirical testing on various datasets, the KCCEA demonstrates superior performance compared to basic multi-objective evolutionary algorithms, providing effective solutions to multi-objective feature selection problems while enhancing the interpretability and effectiveness of prediction models.

On-demand photonic Ising machine with simplified Hamiltonian calculation by phase encoding and intensity detection

The photonic Ising machine is a new paradigm of optical computing that takes advantage of the unique properties of light wave propagation, parallel processing, and low-loss transmission. Thus, the process of solving combinatorial optimization problems can be accelerated through photonic/optoelectronic devices, but implementing photonic Ising machines that can solve arbitrary large-scale Ising problems with fast speed remains challenging. In this work, we have proposed and demonstrated the Phase Encoding and Intensity Detection Ising Annealer (PEIDIA) capable of solving arbitrary Ising problems on demand. The PEIDIA employs the heuristic algorithm and requires only one step of optical linear transformation with simplified Hamiltonian calculation by encoding the Ising spins on the phase term of the optical field and performing intensity detection during the solving process. As a proof of principle, several 20 and 30-spin Ising problems have been solved with high ground state probability (≥0.97/0.85 for the 20/30-spin Ising model).

Reconfigurable stochastic neurons based on strain engineered low barrier nanomagnets

Stochastic neurons are efficient hardware accelerators for solving a large variety of combinatorial optimization problems. ‘Binary’ stochastic neurons (BSN) are those whose states fluctuate randomly between two levels +1 and −1, with the probability of being in either level determined by an external bias. ‘Analog’ stochastic neurons (ASNs), in contrast, can assume any state between the two levels randomly (hence ‘analog’) and can perform analog signal processing. They may be leveraged for such tasks as temporal sequence learning, processing and prediction. Both BSNs and ASNs can be used to build efficient and scalable neural networks. Both can be implemented with low (potential energy) barrier nanomagnets (LBMs) whose random magnetization orientations encode the binary or analog state variables. The difference between them is that the potential energy barrier in a BSN LBM, albeit low, is much higher than that in an ASN LBM. As a result, a BSN LBM has a clear double well potential profile, which makes its magnetization orientation assume one of two orientations at any time, resulting in the binary behavior. ASN nanomagnets, on the other hand, hardly have any energy barrier at all and hence lack the double well feature. That makes their magnetizations fluctuate in an analog fashion. Hence, one can reconfigure an ASN to a BSN, and vice-versa, by simply raising and lowering the energy barrier. If the LBM is magnetostrictive, then this can be done with local (electrically generated) strain. Such a reconfiguration capability heralds a powerful field programmable architecture for a p-computer whereby hardware for very different functionalities such as combinatorial optimization and temporal sequence learning can be integrated in the same substrate in the same processing run. This is somewhat reminiscent of heterogeneous integration, except this is integration of functionalities or computational fabrics rather than components. The energy cost of reconfiguration is miniscule. There are also other applications of strain mediated barrier control that do not involve reconfiguring a BSN to an ASN or vice versa, e.g. adaptive annealing in energy minimization computing (Boltzmann or Ising machines), emulating memory hierarchy in a dynamically reconfigurable fashion, and control over belief uncertainty in analog stochastic neurons. Here, we present a study of strain engineered barrier control in unconventional computing.

Enhancing bio-isoprene production in Escherichia coli through a combinatorial optimization approach

This study proposed a two-step process optimization for enhanced cumulative isoprene production. This involves establishing an isoprene biosynthetic pathway in Escherichia coli BL21 DE-3 via up-regulation of native deoxy xylulose 5-phosphate (DXP) synthase (EcDXS), isopentenyl pyrophosphate isomerase (EcIPPI/EcIDI) and introducing isoprene synthase (PmIspS) from kudzu (Pueraria montana), followed by the process conditions (incubation temperature, time, and inducer concentration) optimization using the Box-Behnken design (BBD) approach. BBD showed the maximum cumulative isoprene production (160.15 mg L-1), productivity (11.18 mg L-1 h-1), and yield (7.69 mg gdcw-1) at the optimized process conditions (incubation temperature 27.32 ºC, incubation time 14.25 h, and inducer concentration 0.453 mM). The transgenic E. coli isoprene production, -productivity, and -yield were 1.52-, 1.46-, and 1.24-fold higher than the unoptimized condition (incubation temperature 30 ºC, incubation time 16 h, and inducer concentration 0.10 mM). This work demonstrates that fine tuning of MEP pathway in addition with process conditions optimization is an efficient strategy for improving isoprene production from engineered E. coli.

Demonstration of an energy-efficient Ising solver composed of Ovonic threshold switch (OTS)-based nano-oscillators (OTSNOs)

As there is an increasing need for an efficient solver of combinatorial optimization problems, much interest is paid to the Ising machine, which is a novel physics-driven computing system composed of coupled oscillators mimicking the dynamics of the system of coupled electronic spins. In this work, we propose an energy-efficient nano-oscillator, called OTSNO, which is composed of an Ovonic Threshold Switch (OTS) and an electrical resistor. We demonstrate that the OTSNO shows the synchronization behavior, an essential property for the realization of an Ising machine. Furthermore, we have discovered that the capacitive coupling is advantageous over the resistive coupling for the hardware implementation of an Ising solver by providing a larger margin of the variations of components. Finally, we implement an Ising machine composed of capacitively-coupled OTSNOs to demonstrate that the solution to a 14-node MaxCut problem can be obtained in 40 µs while consuming no more than 2.3 µJ of energy. Compared to a previous hardware implementation of the phase-transition nano-oscillator (PTNO)-based Ising machine, the OTSNO-based Ising machine in this work shows the performance of the increased speed by more than one order while consuming less energy by about an order.

A Combinatorial Optimization Framework for Probability-Based Algorithms by Means of Generative Models

A Combinatorial Optimization Framework for Probability-Based Algorithms by Means of Generative Models

Autonomous data detection and inspection with a fleet of UAVs

Consider an area of interest A, where a set of nsites lie. Two kinds of information can be captured from each site: light and heavy information. A fleet of m homogeneous UAVs, each one equipped with a battery B, is available at a common depot, where the flight mission of each UAV starts and finishes. The problem we consider focuses on a single flight of the fleet of UAVs and aims at collecting their light information from all sites (that can be retrieved, not necessarily passing over each site, but simply “close” to it). At the same time, the fleet will have to select a limited number of sites from which to collect their heavy information. Flying among sites and acquiring information from them (both light and heavy) has a battery cost. On the other hand, a profit is associated with the action of acquiring heavy information from a site. We refer to the extraction of light and heavy information from a site as to weakly or strongly cover the site. The aim of the problem consists of retrieving light information from all sites while maximizing the overall profit, keeping the battery consumption of each UAV within B. In this paper, we model this real-life situation as a new combinatorial optimization problem that we call m3DIP, for which we provide a mixed integer programming model. Given the high degree of complexity of the problem, in this way we are not able to provide a solution in a reasonable time. To address larger instances we propose a matheuristic in which we exploit a path-based algorithm filled with only a subset of feasible cycles (paths) provided by different heuristics. The output indicates which path to select and the set of nodes to be strongly and weakly covered by each trip. We compare our matheuristic with the results obtained by every single heuristic on a large set of instances, showing that the matheuristic strongly outperforms them. An interesting insight is that even paths provided by a heuristic with very bad performances can be useful if combined with paths provided by other heuristics and if the coverage decisions are reoptimized by the matheuristic. We also show the benefit of adding fictitious additional points that UAVs can visit to weakly cover a subset of sites, without actually visiting none of them.

Combinatorial optimization with quantum imaginary time evolution

Combinatorial optimization with quantum imaginary time evolution

Accurate calculation method of terrain viewshed for wireless signal as line of sight

AbstractViewshed analysis is an important research content of digital terrain analysis. The terrain viewshed refers to the range that can be seen at the current position, which varies with the nature of the observer. When the observer is a wireless signal tower, it is the communication viewshed, which refers to the area consisted of grid cells where the receiving antennas can receive the signals from a transmitting antenna set up on a grid cell of terrain. The core of base station location problem includes two aspects: combinatorial optimization and the calculation of the coverage rate of signal. The calculation of communication viewshed is an important research content for determining signal coverage range. In this article, we propose an accurate communication viewshed computation algorithm for wireless signal (CVCWS) using the projection curve of 3D Fresnel zone analysis based on DEM. The CVCWS method can calculate the signal reception quality at different locations more precisely. Besides, a signal attenuation model is proposed to compute the theoretical attenuation value according to the signal receiving quality index. The proposed algorithm is compared with the existing DEM‐based communication viewshed algorithms, and the theoretical attenuation values are compared with the measured values. The experimental results show that the theoretical values gained by the CVCWS algorithm are close to the measured values, indicating high accuracy of the CVCWS algorithm. The proposed method can provide theoretical support for communication tower location planning and other related applications.

Dynamic programming with meta-reinforcement learning: a novel approach for multi-objective optimization

Multi-objective optimization (MOO) endeavors to identify optimal solutions from a finite array of possibilities. In recent years, deep reinforcement learning (RL) has exhibited promise through its well-crafted heuristics in tackling NP-hard combinatorial optimization (CO) problems. Nonetheless, current methodologies grapple with two key challenges: (1) They primarily concentrate on single-objective optimization quandaries, rendering them less adaptable to the more prevalent MOO scenarios encountered in real-world applications. (2) These approaches furnish an approximate solution by imbibing heuristics, lacking a systematic means to enhance or substantiate optimality. Given these challenges, this study introduces an overarching hybrid strategy, dynamic programming with meta-reinforcement learning (DPML), to resolve MOO predicaments. The approach melds meta-learning into an RL framework, addressing multiple subproblems inherent to MOO. Furthermore, the precision of solutions is elevated by endowing exact dynamic programming with the prowess of meta-graph neural networks. Empirical results substantiate the supremacy of our methodology over previous RL and heuristics approaches, bridging the chasm between theoretical underpinnings and real-world applicability within this domain.

Surrogate-assisted evolutionary algorithms for expensive combinatorial optimization: a survey

As potent approaches for addressing computationally expensive optimization problems, surrogate-assisted evolutionary algorithms (SAEAs) have garnered increasing attention. Prevailing endeavors in evolutionary computation predominantly concentrate on expensive continuous optimization problems, with a notable scarcity of investigations directed toward expensive combinatorial optimization problems (ECOPs). Nevertheless, numerous ECOPs persist in practical applications. The widespread prevalence of such problems starkly contrasts the limited development of relevant research. Motivated by this disparity, this paper conducts a comprehensive survey on SAEAs tailored to address ECOPs. This survey comprises two primary segments. The first segment synthesizes prevalent global, local, hybrid, and learning search strategies, elucidating their respective strengths and weaknesses. Subsequently, the second segment furnishes an overview of surrogate-based evaluation technologies, delving into three pivotal facets: model selection, construction, and management. The paper also discusses several potential future directions for SAEAs with a focus towards expensive combinatorial optimization.

Integration of Mathematical Operators Based on Decision Boundary Complexity and Combinatorial Optimization for Improved Deep Learning Classifiers

The proliferation of complex diseases in livestock, such as lumpy skin disease, demands advanced diagnostic tools that can accurately classify and predict outbreaks. This study explores the integration of complex mathematical operators within deep learning classifiers to enhance their accuracy and efficiency in diagnosing lumpy skin disease. By focusing on the decision boundary complexity, which delineates different disease states in high-dimensional spaces, and employing combinatorial optimization techniques, we develop a novel framework that significantly improves classification performance. The methodology hinges on optimizing the configuration and combination of mathematical operators, such as gradient operators and higher-order derivatives, to refine feature extraction processes. This approach allows for a more nuanced understanding of the disease features that are critical for accurate classification. Using a dataset comprised of clinical and image data from infected cattle, our enhanced classifiers demonstrate a marked improvement in predictive accuracy compared to traditional deep learning models. The case study not only underscores the potential of integrating advanced mathematical concepts into deep learning but also sets a precedent for tackling similar challenges in veterinary medicine and beyond.

Evaluation of the Microbiological Quality of Some Marketed Spices in Lomé (Togo)

This comprehensive study delves deeply into the intricate process of optimizing fish product distribution routes in the expansive Western Kenya region, with an explicit focus on understanding and refining the operational strategies employed by Victory Farm Limited. Leveraging the Hungarian Method, celebrated for its unparalleled effectiveness in the realm of combinatorial optimization, the research endeavors to meticulously evaluate and fine-tune the shortest and most efficient transportation routes for seamlessly ferrying fish products from Kisumu Logistic Center to a myriad of distribution depots and retail markets scattered across the Western Kenya region's diverse landscape. Through an exhaustive analysis that spans road networks, logistical constraints, and the ever-evolving dynamics of market demand, this study systematically identifies and delineates optimal routes that not only minimize the distance traversed but also significantly mitigate associated transportation costs, all while steadfastly adhering to stringent standards for punctuality and product quality. Furthermore, by synergistically integrating sophisticated mathematical modeling techniques, meticulously executed through Python programming, with a robust foundation of real-world data meticulously sourced from Victory Farm Limited, this research endeavors to provide invaluable insights and pragmatic recommendations aimed at fortifying and enhancing the overall efficiency and profitability of fish product distribution operations throughout Victory Farm Western Kenya depots. In essence, the anticipated outcomes of this study transcend the realm of theoretical conjecture, poised instead to catalyze tangible advancements in the sustainable development of the region's burgeoning aquaculture sector, thereby fostering a lasting legacy of economic prosperity and environmental stewardship for generations to come.

Integration of Complex Mathematical Approaches and Statistical Methodologies in Machine Learning for Design of Robust Prediction Models

The disease known as breast cancer continues to be one of the most common and potentially fatal diseases that affect women all over the world. In order to provide effective therapy and more favorable outcomes for patients, early detection and correct diagnosis are absolutely necessary. This research explores the integration of operational research (OR) methodologies and advanced statistical methods within machine learning frameworks to enhance the prediction accuracy of breast cancer outcomes. By harnessing the power of nonlinear optimization and statistical techniques, including regression analysis and probability distribution models, we aim to refine the predictive capabilities of existing algorithms. The research employs a comprehensive dataset derived from clinical trials and patient records, analyzed through a series of machine learning models that incorporate elements of combinatorial optimization, decision analysis, and stochastic modeling. Key performance metrics, such as accuracy, sensitivity, and specificity, are evaluated against standard benchmarks to determine the efficacy of the integrated approaches. Preliminary results indicate that incorporating OR and statistical methods significantly improves model robustness and predictive accuracy. The study not only demonstrates the potential of applied mathematics in medical diagnostics but also provides a framework for future research in enhancing machine learning models for health outcomes prediction through mathematical innovations. This investigation contributes to the field of mathematical oncology by demonstrating how applied nonlinear analysis can bridge the gap between theoretical mathematical approaches and practical clinical applications, offering new pathways for early and more accurate detection of breast cancer.

Topologically Defective Lattice Potential‐Based Gain‐Dissipative Ising Annealer with Large Noise Margin

AbstractGain‐dissipative Ising machines (GIMs) are annealers inspired by physical systems such as Ising spin glasses to solve combinatorial optimization problems. Compared to traditional quantum annealers, GIM is relatively easier to scale and can save on additional power consumption caused by low‐temperature cooling. However, traditional GIMs have a limited noise margin. Specifically, their normal operation requires ensuring that the noise intensity is lower than their saturation fixed point amplitude, which may result in increased power consumption to suppress noise‐induced spin state switching. To enhance the noise robustness of GIM, in this study a GIM based on a topologically defective lattice potential (TDLP) is proposed. Numerical simulations demonstrate that the TDLP‐based GIM can accurately simulate the bifurcation spin evolution in the Ising model. Furthermore, through the MAXCUT benchmark based on G‐set graphs, the optimal performance of TDLP‐based GIM is shown to surpass that of traditional GIMs. Additionally, the proposed TDLP‐based GIM successfully solves the MAXCUT benchmark and domain clustering dynamics benchmark based on G‐set graphs when the noise intensity exceeds its saturation fixed‐point amplitude. This indicates that the proposed system provides a promising architecture for breaking the small noise constraints required by traditional GIMs.

Multi-observation points setting problem based on stepwise maximum viewshed approach

Terrain visibility analysis is vital in geospatial research, employing computer geometry and graphics principles for computing and visualizing visibility between observation and target points. The observation point setup problem is crucial for tasks like sentry point selection and signal base station placement. This problem involves choosing the minimum viewpoints on terrain for optimal joint view coverage, presenting a combinatorial optimization challenge. As technology advances, data can be acquired with increasing precision, the number of viewpoints that can be extracted from the same area is gradually increasing as well, obtaining candidate viewpoints and determining optimal combinations become challenging. This paper proposes a novel method, Stepwise Maximum Viewshed (SMV), addressing observation point setup by stepwise filtering the maximum viewshed and can customize the size of the observation point combination. In complex mountainous terrain, the SMV algorithm demonstrates superior joint view coverage compared to Candidate Viewpoints Filtering (CVF) and Simulated Annealing (SA) algorithms. Experimental results reveal up to 5.59% improvement over CVF and a maximum of 12.52% over SA in joint viewshed coverage.

Automated Pricing and Replenishment Decisions for Vegetable Products Based on Data Analysis

This study delves into the critical factors for the successful operation of vegetable products supermarkets: efficient inventory management and accurate market demand analysis. Given the perishability of vegetables, their variety, and the complexity of supply chains, a linear regression model and the method of least squares have been developed to estimate model parameters. Additionally, the ARIMA model is utilized to forecast the unit prices of commodities. In a more comprehensive approach, considering various influencing factors, a combinatorial optimization algorithm (integrating linear regression and greedy algorithm) is employed to determine the optimal replenishment strategy. Based on this, the study forecasts the sales volume and unit prices for various categories over the next seven days, and accordingly, establishes the pricing strategy and predicted replenishment volume for each category. The research underscores the necessity for retailers to precisely analyze sales data and market trends, optimize supply chain management, and formulate effective pricing strategies to cope with seasonal variations and limited sales space. These measures are essential to satisfy consumer demand and enhance profitability.

A comprehensive survey of convergence analysis of beetle antennae search algorithm and its applications

In recent years, swarm intelligence optimization algorithms have been proven to have significant effects in solving combinatorial optimization problems. Introducing the concept of evolutionary computing, which is currently a hot research topic, into swarm intelligence optimization algorithms to form novel swarm intelligence optimization algorithms has proposed a new research direction for better solving combinatorial optimization problems. The longhorn beetle whisker search algorithm is an emerging heuristic algorithm, which originates from the simulation of longhorn beetle foraging behavior. This algorithm simulates the touch strategy required by longhorn beetles during foraging, and achieves efficient search in complex problem spaces through bioheuristic methods. This article reviews the research progress on the search algorithm for longhorn beetles from 2017 to present. Firstly, the basic principle and model structure of the beetle whisker search algorithm were introduced, and its differences and connections with other heuristic algorithms were analyzed. Secondly, this paper summarizes the research achievements of scholars in recent years on the improvement of longhorn whisker search algorithms. Then, the application of the beetle whisker search algorithm in various fields was explored, including function optimization, engineering design, and path planning. Finally, this paper summarizes the research achievements of scholars in recent years on the improvement of the longhorn whisker search algorithm, and proposes future research directions, including algorithm deep learning fusion, processing of multimodal problems, etc. Through this review, readers will have a comprehensive understanding of the research status and prospects of the longhorn whisker search algorithm, providing useful guidance for its application in practical problems.