Discrete Optimization Algorithm Research Articles

Global and local semantic enhancement of samples for cross-modal hashing

Hashing becomes popular in cross-modal retrieval due to its exceptional performance in both search and storage. However, existing cross-modal hashing (CMH) methods may (a) neglect to learn sufficient modal-specific information, and (b) fail to fully exploit sample semantics. To address these issues, we propose a method called Semantic Enhancement of Sample Hashing (SESH). First, SESH employs a global modal-specific learning strategy to draw overall shared information and global modal-specific information by factoring the mapping matrix. Second, SESH introduces manifold learning and a local modal-specific learning strategy to extract additional local modal-specific and modal-shared data under label guidance. Meanwhile, local modal-specific information is integrated with global modal-specific details to add rich modal-specific information. Third, SESH uses discrete maximum similarity and orthogonal constraint transformation to enhance both global and local semantic information, embedding more discriminative information into the Hamming space. Finally, an efficient discrete optimization algorithm is proposed to generate the hash codes directly. Experiments on three datasets demonstrate the superior performance of SESH. The source code will be available at https://github.com/kokorording/SESH.

Scheduling of Collaborative Vegetable Harvesters and Harvest-Aid Vehicles on Farms

Transporting harvested vegetables in the field or greenhouse is labor-intensive. The utilization of small harvest-aid vehicles can reduce non-productive time for farmers and improve harvest efficiency. This paper models the process of harvesting vegetables in response to non-productive waiting delays caused by the scheduling of harvest-aid vehicles. Taking into consideration harvesting speed, harvest-aid vehicle capacity, and scheduling conflicts, a harvest-aid vehicle scheduling model is constructed to minimize non-production waiting time and coordination costs. Subsequently, to meet the collaborative needs of harvesters, this paper develops a discrete multi-objective Jaya optimization algorithm (DMO-Jaya), which combines an opposition-based learning mechanism and a long-term memory library to obtain scheduling schemes suitable for agricultural environments. Experiments show that the studied model can schedule harvest-aid vehicles without conflicts. Compared to the NSGA-II algorithm and the MMOPSO, the DMO-Jaya algorithm demonstrates a better diversity of solutions, resulting in a shorter non-productive waiting time for harvesters. This research provides a reference model for improving the efficiency of vegetable harvesting and transportation.

Continuous–Discrete Student Psychology-Based Optimization Algorithm for Optimal Planning of Distributed Generators and Distribution Static Compensators in Power Distribution Network

Continuous–Discrete Student Psychology-Based Optimization Algorithm for Optimal Planning of Distributed Generators and Distribution Static Compensators in Power Distribution Network

Co-optimization of the operation and energy for AGVs considering battery-swapping in automated container terminals

Since the number of containers in automated container terminals (ACTs) has been growing, the operation of ACT relies on a large amount of electric power, which comes from the terminal energy system. As the main horizontal transportation equipment in ACTs, the scheduling of automated guided vehicles (AGVs) has become increasingly important. Even though AGV scheduling has been studied, the battery-swapping procedure is often overlooked, which hinders operation efficiency and the usage of renewable energy. We propose a co-optimization problem of the operation and energy for AGVs considering battery-swapping in this paper. A multi-objective model is constructed to minimize the makespan of AGVs and the operation and maintenance cost of energy system. In addition, we create a discrete multi-objective whale optimization algorithm to address this co-optimization problem. A load-balancing-based method for population initialization is developed to make the distribution of solutions more uniform in this algorithm. With the two-dimensional matrix encoding scheme, an individual right-shifted method is proposed for decoding. The best solution is assessed and selected through a fitness evaluation mechanism based on fuzzy correlation entropy. The bubble-net attack based on opposition-based learning is implemented to enhance the capacity for local intensification. Numerous tests confirm that the proposed method can guarantee the operation efficiency, lower the total cost of the energy system, and promote sustainable development in ACTs.

Two-dimensional and high-order directional information modulations for secure communications based on programmable metasurface

Conventional wireless communication schemes indiscriminately transmit information into the whole space and pose inherent security risks. Recently, directional information modulation (DIM) has attracted enormous attention as a promising technology. DIM generates correct constellation symbols in the desired directions and distorts them in undesired directions, thus ensuring the security of the transmitted information. Although several DIM schemes have been reported, they suffer from defects of bulkiness, energy consumption, high cost, and inability to support two-dimensional (2D) and high-order modulations. Here, we propose a DIM scheme based on a 2-bit programmable metasurface (PM) that overcomes these defects. A fast and efficient discrete optimization algorithm is developed to optimize the digital coding sequences, and the correct constellation symbols can be generated and transmitted in multi-directional beams. As a proof-of-concept, three sets of constellation diagrams (8 phase shift keying (PSK), 16 quadrature amplitude modulation (QAM), and 64QAM) are realized in the multi-channel modes. This work provides an important route of employing DIM for ensuring physical-layer security and serves as a stepping stone toward endogenous secure communications.

Parallel Disassembly Sequence Planning Using a Discrete Whale Optimization Algorithm for Equipment Maintenance in Hydropower Station

In a hydropower station, equipment needs maintenance to ensure safe, stable, and efficient operation. And the essence of equipment maintenance is a disassembly sequence planning problem. However, the complexity arises from the vast number of components in a hydropower station, leading to a significant proliferation of potential combinations, which poses considerable challenges when devising optimal solutions for the maintenance process. Consequently, to improve maintenance efficiency and decrease maintenance time, a discrete whale optimization algorithm (DWOA) is proposed in this paper to achieve excellent parallel disassembly sequence planning (PDSP). To begin, composite nodes are added into the constraint relationship graph based on the characteristics of hydropower equipment, and disassembly time is chosen as the optimization objective. Subsequently, the DWOA is proposed to solve the PDSP problem by integrating the precedence preservative crossover mechanism, heuristic mutation mechanism, and repetitive pairwise exchange operator. Meanwhile, the hierarchical combination method is used to swiftly generate the initial population. To verify the viability of the proposed algorithm, a classic genetic algorithm (GA), simplified teaching–learning-based optimization (STLBO), and self-adaptive simplified swarm optimization (SSO) were employed for comparison in three maintenance projects. The experimental results and comparative analysis revealed that the proposed PDSP with DWOA achieved a reduced disassembly time of only 19.96 min in Experiment 3. Additionally, the values for standard deviation, average disassembly time, and the rate of minimum disassembly time were 0.3282, 20.31, and 71%, respectively, demonstrating its superior performance compared to the other algorithms. Furthermore, the method proposed in this paper addresses the inefficiencies in dismantling processes in hydropower stations and enhances visual representation for maintenance training by integrating Unity3D with intelligent algorithms.

A Dual-Polarization Programmable Metasurface for Green and Secure Wireless Communication.

Dual-polarization programmable metasurfaces can flexibly manipulate electromagnetic (EM) waves while providing approximately twice the information capacity. Therefore, they hold significant applications in next-generation communication systems. However, there are three challenges associated with the existing dual-polarization programmable metasurfaces. This article aims to propose a novel design to address them. First, the design overcomes the challenge of element- and polarization-independent controls, enabling more powerful manipulations of EM waves. Second, by using more energy-efficient tunable components and reducing their number, the design can be nearly passive (maximum power consumption of 27.7mW), leading to a significant decrease in the cost and power consumption of the system (at least two orders of magnitude lower than the power consumption of conventional programmable metasurfaces). Third, the design can operate in a broad bandwidth, which is attractive for practical engineering applications. Both the element and array of the metasurface are meticulously designed, and their performance has been carefully studied. The experiments demonstrate that 2D wide-angle beam scanning can be realized. Moreover, secure communication based on directional information modulation can be implemented by exploiting the metasurface and an efficient discrete optimization algorithm, showing its programmable, multiplexing, broadband, green, and secure features.

Integrated optimization of well placement and perforation layer selection using a modified dung beetle algorithm

In the oilfield development process, a proper well pattern plays a crucial role in improving oil recovery and economic benefits. However, traditional well pattern optimization mainly focuses on two-dimensional plane well placement, overlooking the spatial alignment of perforated layers and well placements. This oversight presents significant challenges, especially in meeting the demands for fine development of high water-cut oilfields. Therefore, this paper introduces an innovative approach to well pattern optimization, termed integrated well pattern optimization (WPO–I), which couples well placements with perforation positions. By maximizing net present value, a novel mathematical model for well pattern optimization is established, considering the location of infill wells, the status and type of existing wells, and the perforation position of working wells as co-optimization variables. Besides, an improved discrete dung beetle optimization (IDDBO) algorithm is developed, utilizing multi-layer integer coding and an opposition-based learning strategy. To evaluate the proposed method's performance, the Egg model is used to compare it with five optimization algorithms. Furthermore, this study assesses the development effects of conventional plane well placement optimization (WPO–P) compared to WPO-I. The results highlight the distinct advantages of the IDDBO algorithm in solving discrete nonlinear programming problems. Particularly, the WPO-I method enables spatial matching of the well pattern with reservoir heterogeneity and remaining oil distribution, effectively utilizing spatial residual oil. This method provides a new development optimization strategy in mature oilfields, especially for fine adjustment of well patterns during the secondary recovery stage of water flooding.

A discrete moth-flame optimization algorithm for multiple automated guided vehicles scheduling problem in a matrix manufacturing workshop

The growing need for customized and varied production has highlighted the significance of smart and automated factories in the manufacturing sector. In this particular context, the scheduling of multiple Automated Guided Vehicles (AGVs) plays a pivotal role in enhancing the efficiency of operations within an intelligent manufacturing shop. This study centers on the material handling process within a matrix manufacturing shop with the objective of identifying the most cost-effective transportation routes for materials. To accomplish the specified objective, this research aims to identify an optimal solution for reducing transportation costs. In particular, this study formulates a mixed-integer linear programming model and introduces a novel discrete variant of the moth-flame optimization (MFO) algorithm, named DMFO, to address the scheduling problem. The DMFO algorithm incorporates several significant enhancements. Firstly, a population initialization method is proposed, which combines a nearest-neighbor-based adaptive heuristic and a random sorting technique to ensure the formation of a well-structured population. Secondly, the flame generation mechanism and spiral flight search processes within the MFO have been redefined to achieve a more optimal balance between exploration and exploitation. A neighborhood search mechanism is subsequently devised, employing the concept of neighborhood relevance to accelerate the convergence process. Additionally, a heuristic approach is introduced to reduce the computational cost. Moreover, a population regeneration mechanism is proposed to avoid the algorithm falling into a local optimum. To validate the effectiveness of the DMFO, a comparative analysis is conducted using a dataset of 110 real-world factory instances. In this analysis, eight well-established optimization algorithms are employed. The simulation results consistently demonstrate that the relative percentage deviation (RPD) value of the DMFO tends to approach 0% more closely compared to other algorithms, thereby substantiating the effectiveness of the proposed algorithm.

Fast unsupervised multi-modal hashing based on piecewise learning

Unsupervised hashing has been extensively applied in large-scale multi-modal retrieval by mapping original data from heterogeneous modalities into unified binary codes. However, there still remain challenges especially how to balance the individual modality-specific representations and common representation preserving intrinsic linkages among heterogeneous modalities. In this paper, we propose a novel fast Unsupervised Multi-modal Hashing based on Piecewise Learning, denoted as UMHPL, to deal with the mentioned issue. Initially, we formulate the problem as matrix factorization to derive the individual modality-specific latent representations and common latent representation with consensus matrices in a brief time. To maintain the integrality of multi-modal data, we integrate them by adaptive weight factors and nuclear norm minimization. Subsequently, we establish a connection between the individual modality-specific latent representations and common latent representation based on the piecewise hash learning framework to reinforce the discriminative competency of model, which leads the hash codes more compact. Finally, an effective discrete optimization algorithm in mathematical logic and functional analysis is proposed. Comprehensive experiments on Wiki, MIRFlirck, NUS-WIDE, and MSCOCO datasets demonstrate the superior performance of UMHPL to state-of-the-art hashing methods.

Success-History Based Adaptive Differential Evolution Algorithm for Discrete Structural Optimization

Success-History Based Adaptive Differential Evolution Algorithm for Discrete Structural Optimization

A novel solid waste instance creation for an optimized capacitated vehicle routing model using discrete smell agent optimization algorithm

This paper presents an optimal vehicle routing model for an efficient waste collection process using the Ogun State Waste Management Agency (OGWAMA) as a case study. Just like in many cases, the current manual predetermined routing method used by OGWAMA is inefficient and contributes to excessive fuel usage. These challenges, in addition to the small instances reported in most literature, inspire this research to propose an improved routing scheme that takes into account real-time costs and eventually develops a novel instance based on OGWAMA's operation mode. The developed model was optimized using a new discrete smell agent optimization (SAO) algorithm and compared to firefly algorithm (FA) and particle swarm optimization (PSO). The SAO outperformed FA and PSO, achieving 3.92 % and 19.38 % improvements in service cost (SC) and 2.65 % and 14.96 % improvements in total travel distance (TTD), respectively. The convergence rates of the algorithms were also compared; using the Optimized Depot (OD) techniques and results shows the acceptability of the proposed approaches.

Projected Gaussian Markov Improvement Algorithm for High-Dimensional Discrete Optimization via Simulation

This article considers a discrete optimization via simulation (DOvS) problem defined on a graph embedded in the high-dimensional integer grid. Several DOvS algorithms that model the responses at the solutions as a realization of a Gaussian Markov random field (GMRF) have been proposed exploiting its inferential power and computational benefits. However, the computational cost of inference increases exponentially in dimension. We propose the projected Gaussian Markov improvement algorithm (pGMIA), which projects the solution space onto a lower-dimensional space creating the region-layer graph to reduce the cost of inference. Each node on the region-layer graph can be mapped to a set of solutions projected to the node; these solutions form a lower-dimensional solution-layer graph. We define the response at each region-layer node to be the average of the responses within the corresponding solution-layer graph. From this relation, we derive the region-layer GMRF to model the region-layer responses. The pGMIA alternates between the two layers to make a sampling decision at each iteration. It first selects a region-layer node based on the lower-resolution inference provided by the region-layer GMRF, then makes a sampling decision among the solutions within the solution-layer graph of the node based on the higher-resolution inference from the solution-layer GMRF. To solve even higher-dimensional problems (e.g., 100 dimensions), we also propose the pGMIA+: a multi-layer extension of the pGMIA. We show that both pGMIA and pGMIA+ converge to the optimum almost surely asymptotically and empirically demonstrate their competitiveness against state-of-the-art high-dimensional Bayesian optimization algorithms.

DISCRETE OPTIMIZATION OF THE IMPLEMENTATION STAGES OF PHARMACEUTICAL DEVELOPMENT FOR A SPRAY TREATMENT FOR ORAL DISEASES

Introduction. All over the world more than 3.5 billion people suffer from oral diseases, which can lead to endogenous infectious diseases and create conditions for external infections. The growing antimicrobial resistance of bacterial strains is a global health threat. Oligoalkyleneguanidine polymers may be promising compounds to solve this problem. The spray form for the application of these substances is the most optimal. The aim of the study is to apply discrete optimization to implement the stages of pharmaceutical development of a spray based on branched oli-gohexamethyleneguanidine for the treatment of oral diseases. Material and methods. Experiments are carried out using various equipment and samples with different compositions have been developed. The implementation of the pharmaceutical development stages was carried out using a discrete optimization algorithm. Results. When implementing discrete optimization in pharmaceutical development, it is necessary to prioritize criteria and limitations using the target quality profile of the drug being developed. As a result of the optimization cycles carried out, the optimal composition was selected, which corresponds to the target quality profile, including such parameters as pH, dynamic viscosity, sterilizing filtration, adhesion of the formulations to the oral mucosa and the spray torch. A discrete optimization was carried out, taking into account the wetting edge angle and particle size distribution. The optimal com-position was sample No. 8, which has pseudoplastic properties, provides unhindered spraying and prevents the composition from draining from the mucous membrane of the oral cavity. Conclusion. During the study, the optimal ratio of components was determined and the development of a spray based on oligohexamethyleneguani-dine with the implementation of stages of pharmaceutical development for discrete optimization was proposed.

Loading pattern optimization of VVER-1000 reactor core based on the discrete golden eagle optimization algorithm

The main features of the loading pattern optimization (LPO) problem, such as high-dimensionality, multi-modality, and non-linearity, make it difficult to achieve a truly optimal configuration. In recent years, metaheuristic methods have been successfully used to solve this problem. In this research, a discrete golden eagle optimization (DGEO) algorithm has been developed to solve the LPO problem in the first cycle of VVER-1000 reactor core. To evaluate the proposed algorithm, a linear multi-purpose fitness function has been used to improve the safety parameters of the reactor core by obtaining a flatter power distribution during the first cycle, and also to enhance the economic parameters by increasing the cycle length and reducing the cost of fuel recycling. For this purpose, a FORTRAN program has been written to map the DGEO algorithm for the LPO problem using the PMAX and PARCS core calculation code to compute the fitness function in each iteration. To speed up the calculations, parallel computing has been applied in the written program. The results demonstrated that the loading pattern, which is suggested by the DGEO algorithm, enhances all the safety and economic parameters in the fitness function. Thus, the DGEO algorithm is highly reliable for the LPO problems in the VVER 1000 reactor core.

Optimal scheduling and demand response implementation for home energy management

The optimal scheduling of the loads based on dynamic tariffs and implementation of a direct load control (DLC) based demand response program for the domestic consumer is proposed in this work. The load scheduling is carried out using binary particle swarm optimization and a newly prefaced nature-inspired discrete elephant herd optimization technique, and their effectiveness in minimization of cost and the peak-to-average ratio is analyzed. The discrete elephant herd optimization algorithm has acceptable characteristics compared to the conventional algorithms and has determined better exploring properties for multi-objective problems. A prototype hardware model for a home energy management system is developed to demonstrate and analyze the optimal load scheduling and DLC-based demand response program. The controller effectively schedules and implements DLC on consumer devices. The load scheduling optimization helps to improve PAR by a value of 2.504 and results in energy cost savings of ₹ 12.05 on the scheduled day. Implementation of DLC by 15% results in monthly savings of ₹ 204.18. The novelty of the work is the implementation of discrete elephant herd optimization for load scheduling and the development of the prototype hardware model to show effects of both optimal load scheduling and the DLC-based demand response program implementation.

Manipulation order optimization in industrial pick-and-place operations: application to textile and leather industry

This work addresses the problem of the development of a robotic system for the picking of parts cut by a CNC machine and the optimization of the sequencing of this picking process. An automated parts collection system is optimized to reduce the time required to perform the task of both picking and the subsequent classification by the type of part. The automated picking system, which is located at the end of a cutting machine, uses a robot equipped with an additional axis to expand its working space. Therefore, in this proposal, the industrial equipment necessary to automate this process is designed and the process to be optimized is computationally modeled. In particular, three discrete optimization algorithms are analyzed, with different evolution strategies and operators, but all of them are free of specific configuration parameters. The whole process is shown in this research, from the design of the procedure to the design of the tool, the algorithm selection, and elements validation. Finally, the first steps towards its industrial implementation are presented, and the hypothesis behind this project is validated.

A dual-robot cooperative arc welding path planning algorithm based on multi-objective cross-entropy optimization

In this paper a novel discrete multi-objective cross-entropy optimization (CrMOCEO) algorithm is proposed to solve the path planning problem of dual-robot cooperative arc welding. We strive to find a low-cost, fast and more efficient solution for robotic welding of large complex components. Firstly, an optimization model of dual-robot welding path planning is established by considering various variables and constraints in the actual welding process. Then, three strategies are introduced to improve the multi-objective cross-entropy optimization (MOCEO) algorithm to better solve the discrete path planning problem. Finally, in order to verify feasibility and effectiveness of the proposed algorithm, it is used to solve the 2-, 3-, 5- and 7-objective WFG2–9 problems and plan some typical welding seams of a large complex component, the MOCEO, NSGA-Ⅱ, MOPSO and MOGWO are used for comparison. The simulation demonstrates that the CrMOCEO can obtain better solutions for multiple objectives than the other four algorithms, and the path solved by the CrMOCEO is tested in the Gazebo physical model and workshop site, the results further verified the effectiveness of the CrMOCEO algorithm. Particularly, a series of experiments provide more solutions for actual production.

A Discrete Adaptive Lion Optimization Algorithm for QoS-Driven IoT Service Composition with Global Constraints

A Discrete Adaptive Lion Optimization Algorithm for QoS-Driven IoT Service Composition with Global Constraints

Time-discrete momentum consensus-based optimization algorithm and its application to Lyapunov function approximation

In this paper, we study a discrete momentum consensus-based optimization (Momentum-CBO) algorithm which corresponds to a second-order generalization of the discrete first-order CBO [S.-Y. Ha, S. Jin and D. Kim, Convergence of a first-order consensus-based global optimization algorithm, Math. Models Methods Appl. Sci. 30 (2020) 2417–2444]. The proposed algorithm can be understood as the modification of ADAM-CBO, replacing the normalization term by unity. For the proposed Momentum-CBO, we provide a sufficient framework which guarantees the convergence of algorithm toward a global minimum of the objective function. Moreover, we present several experimental results showing that Momentum-CBO has an improved success rate of finding the global minimum compared to vanilla-CBO and show the stability of Momentum-CBO under different initialization schemes. We also show that Momentum-CBO can be used as the alternative of ADAM-CBO which does not have a proper convergence analysis. Finally, we give an application of Momentum-CBO for Lyapunov function approximation using symbolic regression techniques.