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This paper combines the digital twin system modeling method to conduct an in-depth study and analysis of graph-theoretic combinatorial optimization. This paper provides new ideas and approaches for optimal numerical analysis work by studying the digital twin modeling method that integrates digital modeling and graph theory combination, provides theoretical support for safe, stable, and economic operation of the system, proposes a solution for digital twin model based on big data platform, focuses on the nearest neighbor propagation (AP) and graph theory combination, solves the digital twin real-time monitoring data asynchronous, incomplete problem, and applies the algorithm to the digital twin model based on the big data platform for data preprocessing to achieve better results. This paper also presents a web-based digital twin system based on intelligent practical needs, analysis, and comparison of existing models, combined with digital twin technology, detailing the differences and connections between the various levels of numerical analysis and the implementation of this data in various fields, such as user management, equipment health management, product quality management, and workshop 3D navigation and detailed modeling of the digital twin system based on this numerical analysis to realize remote online monitoring, analysis, and management. In this paper, for the numerical analysis process, firstly, the key technologies of modeling and simulation operation control of production line based on digital twin are studied, and the rapid response manufacturing system based on a digital twin is designed and validated. Secondly, a scheduling technology framework for capacity simulation evaluation and optimization is established, and batching optimization, outsourcing decision, and rolling scheduling techniques are thus proposed to form a batching optimization algorithm based on priority rules, which realizes batching processing, outsourcing decision, and rolling scheduling of production orders to optimize equipment utilization and capacity. Finally, digital twin-based modeling is designed, and the validation results demonstrate the system's superior performance in achieving information interaction between physical and virtual production lines, optimization of numerical analysis, and display of results. [ABSTRACT FROM AUTHOR]

Let G = (V, E) be a connected, basic, and finite graph. A subset T = u 1 , u 2 , ... , u k of V(G) is said to be a resolving set if for any y ∈ V(G), the code of y with regards to T, represented by C T y , which is defined as C T y = d u 1 , y , d u 2 , y , ... , d u k , y , is different for various y. The dimension of G is the smallest cardinality of a resolving set and is denoted by dim(G). If, for any t ∈ V – S, there exists r ∈ S such that S – r ∪ t is a resolving set, then the resolving set S is secure. The secure metric dimension of is the cardinal number of the minimum secure resolving set. Determining the secure metric dimension of any given graph is an NP-complete problem. In addition, there are several uses for the metric dimension in a variety of fields, including image processing, pattern recognition, network discovery and verification, geographic routing protocols, and combinatorial optimization. In this paper, we determine the secure metric dimension of special graphs such as a globe graph G l n , flag graph F l n , H- graph of path P n , a bistar graph B n , n 2 , and tadpole graph T 3 , m . Finally, we derive the explicit formulas for the secure metric dimension of tadpole graph T n , m , subdivision of tadpole graph S T 3 , m , and subdivision of tadpole graph S T n , m . [ABSTRACT FROM AUTHOR]

The stochastic skiving stock problem (SSP), a relatively new combinatorial optimization problem, is considered in this paper. The conventional SSP seeks to determine the optimum structure that skives small pieces of different sizes side by side to form as many large items (products) as possible that meet a desired width. This study studies a multiproduct case for the SSP under uncertain demand and waste rate, including products of different widths. This stochastic version of the SSP considers a random demand for each product and a random waste rate during production. A two-stage stochastic programming approach with a recourse action is implemented to study this stochastic N P -hard problem on a large scale. Furthermore, the problem is solved in two phases. In the first phase, the dragonfly algorithm constructs minimal patterns that serve as an input for the next phase. The second phase performs sample-average approximation, solving the stochastic production problem. Results indicate that the two-phase heuristic approach is highly efficient regarding computational run time and provides robust solutions with an optimality gap of 0.3% for the worst-case scenario. In addition, we also compare the performance of the dragonfly algorithm (DA) to the particle swarm optimization (PSO) for pattern generation. Benchmarks indicate that the DA produces more robust minimal pattern sets as the tightness of the problem increases. [ABSTRACT FROM AUTHOR]

Writing is an important part of testing language ability, and it is urgent to find some objective indicators to determine and evaluate the surface language structure, which will help language learners' better master the target language. Complexity and semantic coherence are considered to be an important factor in the teaching of second language writing. In practice, due to the complexity of English writing syntax, such as a large number of high-dimensional nonlinear optimization problems, a new intelligent evaluation method is needed to solve them. At present, particle swarm optimization (PSO) has been widely used in function optimization, neural network training, combinatorial optimization, and other fields. This paper studies the syntactic complexity and semantic coherence of academic English writing based on PSO. The number of phrases is related to writing achievement. When the number of experiments reaches 25, the significant values of syntactic complexity and semantic coherence of data mining algorithm, artificial intelligence algorithm, decision tree algorithm, and PSO algorithm are 0.008, 0.003, 0.002, and 0.013, respectively, which shows that PSO algorithm is the best among them. [ABSTRACT FROM AUTHOR]

Accelerating product flow, improving service level, lowering logistics costs, reducing the possibility of product losses in circulation, and thus optimizing the logistics distribution system are the issues that enterprise managers should consider in logistics distribution. Traditional algorithms can only solve simple problems, while intelligent algorithms can solve the most complex combinatorial optimization problems. The optimization problem of logistics vehicle scheduling path with different constraints is studied in this paper using the SVM algorithm, and the improved algorithm is simulated to verify its effectiveness. The simulation results show that the logistics distribution path optimization method based on the SVM algorithm has good global searching ability, effectively avoids the algorithm falling into local optimum, and reduces total distribution cost, proving the algorithm's effectiveness. This scheme can optimize vehicle routes, increase distribution efficiency, and reduce logistics costs, and it can be used in a wide range of logistics distribution route optimization applications. [ABSTRACT FROM AUTHOR]

Optimal project portfolio planning is a typical nonconvex, multiobjective, highly constrained, multitemporal coupling, and combinatorial optimization problem. This paper proposes a novel multistep iterative ranking learning method (MIRL) to solve this complex combinatorial optimization problem from massive infrastructure projects of smart grid. The optimal project portfolio planning problem of power grid is formulated as the optimization process of massive project priority sorting with an improved knapsack model. The proposed method dynamically optimizes the best infrastructure project combination for each round to maximize the economic, social, and security benefits without exceeding the annual investment limit. A pairwise-based ranking learning algorithm is used to mine the priority sorting law from massive historical combination data of power grid to initialize candidate project portfolio. In order to approach the optimal portfolio planning solution with the constraint satisfactions of project construction duration and electric load supplies, a heuristic greedy strategy is designed to search the solution dynamically for selecting the project having highest construction benefits iteratively. The effectiveness of the proposed method is proved by experiments with real-world project data of Hunan power grid in China, and experimental results show that the proposed MIRL can outperform other methods on investment efficiency, calculation time, and rationality of project construction period schedule. [ABSTRACT FROM AUTHOR]

This paper addresses a multiobjective Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGVs) collaboration scheduling problem in which UAVs must be recharged periodically during a persistent task, such as wireless communication and making aerial panoramic VR video by UAV. The proposal is to introduce a UGV/UAV cooperative system in which UAVs should be recharged by UGVs periodically, and also, the UGVs have to visit their task points expect for the recharge points. The objective of the problem is to schedule and plan paths with the tradeoff of path length and makespan for UAVs and UGVs. The mathematical model which can be considered a combinatorial multiobjective optimization problem is presented firstly, and the solution of the problem is composed of presubsolution and postsubsolution. The multiobjective adaptive large neighborhood is hybrid with the Pareto local search (MOALP) to resolve the problem. The experimental result shows that the proposed algorithm outperforms the compared algorithms on the rendezvous planning problem for UAVs and UGVs working collaboratively in intelligent tasks. [ABSTRACT FROM AUTHOR]

Cement asphalt mortar (CA) disengagement of a ballastless track will induce changes of dynamic response of a passing vehicle, which can accordingly be used to estimate the disengagement degree. In this paper, a novel method called CA mortar disengagement degree estimation algorithm (CMDEA) is proposed through an analysis of wheel acceleration of a passing vehicle. The disengagement degree estimation is transformed into an optimization problem by regarding the CA mortar disengagement degree as a parameter of a vehicle-track coupling model. An improved genetic algorithm with a shifting window is employed for the parameter optimization, which is split into a number of phases and whose initial values are given in terms of a priori probabilities. The accuracy and robustness of the estimation are discussed, and the results are compared with regular genetic algorithm. The simulation results show that CMDEA can estimate CA mortar degrees with an acceptable accuracy. Meanwhile, the proposed algorithm has the advantages of a lower error value and much shorter computation time. Moreover, the robustness of the algorithm under different vehicle speeds, track irregularities, and signal noise levels is also verified. [ABSTRACT FROM AUTHOR]

Software Defined Networking (SDN) is the novel networking paradigm where decoupling of the control plane from the data plane has its inherent advantages. Controller Placement Problem (CPP) involves placing the optimal number of controllers at the appropriate locations while meeting prerequisites such as latency, load balancing, energy and computational time. To achieve scalability, deployment of multiple controllers on large-scale SDN is one of the key challenges. CPP can be addressed as a multi-objective combinatorial optimization problem whose solution is a trade-off between multiple optimization parameters. In this paper, a novel population-based meta-heuristic algorithm viz. Naked Mole-Rat (NMR) Algorithm has been proposed to optimize the location for controller placement based on Switch-Controller (SC), Controller-Controller (CC) latency while maintaining load balancing among the controllers. The ideas and mechanisms are illustrated using two publicly available standard topologies viz. Ernet and Savvis. The controller localization approach implemented with NMR algorithm has slightly a better result as compared with the Bat algorithm. [ABSTRACT FROM AUTHOR]

Printed circuit board (PCB) orthogonal packing problems derive from the massive circuit board production process in the electronic industry. In the early stage, it mainly relied on human experiences to make decisions on how to make the most of each sheet while meeting customers' orders. Up till now, researchers in enterprises and academic circles are still trying to explore effective mathematical models and feasible optimization methods. In most cases, what PCB companies generally face is the orthogonal layout decision, which considers positioning each rectangle PCB piece (PCB-P) on a rectangle sheet board (panel) in an ideal way so that each panel has the minimal remains with the most PCB-Ps. In this paper, multi-round gradual packing methods (MGPMs) are proposed based on the idea of combinatorial optimization and dynamic programming. MGPMs include the depth priority-based method (MGPM-DP) and breadth priority-based branch-and-prune method (MGPM-BC). The former has the advantage of finding better solutions, while the latter has the advantage of consuming short computational time. Through extensive computational tests on real data from a PCB production enterprise in China, both MGPM-DP and MGPM-BC have achieved noticeable and satisfactory results compared with the simplex method, dynamic programming method, and a widely used industrial software (Yuanbo) in China. [ABSTRACT FROM AUTHOR]

In many cities and regions, decision makers independently develop Train Operation Plan (TOP) for each line in the rail transit network, resulting in a lack of TOP Synchronization (TOPS). Considering the entire network as a whole, researchers have realized that synchronous optimization is of great significance. In this paper, we formulate two Mixed-Integer Linear Programming (MILP) models to optimize demand-driven TOP in the network. The former is an Asynchronous TOP Optimization (ATOPO) model, while the latter is a Synchronous TOP Optimization (STOPO) model. The bi-objective models simultaneously determine train frequency, train timetable, and rolling stock circulation under small-granularity passenger demand to minimize trains' total cost and passengers' total time. Then, we propose the Nondominated Sorting Coevolutionary Memetic Algorithm (NSCMA) to solve the combinatorial optimization problems. The hybrid heuristic algorithm incorporates Coevolutionary Memetic Algorithm (CMA) into Advanced and Adaptive Nondominated Sorting Genetic Algorithm II (AANSGA-II) to ameliorate the evolution process for elite individuals. On this basis, we study the case of Shenyang Metro to verify the models and the algorithm. The results demonstrate that the STOPO model is better than the ATOPO model in reducing trains' total cost and passengers' total time. In addition, NSCMA is better than AANSGA-II in obtaining elite individuals. [ABSTRACT FROM AUTHOR]

A large number of modern communication technologies and sensing technologies are incorporated into the smart grid, which makes its structure unique. The centralized optimized dispatch method of traditional power grids is difficult to achieve effective dispatch of smart grids. Based on the analysis of power generation plan and maintenance plan optimization model, this paper establishes a smart grid power generation and maintenance collaborative optimization model with distributed renewable energy. The objective function of this collaborative optimization problem is the operating cost of conventional units, the cost of wind power generation, and the cost of overhauling units; the constraints considered mainly include system constraints and overhaul constraints. The solution method of combinatorial optimization is analyzed, and the genetic optimization algorithm adopted in this paper is selected and discussed. According to the characteristics of the system, various loads are modeled, and power supply constraints are considered. By establishing an effective objective function, the adjustable load scheduling problem is transformed into a solvable optimal control problem. Taking into account the uncertain factors in the system, the advantage of the real-time control system is that it can realize the dynamic update scheduling of the load, so it is more in line with the requirements of the actual system. The real-time algorithm proposed in the paper is based on a distributed control strategy, which can not only realize dynamic compensation for random fluctuations in renewable energy power generation but also satisfy the load curve optimization under the premise of making full use of power supply resources. In addition, simulation experiments compare the load dispatching capabilities of the proposed algorithm with the existing algorithms, thereby verifying the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Image media are used by people to perceive the world's material reality and spiritual symbols. Traditional folk art images, unlike natural scene images, are characterized by "form to write God." Their semantic data are more abstract and detailed. As a result, folk art images limit the use of low-level visual feature descriptors in natural images. By simulating the evolution of natural species, evolutionary computation solves optimization problems. Black box optimization, combinatorial optimization, nonconvex optimization, and multiobjective optimization are all examples of optimization techniques. The semantic model is built using the semantic dictionary, and the semantic expression between images is then determined using semantic measurement. As a result, this paper focuses on the "semantic gap" of evolutionary computing-based image search technology, as well as related feedback and image semantic analysis technology. The results show that this method uses the deep database's triple cross validation method to achieve average comfort and wakefulness, with accuracy of 63.62 percent and 72.38 percent, respectively, which is better than the methods in the comparative literature and can verify the algorithm's efficiency and feasibility. To summarize, using evolutionary computing technology to improve the performance of object classification and retrieval, traditional folk art image composition and semantic expression can effectively reduce quantization error and improve the resolution of image semantic expression. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) refers to the interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data. These devices can be controlled remotely, which makes them susceptible to exploitation or even takeover by an attacker. The lack of security features on many IoT devices makes them easy to access confidential information, issue commands from a distance, or even use the compromised device as part of a DDoS attack against another network. Feature selection is an important part of problem formulation in machine learning. To overcome the above problems, this paper proposes a novel feature selection framework RFS for IoT attack detection using machine learning (ML) techniques. The RFS is based on the concept of effective feature selection and consists of three main stages: feature selection, modeling, and attacks detection. For feature selection, three different models are proposed. Based on these approaches, three different algorithms are proposed. A set of 40 features was included in the model, derived from combinatorial optimization and statistical analysis methods. Our experimental study shows that the proposed frame work significantly improves over state-of-the-art cyberattacks techniques for time series data with outliers. [ABSTRACT FROM AUTHOR]

Under the social background of the rapid development of big data, human beings have gradually moved towards a comprehensive digital era, and people have begun to apply digital technology in various fields. Therefore, it is necessary to study methods that can efficiently process big data and solve problems such as data intelligence. Intelligent products and applications that we use everywhere in our lives are changing people's lives. Among them, location service is one of the important technical supports. No matter which software is opened on the mobile phone, it will prompt us to open location service. Location prediction is an important part of location-based services and plays an indispensable role in the recommendation system and urban resource planning. At present, GPS-based trajectory data is widely concerned in position prediction task. GPS trajectory data belongs to spatiotemporal series data, which not only contains time and position information but also contains abundant context information in trajectory sequence. Most of the traditional position prediction methods focus on the position sequence in GPS trajectory but do not fully mine the context information in the trajectory, which leads to poor prediction results. The intelligent optimization algorithm greatly enriches the optimization technology and provides a feasible solution for those combinatorial optimization problems which are difficult to deal with by traditional optimization technology. GPS trajectory data has the advantages of wide coverage, quick update, easy collection, and low cost, and it also implies abundant road network information. As a result, GPS trajectory data of users has gradually become a new data source for automatic construction of urban road network and has also become a research hotspot of many scholars. In this paper, the idea of swarm intelligence algorithm and swarm intelligence algorithm are used to design and implement a data analysis and behavior prediction system for calculating GPS user trajectory, aiming at solving the shortcomings of existing GPS tracking and positioning. [ABSTRACT FROM AUTHOR]

The Ant Colony Optimization (ACO) algorithms have been well-studied by the Operations Research community for solving combinatorial optimization problems. A handful of researchers in the Data Science community have successfully implemented various ACO methodologies for rule-based classification. This family of ACO algorithms is referred to as AntMiner algorithms. Due to the flexibility of the framework, and the availability of alternative strategies at the modular level, a systematic review on the AntMiner algorithms can benefit the broader community of researchers and practitioners interested in highly interpretable classification techniques. In this paper, we provided a comprehensive review of each module of the AntMiner algorithms. Our motivation is to provide insight into the current practices and future research scope in the context of the rule-based classification. Our discussions address ACO methodologies, rule construction strategies, candidate selection metrics, rule quality evaluation functions, rule pruning strategies, methods to address continuous attributes, parameter selection, and experimental settings. This review also reports a summary of real-life implementations of the rule-based classifiers in diverse domains including medical, genetics, portfolio analysis, geographic information system (GIS), human-machine interaction (HMI), autonomous driving, ICT, quality, and reliability engineering. These implementations demonstrate the potential application domains that can be benefitted from the methodological contributions to the rule-based classification technique. [ABSTRACT FROM AUTHOR]

SUMMARY How to decide the location and the capacity of distributed generators (DGs) in the distribution network is a complex and hard work. In this paper, several technical indexes involving energy loss, voltage quality and stability, and line loadability are considered to optimize DG capacity and location. It is an optimization problem wherein both discrete and continuous variables are involved, and cloud theory adapted genetic algorithm (CAGA) is employed to avoid search exploration and long time search. The weights of the indexes are decided by judgment matrix. DGs in a 28-node distributed network are employed to be optimized quickly to improve the indexes aforementioned to demonstrate the CAGA developed in this paper. Meanwhile, the optimization results are compared with traditional GA. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Coloring of fuzzy graphs has many real life applications in combinatorial optimization problems like traffic light system, exam scheduling, register allocation, etc. In this paper, the concept of fuzzy chromatic polynomial of fuzzy graph is introduced and defined based on α-cuts of fuzzy graph. Two different types of fuzziness to fuzzy graph are considered in the paper. The first type was fuzzy graph with crisp vertex set and fuzzy edge set and the second type was fuzzy graph with fuzzy vertex set and fuzzy edge set. Depending on this, the fuzzy chromatic polynomials for some fuzzy graphs are discussed. Some interesting remarks on fuzzy chromatic polynomial of fuzzy graphs have been derived. Further, some results related to the concept are proved. Lastly, fuzzy chromatic polynomials for complete fuzzy graphs and fuzzy cycles are studied and some results are obtained. [ABSTRACT FROM AUTHOR]

Many water-based optimization metaheuristics have been introduced during the last decade, both for combinatorial and for continuous optimization. Despite the strong similarities of these methods in terms of their underlying natural metaphors (most of them emulate, in some way or another, how drops collaboratively form paths down to the sea), in general the resulting algorithms are quite different in terms of their searching approach or their solution construction approach. For instance, each entity may represent a solution by itself or, alternatively, entities may construct solutions by modifying the landscape while moving. A researcher or practitioner could assume that the degree of similarity between two water-based metaheuristics heavily depends on the similarity of the natural water mechanics they emulate, but this is not the case. In order to bring some clarity to this mosaic of apparently related metaheuristics, in this paper we introduce them, explain their mechanics, and highlight their differences. [ABSTRACT FROM AUTHOR]

A seven-parameter BRDF model with double-peak characteristic was proposed in this paper, which can fit double-peak data. The global genetic algorithm was used to model the BRDF experimental data of sandy soil, and the parameter values and relative mean square error of the seven-parameter BRDF model were obtained. The results proved the correctness of the model, and the relative mean square errors of this model are, respectively, 0.30%, 0.22%, 0.26%, and 0.25% corresponding to the incident angles of 15°, 30°, 45°, and 60°. Additionally, we also combined data from four incident angles to derive seven parameters, which do not depend on incident angle, and the overall error is 1.79%. Finally, in order to intuitively show the BRDF of sandy soil, 3D BRDF graph of sandy soil with different incident angles is, respectively, given. It will be of great significance in practical project applications. [ABSTRACT FROM AUTHOR]

Logistics distribution is the terminal link that connects the manufacturer and product user and determines the efficiency of the manufacturer's service. Therefore, the disruption risk of the joint system is an essential factor affecting the product user experience. In this paper, while considering the product user's supply disruption risk preference (PUSDRP), a biobjective integer nonlinear programming (INLP) model with subjective cost-utility is proposed to solve the manufacturer's combined location routing inventory problem (CLRIP). According to the user's time satisfaction requirement, a routing change selection framework (RCSF) is designed based on the bounded rational behavior of the user. Additionally, the Lagrange Relaxation and Modified Genetic Algorithm (LR-MGA) is proposed. The LR method relaxes the model, and the MGA finds a compromise solution. The experimental results show that the biobjective cost-utility model proposed in this paper is effective and efficient. The RCSF based on user behavior is superior to the traditional expected utility theory model. The compromise solution provides a better solution for the manufacturer order allocation delivery combinatorial optimization problem. The compromise solution not only reduces the manufacturer's total operating cost but also improves the user's subjective utility. To improve the stability of cooperation between manufacturers and users, the behavior decision-making method urges manufacturers to consider product users' supply disruption risk preferences (PUSDRPs) in attempting to optimize economic benefits for the long term. This paper uses behavior decision-making methods to expand the ideas of the CLRIP joint system. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism. The algorithm does not need to give the number of clusters in advance. After the number of initial clusters and the center coordinates are given randomly, the optimal solution set is found by the multiobjective evolutionary algorithm. After determining the optimal number of clusters by majority vote method, the Jm value is continuously optimized through the combination of Canonical Genetic Algorithm and FCM, and finally the best clustering result is obtained. By using standard UCI dataset verification and comparing with existing single-objective and multiobjective clustering algorithms, the effectiveness of this method is proved. [ABSTRACT FROM AUTHOR]

During the construction of wireless sensor networks (WSNs) for smart cities, a preliminary survey of the relative criticalness within the monitored area can be performed. It is a challenge for deterministic sensor deployment to balance the tradeoff of sensing reliability and cost. In this paper, based on the sensing accuracy of the sensor, we establish a reliability model of the sensing area which is divided into sensing grids, and different weights are allocated to those grids. We employ a practical evaluation criterion using seesaw mapping for determining the weights of sensing grids. We further formulate and solve an optimization problem for maximizing the trust degree of the WSNs. With our proposed method, the efficient deployment of sensors can be realized. Simulation results show that our proposed deployment strategy can achieve higher trust degree with reduced sensor deployment cost and lower number of sensors at a certain miss probability threshold. [ABSTRACT FROM AUTHOR]

The industry wireless sensor network (IWSN) technology, which is used to monitor industrial equipment, has attracted more and more attention in recent years. Sensor nodes in IWSN can spontaneously complete distributed networking and carry out monitoring tasks under random deployment conditions. Therefore, a self-organized IWSN is particularly suitable for the fault detection and diagnosis of industrial equipment in complex environments. However, due to the detection, ability of a single sensor node is limited, and the monitoring distribution problem is a typical multidimensional discrete NP-hard combinatorial stochastic optimization problem, which is challenging to solve for the traditional mathematical methods. With the purpose of improving the target monitoring capability and prolonging lifetime of IWSN, a novel hybrid niche immune genetic algorithm (HNIGA) for optimizing the target coverage model of fault detection is proposed. It uses the genetic operation to evolve antibody groups and applies niche technology to maintain the diversity of antibody groups. As a result, HNIGA can effectively reduce the failure rate of detection targets. To verify the performance of HNIGA, a series of simulations under different simulation conditions are carried out. Specifically, HNIGA is compared with genetic algorithm (GA) and simulated annealing (SA). Simulation results show that HNIGA has a faster convergence speed and more robust global search capability than the other two algorithms. [ABSTRACT FROM AUTHOR]

Summary: The optimal configuration of the sensor system is extremely critical to effectively control the test cost and ensure the quality of the collected data for structural health monitoring (SHM) system. Different from the traditional sensor configuration criterion, the information entropy measure aims to minimize the uncertainty of structural model parameters to be identified, which can ensure that the collected data provides the maximum amount of information for the model identification. However, most of the current researches within the framework of entropy measure are aimed at the model identification, while studies that consider response prediction are still rare. In this paper, by constructing the posterior predictive distribution function of the frequency response functions (FRFs) at the unmeasured positions of structures and also the corresponding information entropy representation, the optimal sensor configuration problem for the purpose of structural response prediction at the unmeasured position is studied. A modified bound‐constrained Nelder–Mead simplex method to handle the binary encoded optimization variables is also proposed, which can effectively solve the combinatorial optimization problem in the optimal sensor configuration. The methodology proposed is validated by a forced vibration study conducted for a laboratory rectangular cantilever plate. The obtained results show that the prediction error and uncertainty of the higher‐order modes of the FRFs at the unmeasured positions is generally larger than that of the lower ones. Also, by comparing the performance of worst and optimal sensor layouts, the importance of the optimal configuration for response prediction at unmeasured locations is clearly revealed especially for a small number of sensors. [ABSTRACT FROM AUTHOR]

The Chinese postman problem is a classic resource allocation and scheduling problem, which has been widely used in practice. As a classical nondeterministic polynomial problem, finding its efficient algorithm has always been the research direction of scholars. In this paper, a new bioinspired algorithm is proposed to solve the Chinese postman problem based on molecular computation, which has the advantages of high computational efficiency, large storage capacity, and strong parallel computing ability. In the calculation, DNA chain is used to properly represent the vertex, edge, and corresponding weight, and then all possible path combinations are effectively generated through biochemical reactions. The feasible solution space is obtained by deleting the nonfeasible solution chains, and the optimal solution is solved by algorithm. Then the computational complexity and feasibility of the DNA algorithm are proved. By comparison, it is found that the computational complexity of the DNA algorithm is significantly better than that of previous algorithms. The correctness of the algorithm is verified by simulation experiments. With the maturity of biological operation technology, this algorithm has a broad application space in solving large-scale combinatorial optimization problems. [ABSTRACT FROM AUTHOR]

The set packing problem (SPP) is a significant NP-hard combinatorial optimization problem with extensive applications. In this paper, we encode the set packing problem as the maximum weighted independent set (MWIS) problem and solve the encoded problem with an efficient algorithm designed to the MWIS problem. We compare the independent set-based method with the state-of-the-art algorithms for the set packing problem on the 64 standard benchmark instances. The experimental results show that the independent set-based method is superior to the existing algorithms in terms of the quality of the solutions and running time obtained the solutions. [ABSTRACT FROM AUTHOR]

As a hot-spot of 5G, the research on detection algorithms for massive multiple input multiple output (MIMO) system is significant but difficult. The traditional MIMO detection algorithms or their improvements are not appropriate for large scaled antennas. In this paper, we propose artificial bee colony (ABC) detection algorithm for massive MIMO system. As one advanced technology of swarm intelligence, ABC algorithm is most efficient for large scaled constrained numerical combinatorial optimization problem. Therefore, we employ it to search the optimum solution vector in the modulation alphabet with linear detection result as initial. Simulation and data analysis prove the correctness and efficiency. Versus the scale of massive MIMO systems from 64 × 64 to 1024 × 1024 with uncoded four-quadrature-amplitude-modulation signals, the proposed ABC detection algorithm obtains bit error rate of 10 − 5 at low average received signal-to-noise-ratio of 12 dB with rapid convergence rate, which approximates the optimum bit error rate performance of the maximum likelihood and achieves the theoretical optimum spectral efficiency with low required average received signal-to-noise-ratio of 10 dB in similar increasing regularity, over finite time of low polynomial computational complexity of [ABSTRACT FROM AUTHOR]

In this paper, a rear-end collision control model is proposed using the fuzzy logic control scheme. Through detailed analysis of car-following cases, our fuzzy control system is established with reasonable control rules. Furthermore, a genetic algorithm is introduced into the fuzzy rules refining process to reduce the computational complexity while maintaining accuracy. Numerical results indicate that our genetic algorithm-optimized fuzzy logic controller outperforms the traditional fuzzy logic controller in terms of better safety guarantee and higher traffic efficiency. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Recently, learning scheduling problems have received increasing attention. However, the majority of the research assume that the actual job processing time is a function of its position. This paper deals with the single-machine scheduling problem with a sum-of-processing-time-based learning effect. By the effect of sum-of-processing-time-based learning, we mean that the processing time of a job is defined by total normal processing time of jobs in front of it in the sequence. We show that the single-machine makespan problem remains polynomially solvable under the proposed model. We show that the total completion time minimization problem for a ≥ 1 remains polynomially solvable under the proposed model. For the case of 0 < a < 1, we show that an optimal schedule of the total completion time minimization problem is V -shaped with respect to normal job processing times. [ABSTRACT FROM AUTHOR]

Many people use traditional methods such as quasi-Newton method and Gauss–Newton-based BFGS to solve nonlinear equations. In this paper, we present an improved particle swarm optimization algorithm to solve nonlinear equations. The novel algorithm introduces the historical and local optimum information of particles to update a particle's velocity. Five sets of typical nonlinear equations are employed to test the quality and reliability of the novel algorithm search comparing with the PSO algorithm. Numerical results show that the proposed method is effective for the given test problems. The new algorithm can be used as a new tool to solve nonlinear equations, continuous function optimization, etc., and the combinatorial optimization problem. The global convergence of the given method is established. [ABSTRACT FROM AUTHOR]

Network-wide maintenance lacks strong theoretical support and practical cases. However, research on this topic has entered an extensive exploratory stage; for example, new network design methods are being sought, and successful practices from traditional maintenance by line and by profession are being incorporated. This paper proposes a novel set-covering model with the variable coverage radius for the maintenance network of urban rail transit systems in the context of network-wide maintenance. The concepts of network-wide maintenance follow principles that are similar to those of bio-geography-based optimization (BBO), i.e., patterns of migration, variation, and extinction of different populations in different habitats. Therefore, a BBO algorithm is implemented with combinatorial optimization programming. Experiments from Changchun city in China show that the proposed model and algorithm are effective in fulfilling network-wide requirements through a direct tradeoff between the coverage radius and maintenance response time. In addition, the maintenance capacity and variable coverage radius of each maintenance point influence both the maintenance timeliness and resource utilization of maintenance units. [ABSTRACT FROM AUTHOR]

One of the major challenges in the coordination of large, open, collaborative, and commercial vehicle fleets is dynamic task allocation. Self-concerned individually rational vehicle drivers have both local and global objectives, which require coordination using some fair and efficient task allocation method. In this paper, we review the literature on scalable and dynamic task allocation focusing on deterministic and dynamic two-dimensional linear assignment problems. We focus on multiagent system representation of open vehicle fleets where dynamically appearing vehicles are represented by software agents that should be allocated to a set of dynamically appearing tasks. We give a comparison and critical analysis of recent research results focusing on centralized, distributed, and decentralized solution approaches. Moreover, we propose mathematical models for dynamic versions of the following assignment problems well known in combinatorial optimization: the assignment problem, bottleneck assignment problem, fair matching problem, dynamic minimum deviation assignment problem, Σ k -assignment problem, the semiassignment problem, the assignment problem with side constraints, and the assignment problem while recognizing agent qualification; all while considering the main aspect of open vehicle fleets: random arrival of tasks and vehicles (agents) that may become available after assisting previous tasks or by participating in the fleet at times based on individual interest. [ABSTRACT FROM AUTHOR]

Generalized assignment problem (GAP) is a well-known problem in the combinatorial optimization. This problem is a specific form of assignment problem (AP) when the employees can carry out more than one task simultaneously or each work can be assigned to more than one employee. In this paper, a new model is proposed for reassigning tasks on the available employees of Iraq companies when at least one of the jobholders is absent. Likewise, the returning workers from long holidays assumption are incorporated. Finally, a heuristic algorithm for solving reassignment tasks on laborers is introduced. [ABSTRACT FROM AUTHOR]

Parameter optimization of a hydrological model is intrinsically a high dimensional, nonlinear, multivariable, combinatorial optimization problem which involves a set of different objectives. Currently, the assessment of optimization results for the hydrological model is usually made through calculations and comparisons of objective function values of simulated and observed variables. Thus, the proper selection of objective functions' combination for model parameter optimization has an important impact on the hydrological forecasting. There exist various objective functions, and how to analyze and evaluate the objective function combinations for selecting the optimal parameters has not been studied in depth. Therefore, to select the proper objective function combination which can balance the trade-off among various design objectives and achieve the overall best benefit, a simple and convenient framework for the comparison of the influence of different objective function combinations on the optimization results is urgently needed. In this paper, various objective functions related to parameters optimization of hydrological models were collected from the literature and constructed to nine combinations. Then, a selection and evaluation framework of objective functions is proposed for hydrological model parameter optimization, in which a multiobjective artificial bee colony algorithm named RMOABC is employed to optimize the hydrological model and obtain the Pareto optimal solutions. The parameter optimization problem of the Xinanjiang hydrological model was taken as the application case for long-term runoff prediction in the Heihe River basin. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) based on the entropy theory is adapted to sort the Pareto optimal solutions to compare these combinations of objective functions and obtain the comprehensive optimal objective functions' combination. The experiments results demonstrate that the combination 2 of objective functions can provide more comprehensive and reliable dominant options (i.e., parameter sets) for practical hydrological forecasting in the study area. The entropy-based method has been proved that it is effective to analyze and evaluate the performance of different combinations of objective functions and can provide more comprehensive and impersonal decision support for hydrological forecasting. [ABSTRACT FROM AUTHOR]

This paper studies a flexible flowshop scheduling problem with step-deteriorating jobs and sequence-dependent setup times (FFSP-SDJ&SDST) where there are multiple unrelated parallel machines at each stage. The actual processing time of each job is modeled as a step function of its starting time. An integer programming model is first formulated with the objective of minimizing the total weighted completion time. Since this problem is NP-complete, it becomes an interesting and challenging topic to develop effective approximation algorithms for solving it. The artificial bee colony (ABC) algorithm has been successfully applied to solve both continuous and combinatorial optimization problems with the advantages of fewer control parameters and ease of implementation. So, an improved discrete artificial bee colony algorithm is proposed. In this algorithm, a dynamic generation mechanism of initial solutions is designed based on job permutation encoding. A genetic algorithm and a modified variable neighborhood search are introduced, respectively, to obtain new solutions for the employed and onlooker bees. A greedy heuristic is proposed to generate the solutions of the scout bees. Finally, to verify the performance of the proposed algorithm, an orthogonal test is performed to optimize the parameter settings. Simulation results on different scale problems demonstrate that the proposed algorithm is more effective compared against several presented algorithms from the existing literatures. [ABSTRACT FROM AUTHOR]

The use of effective central selection provides flexibility in design by offering modularity and extensibility. In earlier papers we have focused on the development of a simple centralized selection mechanism. Our current goal is to integrate evolutionary methods in the design of non-sequential behaviours and the tuning of specific parameters of the selection model. The foraging behaviour of an animal robot (animat) has been modelled in order to integrate the sensory information from the robot to perform selection that is nearly optimized by the use of genetic algorithms. In this paper we present how selection through optimization finally arranges the pattern of presented behaviours for the foraging task. Hence, the execution of specific parts in a behavioural pattern may be ruled out by the tuning of these parameters. Furthermore, the intensive use of colour segmentation from a colour camera for locating a cylinder sets a burden on the calculations carried out by the genetic algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we consider large-scale remote environmental monitoring (data gathering) through deploying an unreliable wireless sensor network in a remote region. The data monitoring center is geographically located far away from the region of the sensor network, which consists of sensors and gateways. Sensors are responsible for sensing and relaying data, and gateways are equipped with 3G/4G radios and can store the collected data from sensors temporarily and transmit the data to the remote data center through a third-party communication service. A service cost of using this service will be charged, which depends on not only the number of gateways employed but also the volume of data transmitted from each gateway within a given monitoring period. For this large-scale, remote, and unreliable data gathering, we first formulate a problem of maximizing network throughput with minimal service cost with an objective to maximize the amount of data collected by all gateways while minimizing the service cost. We then show that the problem is NP-complete and propose novel approximation algorithms. The key ingredients of the proposed algorithms include building load-balanced routing trees rooted at gateways and dynamically adjusting data load among the gateways. Finally, we conduct experiments by simulations to evaluate the performance of the proposed algorithms. Experimental results demonstrate that the proposed algorithms are very promising, and the obtained solutions are fractional of the optimum in terms of network throughput and the data service cost. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The many-to-many assignment problem (MMAP) is a recent topic of study in the field of combinatorial optimization. In this paper, a gradient-based interior-point method is proposed to solve MMAP. It is a deterministic method which assures an optimal solution. In this approach, the relaxation of the constraints is performed initially using the cardinality constraint detection operation. Then, the logarithmic barrier function (LBF) based gradient descent approach is executed to reach an accurate solution. Experiments have been performed to validate the practical implementation of the proposed algorithm. It also illustrates a significant improvement in convergence speed for the large MMAPs (i.e., if group size, α≥80) over state-of-the-art literature. [ABSTRACT FROM AUTHOR]

In this paper, a new probability mechanism based particle swarm optimization (PMPSO) algorithm is proposed to solve combinatorial optimization problems. Based on the idea of traditional PSO, the algorithm generates new particles based on the optimal particles in the population and the historical optimal particles in the individual changes. In our algorithm, new particles are generated by a specially designed probability selection mechanism. We adjust the probability of each child element in the new particle generation based on the difference between the best particles and the elements of each particle. To this end, we redefine the speed, position, and arithmetic symbols in the PMPSO algorithm. To test the performance of PMPSO, we used PMPSO to solve resource-constrained project scheduling problems. Experimental results validated the efficacy of the algorithm. [ABSTRACT FROM AUTHOR]

Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems. However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems. To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP). Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population. By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum. Secondly, a new RLP is defined and introduced to improve the migration operator. This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution. A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency. Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets. Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases. [ABSTRACT FROM AUTHOR]

The solution to a dynamic context of the Capacitated Vehicle Routing Problem (CVRP) is challenging. Routing and replenishment decisions are necessary by considering the assignment of customers to vehicles when the information is gradually revealed over horizon time. The procedure to solve this type of problems is referred to as route reoptimization, which is the best option for minimizing expected transportation cost without incurring failures of unsatisfied demand on a route. This paper proposes a heuristic algorithm for the reoptimization of CVRP in which the number of customers increases. The algorithm uses proposed performance metrics to reduce route dispersion and minimize length. The initial solution is generated using the savings algorithm and then enhanced using the Record-to-Record travel metaheuristic. By including or reducing new customers in the system, a reoptimization is performed which considers the visited nodes and edges as fixed. The optimization of the algorithm is implemented hierarchically by first minimizing dispersion and then minimizing distance. Next, the local search procedure is executed to improve the solution. A classic optimization is performed on all instances using the original and new customers’ information for later comparison to minimize distance. The efficiency of the proposed algorithm was validated using real-world cases from the literature. The results are promising and show the effectiveness of the proposed method for solving the considered problem by using reoptimization procedures in order to achieve good approximation ratios within short computing times. [ABSTRACT FROM AUTHOR]

Maximizing the network lifetime and data collection are two major functions in WSN. For this aim, mobility is proposed as a solution to improve the data collection process and promote energy efficiency. In this paper, we focus on Sink mobility which has the role of data collection. The problem is how to find an optimal data collection trajectory for the Mobile Sink using approximate optimization techniques. To address this challenge, we propose an optimization model for the Mobile Sink to improve the data collection process and thus to extend the network lifetime of WSN. Our proposition is based on a multiobjective function using a Weighted Sum Method (WSM) by adapting two metaheuristics methods, Tabu Search (TS) and Simulated Annealing (SA), to this problem. To test our proposal by experiment, we designed and developed an Integrated Environment of Optimization and Simulation based on metaheuristics tool (IEOSM). The environment IEOSM helps us to determine the best optimization method in terms of optimal trajectory, execution time, and quality of data collection. The IEOSM also integrates a powerful simulation tool to evaluate the methods in terms of energy consumption, data collection, and latency. [ABSTRACT FROM AUTHOR]

Train timetabling is crucial for passenger railway operation. Demand-oriented train timetable optimization by minimizing travel time plays an important role in both theory and practice. Most of the current researches of demand-oriented timetable models assume an idealized situation in which the service order is fixed and in which zero overtaking exists between trains. In order to extend the literature, this paper discusses the combinatorial effect of service order and overtaking by developing four mixed-integer quadratic programming timetabling models with different service order as well as overtaking conditions. With the objective of minimizing passengers’ waiting time and in-vehicle time, the models take five aspects as constraints, namely dwell time, running time, safety interval, overtaking, and capacity. All four models are solved by ILOG CPLEX; and the results, which are based on Shanghai-Hangzhou intercity high-speed rail data, show that either allowing overtaking or changing service order can effectively optimize the quality of timetable with respect to reducing the total passengers’ travel time. Although optimizing train overtaking and service order simultaneously can optimize the timetable more significantly, compared to overtaking, allowing the change of service order can help passengers save total travel time without extending the train travel time. Moreover, considering the computation effort, satisfying both of the conditions in the meantime, when optimizing timetable has not got a good cost benefit. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present the most common practices in multidisciplinary design optimization (MDO) of aerial vehicles over the past decade. The literature sample is identified through established internet search engines, and a stringent review methodology is implemented in order to ensure the selection of the most relevant sources. In this work, the primary emphasis is on the assessment of the state-of-the-art framework development strategies, while at a secondary level, the objective is to identify the possible improvement directions by evaluating the research trends and gaps. As an additional contribution, statistical studies are also provided, and it is shown how MDO of aerial vehicles has evolved in terms of problem formulation, disciplinary modeling, analysis capabilities, tool implementation, and general applicability. Given this foundation as well as the results of the review, this work concludes by presenting a roadmap for guiding academia and industry in respect to the application of MDO on aerial vehicles. Overall, the roadmap together with the literature review is not only expected to serve as a guide for newcomers into the MDO field but also as an elementary basis which will allow researchers to conduct additional studies in this important and constantly evolving area of design. [ABSTRACT FROM AUTHOR]

Inspired by the basic theory of Fruit Fly Optimization Algorithm, in this paper, cat mapping was added to the original algorithm, and the individual distribution and evolution mechanism of fruit fly population were improved in order to increase the search speed and accuracy. The flowchart of the improved algorithm was drawn to show its procedure. Using classical test functions, simulation optimization results show that the improved algorithm has faster and more reliable optimization ability. The algorithm was then combined with sparse decomposition theory and used in processing fouling detection ultrasonic signals to verify the validity and practicability of the improved algorithm. [ABSTRACT FROM AUTHOR]

Large-scale wireless sensor networks consist of a large number of tiny sensors that have sensing, computation, wireless communication, and free-infrastructure abilities. The low-energy clustering scheme is usually designed for large-scale wireless sensor networks to improve the communication energy efficiency. However, the low-energy clustering problem can be formulated as a nonlinear mixed integer combinatorial optimization problem. In this paper, we propose a low-energy clustering approach based on improved niche chaotic genetic algorithm (INCGA) for minimizing the communication energy consumption. We formulate our objective function to minimize the communication energy consumption under multiple constraints. Although suboptimal for LSWSN systems, simulation results show that the proposed INCGA algorithm allows to reduce the communication energy consumption with lower complexity compared to the QEA (quantum evolutionary algorithm) and PSO (particle swarm optimization) approaches. [ABSTRACT FROM AUTHOR]

The most important operating problem for intercity rail lines, which are characterized with the train operations at rapid speed and high frequency, is to design a service-oriented schedule with the minimum cost. This paper proposes a phase-regular scheduling method which divides a day equally into several time blocks and applies a regular train-departing interval and the same train length for each period under the period-dependent demand conditions. A nonlinear mixed zero-one programming model, which could accurately calculate the passenger waiting time and the in-train crowded cost, is developed in this study. A hybrid genetic algorithm associated with the layered crossover and mutation operation is carefully designed to solve the proposed model. Finally, the effectiveness of the proposed model and algorithm is illustrated through the application to Hefei-Wuhan intercity rail line in China. [ABSTRACT FROM AUTHOR]