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Abstract: The continuation education is very important for people who have left school to work to increase their competence and skills. To avoid the disadvantages of the common test paper generating methods, genetic algorithm is used to generate the test paper automatically. The concrete design process of test paper generating based on genetic algorithm is discussed in this paper, and some corresponding parameters setting have been compared and defined. The application results demonstrated that the genetic algorithm was an effective tool in the test paper generating. [Copyright &y& Elsevier]

This paper discusses a particular situation in project management in which an analyst attempts to prioritize several independent activities to handle all of them one by one in such a way that there would be no precedence relationships over the activities. The novelty of this research is that the structure of prioritized activities is linear in arrangement which can be considered as a combinatorial optimization problem. The paper formulates a mathematical model and applies it to two real cases in the oil and gas industry. In addition, a row generation procedure is developed to solve largescale problems and the computational results for the problem instances of size up to 300 activities are reported. The results demonstrate the applicability and efficiency of the proposed methodology. [ABSTRACT FROM AUTHOR]

The global energy industry engages in exploration, drilling, production, and abandonment activities in pursuit of crude oil and natural gas. This paper provides a methodology to optimize the location and assignment of oil spill response containment systems to limit environmental damage. Utilizing 1,802 active offshore sites and 13 eligible service depots in the Gulf of Mexico's offshore energy basin, this study locates and assigns deployable land-based containment systems while evaluating the progressive enhancement of including additional service depots. The model solves the practical problem of locating and allocating oil spill containment systems to a global optimum. Adding service depots reduces the average distance, or travel time, of response vessels, which minimizes the dispersion of the oil spill. This study represents a critical component of a broader emergency oil spill response plan. Specifically, it addresses the preparation phase of a comprehensive oil spill response plan, which is preceded by the mitigation phase and followed by the response and recovery phases. [ABSTRACT FROM AUTHOR]

Vehicle routing problem is a NP-hard problem and a combinatorial optimization problem; it appeared first time in 1959 in the paper of the mathematician Dantzig. The goal of VRP is to locate the optimal routes of some vehicles that begin from a depot and serve each customer one time and then return to the depot (i.e., Starting point). From its beginning, the research literature in this area is growing rapidly and causing the extension of VRP to many variants for making it a real-world problem. For solving it, the researchers have tried firstly the exact methods then the heuristics and lastly the metaheuristics. This paper aims for many targets for instance: (i) discovering the evolution of VRP and its variants over the last decade; (ii) knowing the trends, challenges and opportunities in the next years in this fields by discovering, comparing many recent reviews and papers related either to VRP or to metaheuristics for exploiting these results and building on them in other papers. [ABSTRACT FROM AUTHOR]

The Graph Coloring Problem is a well-known NP-hard problem. Over the years, numerous scholars have been pursuing efficient algorithms to obtain high-quality solutions. Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo Tree Search algorithm for single-player games, and it has been proven effective and good in combinatorial optimization problems. In this paper, we use for the first time the NRPA algorithm combined with the destruction and reconstruction ideas of the Iterative Greedy algorithm to solve the Graph Coloring Problem. First, the basic principle of the NRPA algorithm is introduced. Then, NRPA is extended by using mixed sorting, destruction and reconstruction, and the Diversity-NRPA algorithm is proposed, which improves the diversity of the algorithm. Finally, Diversity-NRPA is applied to solve the Graph Coloring Problem by combining it with the knowledge of the graph theory field. We evaluate the performance of Diversity-NRPA on DIMACS, a well-known graph benchmark instance, and compare it with traditional graph coloring algorithms. The experimental results show that the Diversity-NRPA algorithm can achieve excellent performance in both solution quality and search efficiency in solving the Graph Coloring problem. [ABSTRACT FROM AUTHOR]

The 0–1 Knapsack problem is a non-deterministic polynomial-time-hard combinatorial optimization problem that cannot be solved in reasonable time using traditional methods. Therefore, researchers have turned to metaheuristic algorithms for their ability to solve several combinatorial problems in a reasonable amount of time. This paper adapts the Kepler optimization algorithm using eight V-shaped and S-shaped transfer functions to create a binary variant called BKOA for solving the 0–1 Knapsack problem. Several experiments were conducted to compare the efficacy of the binary Kepler optimization algorithm to several competing optimizers when solving 20 well-known knapsack instances with dimensions ranging from 4 to 75. The experimental results demonstrate the superiority of this algorithm over other metaheuristic algorithms, except for the genetic algorithm, which is marginally superior. To further improve the binary Kepler optimization algorithm, it is combined with an enhanced improvement strategy to create a new hybrid variant. This hybrid variant, termed HBKOA, has superior exploration and exploitation capabilities that make it better than genetic algorithm and other optimizers for all performance metrics considered. The enhanced improvement strategy is also integrated with several competing optimizers, and the experimental results show that HBKOA, hybrid binary manta ray foraging optimization, and hybrid binary equilibrium optimizer are competitive for small and medium-dimensional instances and superior for the higher dimensions. [ABSTRACT FROM AUTHOR]

In this paper, an electromagnetism-like approach (EM) for solving the two-level hierarchical covering location problem (TLHCLP) is proposed. An EM metaheuristic is a powerful algorithm for global optimization that converges rapidly to the optimum. Therefore, it has the potential to solve this type of problem since movement based on the attraction-repulsion mechanisms, combined with the proposed scaling technique, directs EM to promising search regions. The fast implementation of the objective function and local search procedure for TLHCLP additionally improves the efficiency of the overall EM system. The proposed EM approach reaches all optimal solutions in a relatively short amount of computational time. EM also obtains high-quality solutions for large-scale problem instances that are out of reach for exact methods. [ABSTRACT FROM AUTHOR]

Based on uncertainty theory, this paper proposed a vehicle scheduling method considering the dynamic departure interval and vehicle configuration of electric buses (EBs). An uncertain bi-level programming model (UBPM) is established, which takes the total cost of passenger travel (CP) as the upper and total cost of EBs (CB) as the lower. A chance constrained programming model (CCPM) based on the randomness of passenger waiting time and the uncertainty of interstation running time is proposed as the upper model. With a certain confidence level of service level as constraints, the goal is to minimize the total cost of passenger travel. Then, an expected value model (EVM) based on the fluctuation of energy consumption is proposed as the lower model. Taking the number of EBs as the constraint condition, the goal was to minimize the energy consumption of EBs. Finally, a practical bus route is taken as an example to verify the effectiveness of the proposed method. The results demonstrated that the optimal scheduling plan considering the uncertain variables can reduce the passenger travel cost. Collaborative optimization of EBs vehicle configuration can reduce energy consumption, delay, and the number of EBs. [ABSTRACT FROM AUTHOR]

The optimization of train formation plan is a large-scale combinatorial optimization problem, which is difficult to solve. This paper mainly studies the optimization algorithm of the single-block train formation plan. The corresponding mathematical model is established by consulting relevant literature, and a positive feedback search algorithm based on absolute conditions is proposed. First of all, the wagon flow that meets the absolute conditions directly runs through train flow without making other choices. For the wagon flow that does not meet the absolute conditions, select the target station to reach directly according to the probability. The probability of wagon flow selecting a station is calculated according to the pheromone of the wagon flow at the station. At the same time, a pheromone update strategy with positive feedback mechanism is proposed to make the search process converge. Finally, the feasibility of the algorithm and the necessity of introducing absolute conditions into the algorithm are verified by taking eight technical stations in the linear direction of the road network as examples. [ABSTRACT FROM AUTHOR]

Scheduling is one critical issue both in the field of industry engineering and combinatorial optimization research. In order to solve multi-objective scheduling problem with uncertainty, this paper presents a method of enhanced hybrid Estimation of Distribution Algorithm (EDA) with Teaching and Learning-Based Optimization Algorithm (TLBO). First, in order to concentrate their respective advantages, two algorithms of EDA and TLBO are integrated to enhance the capability of both global and local search. Second, scenario-based simulation is adopted to deal with uncertainty, and an adaptive sampling strategy is involved to dynamically adjust the number of scenarios during the evolving process. Third, a problem-specific local search is designed to further improve the optimality of candidate solutions. By comparing with existing algorithms on the benchmark problems of flexible job shop scheduling problem (FJSP), it is to demonstrate that our proposal can obtain better solutions in the aspects of optimality and computational efficiency. [ABSTRACT FROM AUTHOR]

Elevator systems in buildings face challenges due to unpredictable passenger flow, which can make scheduling elevators complicated to optimize their operation. Most of the existing algorithms are developed based on pattern recognition and may not be effective in scenarios where patterns are difficult to classify, especially when elevator operations involve uncertain human behaviors. To address this issue, this paper proposes a time-dependent optimization model that considers long-term and multi-floor peaks, with a focus on peak floors. The proposed model accounts for dynamic scheduling patterns of elevators and represents passenger flow direction as a relation matrix. The proposed direction optimization method contains several functions to guide iteration direction and improve the efficiency of an iteration process based on classical algorithms. This method also ensures the stability of Markov chains by adjusting an iteration process. The feasibility of the proposed method is supported by the relevant theory, and experimental results show that the direction-optimized algorithms outperform classical algorithms, resulting in the superb operating efficiency of elevators at a lower cost. This paper contributes to the development of efficient algorithms for scheduling elevators in complex traffic patterns, which can improve performance of elevator group systems in buildings. The proposed method is not only limited to elevators but can also be extended to other transportation systems with flow requirements. • Estimating the relation between peak floors without pattern recognition. • Four relations are defined to classify the passenger flow. • Dynamically modifying three metaheuristic algorithms' iteration law. • The application for transportation systems with unpredictable flow. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Graph Partitioning Problem (GPP) is a classical combinatorial optimization problem that has been extensively researched. In recent years, many methods for solving the GPP have been proposed, which can be divided into direct partitioning approaches and iterative improvement approaches. Direct partitioning approaches directly give a complete partition of the input graph. Iterative improvement approaches require further adjustment of feasible solutions based on the result of direct partitioning approaches in order to obtain a better performance. In this paper, we summarize the recent trends in algorithms and applications for GPP. In addition, we propose a graph partitioning algorithm based on semi-supervised learning in combination with graph filtering methods. [ABSTRACT FROM AUTHOR]

This paper addresses the Multiobjective Travelling Salesman Problem (MoTSP) with the aim to study the effects of including random immigrants in the population of solutions processed by the evolutionary algorithm. Random immigrants are typically used in evolutionary optimization in order to increase the diversity of the population and to allow the algorithm to explore a larger area of the search space. Introducing random immigrants incurs a certain overhead which is especially significant in combinatorial optimization, because local search procedures are usually employed, which, while effective in improving the solutions, are computationally expensive. In this paper several strategies of introducing new specimens are tested with the aim of improving the effectiveness of the optimization process given a limited computation time. In the experiments the proposed approach was tested on kroABnnn instances of the MoTSP. It was found to improve the results of multiobjective optimization in terms of both the hy-pervolume and the IGD indicators. The most effective immigration strategy turned out to be to decrease the number of immigrants with time. [ABSTRACT FROM AUTHOR]

Resource-constrained project scheduling problem (RCPSP) is a kind of representative practical engineering problem, the purpose is to schedule activities in the project through rational use of limited resources in the minimum time. This paper proposes an improved hybrid cuckoo algorithm (CS&SA) to solve the RCPSP problem. Firstly, the individual elements are randomly coded into priority vectors, and the population is decoded using serial scheduling to convert the individual into a set of task scheduling sequences. Secondly, the Levy flight is redesigned to change the algorithm from random walk to adaptive as the population fitness changes. Then, this article adds three neighborhood update techniques to meet the update requirements of the algorithm at different stages. Finally, in order to prevent the algorithm from falling into a local optimum, this paper introduces a simulated annealing strategy to allow the algorithm to accept some individuals with poor quality with a certain probability in each iteration. In the testing part, this paper firstly tests the effectiveness and optimization of three different-scale examples in the classic example library PSPLIB for RCPSP problems. Among them, the average error of the CS&SA algorithm in the small-scale J30 is 0.25%, and the average error in the medium-scale J60 is 11.21%, and the average error of the large-scale J120 is 19.83%. Then, by comparing other intelligent optimization algorithms, it is proved that NCS&SA is superior to other algorithms in optimization and accuracy. [ABSTRACT FROM AUTHOR]

For the group travel, reasonable vehicle scheduling needs to consider constraints such as traffic distance and route conflict, which is common in many vehicle transportation scheduling systems, and it is crucial for operators to improve operational efficiency and provide a higher quality service experience. In this paper, a scenic spot vehicle scheduling problem is designed based the real-world scenario. In order to improve the search efficiency and maintain the diversity of solutions , an intelligent scheduling algorithm based on improved migrating birds optimization(gMBO) is proposed. The gMBO applies a neighborhood structure based on disjunctive graph to accelerate the solution search in the touring phase by avoiding redundancy. Besides, leveraging the left–right sequential queue characteristics of the MBO algorithm, gMBO utilizes two mechanisms to enhance the interaction between queues in the leader replacement stage, which can expand the search space of solutions and at the same time to maintain population diversity. Finally, we consider using the POX crossover operator in the individual, it is well adapted to the characteristics of the problem can reduce the generation of unreasonable solutions. The computational results show that the neighborhood structure is feasible. Considering the problem of scenic spot vehicle scheduling in practical urban applications, the multivariate migrating birds optimization algorithm based on disjunctive graph neighborhood is more effective than the MILP and other three meta-heuristic algorithms, and the optimal solution is obtained under the same stopping criteria, with an average RPD of 2.16. It has the advantages of fast convergence and good robustness. • An improved migrating birds optimization algorithm and MILP algorithm are proposed for scenic spot vehicles scheduling. • We summarized the theoretical results required for several problems. • Proposed a neighborhood based on graph structure to avoid redundancy. • We add new mechanisms for the Migrating Birds Optimization algorithm to improve efficiency. • We compare the proposal with other advanced meta-heuristics. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recently, there has been a surge in the use of metaheuristic algorithms to design materials with optimum performance. In this paper, the RPSOLC (Repulsive Particle Swarm Optimization with Local search and Chaotic perturbation) metaheuristic algorithm was used to design Diamond-Like Carbon (DLC) thin films with improved hardness. Based on the Box-Behnken design, 15 independent DLC deposition experiments are performed in a PECVD (Plasma-Enhanced Chemical Vapor Deposition) setup by varying the CH4-Argon ow rate, hydrogen ow rate, and deposition temperature. The nano-hardness of the DLCs is evaluated using nano-indention tests. The hardness is then expressed as the function of the three process parameters using a polynomial regression metamodel. Finally, using RPSOLC, the metamodel is optimized and compared to the optimal predictions of a traditional Genetic Algorithm (GA). It is seen that RPSOLC has faster convergence and is more reliable than the GA. In general, a high H2 ow rate along with a low CH4-Ar ow rate and high temperature is found to be beneficial in improving the hardness. [ABSTRACT FROM AUTHOR]

Emerging Unmanned Aerial Vehicle (UAV) application patterns, including the Loyal Wingman and Unmanned Swarms, have significantly challenged the administration and defense against illegal or trespassing UAVs. The weapon-target assignment (WTA) problem, a famous combinatorial optimization problem in military operations research, is decisive for the success of UAV defense programming. Related investigations face two main challenges: 1) Constructing a multi-objective optimization model reflecting the complexities of new UAV application patterns, and 2) solving the time-consuming WTA problem with high-dimension decision variables as it is NP-complete. This paper introduces a modified differential evolution based on a new mapping method called Pareto-Optimal-Matching that solves multi-objective binary optimization problems and constructs a 3 objective Sensor-Weapon-Target Assignment model. The proposed algorithm overcomes the inherent limitation of traditional differential evolution, which only performs well in continuous problems. Promising results were obtained based on different simulation metrics under various problems (proposed model, MOKP, and ZDT5), validating the superior performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• A distribution network integrating urban waterways and last-mile delivery, exemplified by Amsterdam. • A novel two-echelon location routing model and an efficient hybrid solution approach. • Results demonstrating substantial cost savings and enhanced infrastructure lifetime. • A significant emission reduction, highlighting the potential for greener urban logistics. • Extending beyond Amsterdam, with guidance for cities to promote sustainable urban logistics. This paper tackles the growing challenges in urban logistics by presenting an optimal distribution network that integrates urban waterways and last-mile delivery, tailored for cities boasting extensive waterway networks. We examine Amsterdam's city center as a case study, prompted by the strain on quay walls, congestion, and emissions, urging a reevaluation of its urban logistics design. We formulate the problem as a two-echelon location routing problem with time windows and introduce a hybrid solution approach for effective resolution. Our algorithm consistently outperforms existing methods, with a superior solution quality, demonstrating its effectiveness across established and newly developed benchmark instances. In our case study, we evaluate the benefits of transitioning from a roadway-centric to a waterway-based system, showcasing significant cost savings (approximately 28 %), reductions in vehicle weight (approximately 43 %), and minimized travel distances (approximately 80 %) within the city center. The integration of electric vehicles enhances environmental sustainability, resulting in a total daily emission reduction of 43.46 kg. Our study underscores the untapped potential of inland waterways in easing urban logistics challenges. Inspired by Amsterdam's experience, global cities can adopt innovative approaches for sustainable logistics, providing valuable insights for managers striving to enhance efficiency, cut costs, and promote sustainable transportation practices. [ABSTRACT FROM AUTHOR]

The job-shop scheduling problem (JSP) is a well-known combinatorial optimization problem in manufacturing systems. For the past two decades, real-number metaheuristics have been widely used to solve the JSP using the real-number transform methods. A limitation of the real-number metaheuristics is the premature convergence due to the stochasticity of the transform methods. To eliminate this limitation, a novel operator framework has been designed, building the bridge between the discrete optimization problem (JSP) and real-number metaheuristics (also called continuous metaheuristics). Specifically, this paper captures the core operators of the real-number metaheuristics, namely, addition, subtraction, and multiplication. Firstly, three new operators are reconstructed according to several simple neighborhood structures to solve the JSP robustly and effectively. The properties of the arithmetic (symmetry) are taken into account in the reconstruction of the proposed operators to avoid excessive redundant searches. Secondly, a positional similarity-based population diversity is presented for the JSP to demonstrate the intrinsic distinctions between the proposed operators and the transform methods during the evolutionary process. Finally, the results of five widely used benchmark test suites (185 instances) show that the proposed operators can achieve a better balance between exploration and exploitation than the current real-number transform methods. • The operator framework is proposed for the real-number metaheuristics. • The relationship between a discrete optimization problem (JSP) and the continuous metaheuristics is established. • The diversity of the JSP is defined based on the similarity of the sequence. • The performance of the proposed framework is verified on 185 instances. [ABSTRACT FROM AUTHOR]

In this paper we pursue the study of pseudo-Boolean functions as ranking generators. The objective of the work is to find new insights between the relation of the degree m of a pseudo-Boolean function and the rankings that can be generated by these insights. Based on a characterization theorem for pseudo-Boolean functions of degree m , several observations are made. First, we verify that pseudo-Boolean functions of degree m < n , where n is the search space dimension, cannot generate all the possible rankings of the solutions. Secondly, the sufficient condition for a ranking to be generated by a pseudo-Boolean function of dimension (n − 1) is presented, and also the necessary condition is conjectured. Finally, we observe that the same argument is not sufficient to prove which ranking can be generated by pseudo-Boolean functions of degree m < n − 1. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a new variant of the multi-commodity network flow problem, taking into consideration multiple transport lines and time windows. This variant arises in a city logistics environment, more specifically in a long-haul passenger transport system that is also used to transport urban freight. We propose two mixed integer programming models for two objective functions: minimization of network operational costs and minimization of travel times. Since the problems under study are NP-hard, we propose three size reduction heuristics. In order to assess the performance of the proposed algorithms, we carried out computational experiments on a set of synthetic problem instances. We use the relative percentage deviation as performance criterion. For the cost objective function, a LP-and-Fix algorithm outperforms other methods in most tested instances, but for the travel time, a hybrid method (size reduction with LP-and-Fix algorithm) is, in general, better than other approaches. [ABSTRACT FROM AUTHOR]

Traveling Salesman Problem(TSP) is a main attention issue at present. Neural network can be used to solve combinatorial optimization problems. In recent years, there have existed many neural network methods for solving TSP, which has made a big step forward for solving combinatorial optimization problems. This paper reviews the neural network methods for solving TSP in recent years, including Hopfield neural network, graph neural network and neural network with reinforcement learning. Using neural network to solve TSP can effectively improve the accuracy of the approximate solution. Finally, we put forward the prospect of solving TSP in the future. [ABSTRACT FROM AUTHOR]

Recently, combinatorial optimization problems have aroused a great deal of interest in Machine Learning, leading to interesting advances in Neural Combinatorial Optimization (NCO): the study of data-driven solvers for NP-Hard problems based on neural networks. This paper studies the benefit of Transfer Learning for NCO by evaluating how model training can be improved taking advantage of knowledge learned while solving similar tasks. We focus, in particular, on two famous routing problems: the Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP). The latter being a generalization of the former, we study the effect of applying Transfer Learning from a model trained to solve TSP while training a model learning to solve the Capacitated VRP (CVRP). We present adaptations of a state-of-the-art NCO model for implementing Transfer Learning. Our results based on extensive empirical experiments in different settings show that Transfer Learning may help to speed up the training process while being more sample efficient. [ABSTRACT FROM AUTHOR]

Abstract A shortcut search between different objects is one of the classic optimization goals. Simulated Annealing algorithms are often used for optimization purposes. The Simulated Annealing method is applied in combinatorial optimization tasks. Simulated Annealing is a stochastic optimization method that can be used to minimize the specified cost function given a combinatorial system with multiple degrees of freedom. The application of Simulated Annealing method and Travelling Salesman Problem is demonstrated in this paper and an experiment aimed to find the shortest route between Belarusian processing enterprises is performed. The paper also provides mapping of these objects using GPS location. The main goal of the paper is to show the possibilities of applying mathematical models in solving practical tasks - to determine the shortest route between the different objects. [ABSTRACT FROM AUTHOR]

Abstract Two pattern recognition technologies in the field of machine learning, clustering and classification, have been applied in many domains. Density-based clustering is an essential clustering algorithm. The best known density-based clustering method is Density-Based Spatial Clustering of Applications with Noise (DBSCAN), which can find arbitrary shaped clusters in datasets. DBSCAN has three drawbacks: firstly, the parameters for DBSCAN are hard to set; secondly, the number of clusters cannot be controlled by the users; and thirdly, DBSCAN cannot directly be used as a classifier. In this paper a novel Particle swarm Optimized Density-based Clustering and Classification (PODCC) is proposed, designed to offset the drawbacks of DBSCAN. Particle Swarm Optimization (PSO), a widely used Evolutionary and Swarm Algorithm (ESA), has been applied in optimization problems in different research domains including data analytics. In PODCC, a variant of PSO, SPSO-2011, is used to search the parameter space so as to identify the best parameters for density-based clustering and classification. PODCC can function in terms of both Supervised and Unsupervised Learnings by applying the appropriate fitness functions proposed in this paper. With the proposed fitness function, users can set the number of clusters as input for PODCC. The proposed method was evaluated by testing ten synthetic datasets and ten benchmarking datasets selected from various open sources. The experimental results indicate that the proposed PODCC can perform better than some established methods, especially with respect to imbalanced datasets. [ABSTRACT FROM AUTHOR]

Cloud Computing can be defined as enabling computing resources to whoever want remotely upon demand. This necessarily requires using virtualization. A virtual machine (VM) is an emulation of a computer that runs in virtualization software. Tasks coming from different users are passed to the virtual machines to be processed. This paper studies the load balancing problem among various VMs; to ensure that the network resources are distributed in a fair way between various clients. This paper proposes solving the load balancing problem using a combinatorial optimization approach named the Hungarian method. The proposed protocol is named LBCC-Hung (Load Balancing Protocol for Cloud Computing Based on Hungarian Method). Using simulation, the performance of LBCC-Hung is measured and compared with two well-known methods; MIN-MIN and First Come First Serve FCFS methods. The simulation results proved that LBCC-Hung overperforms the others two protocols, in terms of both the Makespan and the throughput and the virtual machine utilization deviation. [ABSTRACT FROM AUTHOR]

In automated container terminals, effectively scheduling quay cranes (QCs), automated guided vehicles (AGVs), automated stacking cranes (ASCs) and AGV routing are two important problems. This paper studies the combinatorial optimization of two problems in the synchronous loading and unloading operation mode of the automated container terminal. First, a bi-level programming model for integrated scheduling problems is established with the goal of minimizing the completion time of all containers. Secondly, in order to solve the model, we design two bi-level optimization algorithms based on conflict resolution strategy: bi-level adaptive genetic algorithm based on conflict resolution strategy (CRS-BAGA) and bi-level genetic algorithm based on conflict resolution strategy (CRS-BGA). Finally, the effectiveness of the two optimization algorithms is verified by numerical experiments. Experimental results show that the proposed optimization algorithms are effective. They can coordinate the scheduling of three equipments and solve the conflict congestion problem of AGV. [ABSTRACT FROM AUTHOR]

In this paper we consider the combinatorial optimization problem known as workflow scheduling. We compare three encoding schemes of varying density: one-hot, binary, and domain wall, and test their performance against two wellknown hybrid quantum-classical algorithms: Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE). In an attempt to obtain the best results possible, we investigate various parameters of the algorithms and test out other state-of-the-art improvements, such as dedicated QAOA mixers. Ultimately, we prove that, despite its popularity, one-hot encoding is not always the best, and using a denser encoding scheme, such as binary or domain wall, can allow for solving larger instances of workflow scheduling. Additionally, combining the above-mentioned encodings with dedicated QAOA mixers reduces the number of infeasible solutions, leading to better results. [ABSTRACT FROM AUTHOR]

This paper presents a new extractive Arabic text summarization approach based on the Knapsack balancing of effective retention. Effective retention refers to maximizing retention while prioritizing salient themes. First, text segments are mapped to salient topics, and an effective retention score is computed for every sentence. Next, the summarization task is formulated as a combinatory optimization problem. The final output is generated through the maximization of effective retention. Aka selected sentences to be part of the summary are the best ones to encode salient ideas while covering most central themes. Experimental results show that the proposed approach outperforms three state-of-the-art Arabic summarization protocols. [ABSTRACT FROM AUTHOR]

We investigate the channel allocation problem in an ultra-dense device-to-device (D2D) enabled cellular network in underlaying mode where multiple D2D users are forced to share the same channel. Two kinds of low complexity solutions, which just require partial channel state information (CSI) exchange, are devised to resolve the combinatorial optimization problem with the quality of service (QoS) guaranteeing. We begin by sorting the cellular users equipment (CUEs) links in sequence in a matric of interference tolerance for ensuring the SINR requirement. Moreover, the interference quota of CUEs is regarded as one kind of communication resource. Multiple D2D candidates compete for the interference quota to establish spectrum sharing links. Then base station calculates the occupation of interference quota by D2D users with partial CSI such as the interference channel gain of D2D users and the channel gain of D2D themselves, and carries out the channel allocation by setting different access priorities distribution. In this paper, we proposed two novel fast matching algorithms utilize partial information rather than global CSI exchanging, which reduce the computation complexity. Numerical results reveal that, our proposed algorithms achieve outstanding performance than the contrast algorithms including Hungarian algorithm in terms of throughput, fairness and access rate. Specifically, the performance of our proposed channel allocation algorithm is more superior in ultra-dense D2D scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The problem of finding the maximum number of d-vertices cliques (d = 3) in d-partite graph (d = 3) when graph density q is lower than 1 is an important problem in combinatorial optimization and it is one of many NP-complete problems. For this problem a meta-heuristic algorithm has been developed, namely an ant colony optimization algorithm. In this paper a new development of this ant algorithm and experimental results are presented. The problem of finding the maximum number of 3-vertices cliques can be encountered in computer image analysis, computer vision applications, automation and robotic vision systems. The optimal solution of this problem boils down to finding a set of 3-vertices cliques in a 3-partite graph and this set should have cardinality as high as possible. The elaborated ant colony algorithm can be easily modified for d-dimensional problems, that is for finding the maximum number of d-vertices cliques in a d-partite graph. [ABSTRACT FROM AUTHOR]

Vehicle routing problem with time windows (VRPTW), which is a typical NP-hard combinatorial optimization problem, plays an important role in modern logistics and transportation systems. Recent years, heuristic and meta-heuristic algorithms have attracted many researchers' attentions to solve the VRPTW problems. As an outstanding meta-heuristic algorithm, particle swarm optimization (PSO) algorithm exhibits very promising performance on continuous problems. However, how to adapt PSO to efficiently deal with VRPTW is still challenging work. In this paper, we propose a neighborhood comprehensive learning particle swarm optimization (N-CLPSO) to solve VRPTW. To improve the exploitation capability of N-CLPSO, we introduce a new remove-reinsert neighborhood search mechanism, which consists of the removed operator and the reinsert operator. When performing the removed operator, the probability of adjacency between two customers is calculated by an information matrix (I M), which is constructed based on the customers' time-space information and elite individuals' local information. When executing the reinsert operator, the I M and a cost matrix (C M), which is introduced to record the cost of customer insertion, are used to find an optimal insert position. Moreover, to enhance the exploration of N-CLPSO, a semi-random disturbance strategy is proposed, in which elites' longest common sequences (LCS) are saved, aiming to prevent population degradation. The N-CLPSO algorithm is tested on 56 Solomon benchmark instances, and it attains the optimal solutions on 29 instances. The simulation results and comparison results illustrate that the proposed algorithm outperforms or can compete with the majority of other 3 PSO variants as well as other 12 state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The quadratic assignment problem (QAP) is an extremely challenging NP-hard combinatorial optimization program. Due to its difficulty, a research emphasis has been to identify special cases that are polynomially solvable. Included within this emphasis are instances which are linearizable ; that is, which can be rewritten as a linear assignment problem having the property that the objective function value is preserved at all feasible solutions. Various known sufficient conditions for identifying linearizable instances have been explained in terms of the continuous relaxation of a weakened version of the level-1 reformulation-linearization-technique (RLT) form that does not enforce nonnegativity on a subset of the variables. Also, conditions that are both necessary and sufficient have been given in terms of decompositions of the objective coefficients. The main contribution of this paper is the identification of a relationship between polyhedral theory and linearizability that promotes a novel, yet strikingly simple, necessary and sufficient condition for identifying linearizable instances; specifically, an instance of the QAP is linearizable if and only if the continuous relaxation of the same weakened RLT form is bounded. In addition to providing a novel perspective on the QAP being linearizable, a consequence of this study is that every linearizable instance has an optimal solution to the (polynomially-sized) continuous relaxation of the level-1 RLT form that is binary. The converse, however, is not true so that the continuous relaxation can yield binary optimal solutions to instances of the QAP that are not linearizable. Another consequence follows from our defining a maximal linearly independent set of equations in the lifted RLT variable space; we answer a recent open question that the theoretically best possible linearization-based bound cannot improve upon the level-1 RLT form. [ABSTRACT FROM AUTHOR]

Highlights • This paper analyzed the problem-specific knowledge of the network reconstruction problem. • A hybrid algorithm scheme for the problem of network reconstruction is established. • The hybrid algorithm is more general, and the accuracy is dramatically increased. Abstract The topology of a network is crucial to its function and behavior. In many cases, various data are obtained from the network, for example, information spreading data, gene expression microarray data, game data, but the topology of the network is unknown. Reconstructing the topology of the network from the observed data is meaningful in many applications. In this paper an evolutionary algorithm is proposed for network reconstruction from observed game data. The proposed two-stage evolutionary algorithm decomposes the network reconstruction problem as sequentially reconstructing the edges of the nodes. The edges of a node are described by the corresponding column vector of the network adjacency matrix. In the first stage, possible vectors are obtained from the proposed genetic algorithm. In the second stage, the true vector is obtained by the proposed heuristic local search. Both analyses and experiments show that the proposed evolutionary algorithm is more accurate and applicable in more general cases than the algorithm based on compressive sensing theory. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • This paper proposed an improved version of fruit fly optimization (FOA). • Mutation adaptation mechanism is adopted to enhance the balance of FOA in terms of the exploitation and exploration ability. • The phase angle-based encoded strategy helps to achieve the high convergence performance. • The proposed method is used to find the optimal UAV path in the 3D environments. • Numerical experiments results show the proposed method is superior to several existing state-of-the-art optimization algorithms. Abstract This paper proposed an improved version of fruit fly optimization (FOA) to solve the unmanned aerial vehicle (UAV) path planning problem. The improved algorithm combines the phase angle-encoded and mutation adaptation mechanisms into the basic FOA and is referred to as θ-MAFOA. Mutation adaptation mechanism is adopted to enhance the balance of FOA in terms of the exploitation and exploration ability, while phase angle-based encoded strategy for fruit fly locations helps to achieve the high performance in the convergence process. Then, the proposed θ-MAFOA is used to find the optimal flight path for UAV in the complex three-dimensional (3D) terrain environments with various ground defense weapons. B-Spline curve that connects the start and target points is employed to represent a smooth path. Several performance criteria and constraints are taken into consideration to evaluate the cost of UAV paths. Numerical experiments are carried out on various test scenarios and the results show the proposed θ-MAFOA is superior to the basic and other two modified versions of FOA, and also more powerful than several existing state-of-the-art optimization algorithms. [ABSTRACT FROM AUTHOR]

Highlights • We present an analysis of control parameters of MOEA/D under two scenarios. • Our findings reveal that suitable parameter values for each scenario are different. • The reason why the best configuration is different for each scenario is analyzed. Abstract An unbounded external archive (UEA), which stores all nondominated solutions found during the search process, is frequently used to evaluate the performance of multi-objective evolutionary algorithms (MOEAs) in recent studies. A recent benchmarking study also shows that decomposition-based MOEA (MOEA/D) is competitive with state-of-the-art MOEAs when the UEA is incorporated into MOEA/D. However, a parameter study of MOEA/D using the UEA has not yet been performed. Thus, it is unclear how control parameter settings influence the performance of MOEA/D with the UEA. In this paper, we present an analysis of control parameters of MOEA/D under two performance evaluation scenarios. One is a final population scenario where the performance assessment of MOEAs is performed based on all nondominated solutions in the final population, and the other is a reduced UEA scenario where it is based on a pre-specified number of selected nondominated solutions from the UEA. Control parameters of MOEA/D investigated in this paper include the population size, scalarizing functions, and the penalty parameter of the penalty-based boundary intersection (PBI) function. Experimental results indicate that suitable settings of the three control parameters significantly depend on the choice of an optimization scenario. We also analyze the reason why the best parameter setting is totally different for each scenario. [ABSTRACT FROM AUTHOR]

Throughput efficiency holds great importance in the present scenario of the cognitive radio system. In this paper, optimization of throughput in multi-user single relay cognitive radio system has been performed. In a multi-user cognitive radio network, collisions occur between data packets of multiple secondary users, which cause a reduction in the throughput of the network to a great extent. In this paper, a new analytical model for throughput of the multi-user cognitive radio network in terms of the probability of collision has been presented. The model is convenient for the formulation of the optimization problem in such a way that an optimal value of throughput can be achieved by keeping the probability of collision below the allowable limit. This leads to an enhancement in throughput as well as maintains system reliability in the cognitive radio network. Optimal power allocation scheme is used to allocate power among secondary users which makes the system energy efficient. The optimization of the throughput of the system is done using swarm intelligence-based optimization techniques like Particle Swarm Optimization (PSO), Human behavior based Particle Swarm Optimization (HPSO), Particle Swarm Optimization with Aging Leader and Challengers (ALCPSO) and Whale Optimization Algorithm (WOA). Simulation results illustrate that the system optimized with WOA have better throughput than that of the aforementioned optimization algorithms. Moreover, a comparative analysis of the achievable throughput in the multi-user cognitive radio system with the proposed scheme and that with the equal power allocation scheme is presented. The analysis reveals that the proposed scheme makes the system more efficient in terms of throughput than equal power allocation scheme. [ABSTRACT FROM AUTHOR]

Huge growth is observed in the speech and speaker recognition field due to many artificial intelligence algorithms being applied. Speech is used to convey messages via the lan- guage being spoken, emotions, gender and speaker identity. Many real applications in healthcare are based upon speech and speaker recognition, e.g. a voice-controlled wheelchair helps control the chair. In this paper, we use a genetic algo- rithm (GA) for combined speaker and speech recognition, rely- ing on optimized Mel Frequency Cepstral Coefficient (MFCC) speech features, and classification is performed using a Deep Neural Network (DNN). In the first phase, feature extraction using MFCC is executed. Then, feature optimization is per- formed using GA. In the second phase training is conducted using DNN. Evaluation and validation of the proposed work model is done by setting a real environment, and efficiency is calculated on the basis of such parameters as accuracy, precision rate, recall rate, sensitivity, and specificity. Also, this paper presents an evaluation of such feature extraction methods as linear predictive coding coefficient (LPCC), per- ceptual linear prediction (PLP), mel frequency cepstral coef- ficients (MFCC) and relative spectra filtering (RASTA), with all of them used for combined speaker and speech recogni- tion systems. A comparison of different methods based on existing techniques for both clean and noisy environments is made as well. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm based on the General Variable Neighborhood Search (GVNS) metaheuristic for solving the Multi-Depot Vehicle Routing Problem (MDVRP). The MDVRP consists in designing a set of vehicle routes serving all customers, such that the maximum number of vehicle per depot and vehicle-capacity are respected, and the total cost of transportation is minimized. The proposed algorithm uses Randomized Variable Neighborhood Descent (RVND) as local search method and it is tested in classical instances of the problem. The obtained results are presented and discussed in this paper. [ABSTRACT FROM AUTHOR]

Travelling salesman problem is a combinatorial optimization problem with wide application background and important theoretical value. The traditional method is only suitable for solving small scale travelling salesman problems, thus limiting the application and popularization of such methods. Based on genetic algorithm, the paper proposes an improved strategy combining random crossover and dynamic mutation to increase population diversity and optimize mutation characters. The simulation results show the convergence rate and the optimal solution of the improved algorithm in the paper are obviously superior to the traditional genetic algorithm, the adaptive crossover probability genetic algorithm and the improved selection genetic algorithm, and it provides a new method for the travelling salesman problem. [ABSTRACT FROM AUTHOR]

This paper presents an evolutionary algorithm employing differential evolution to solve nonlinear optimisation problems with (or without) constraints and multiple objectives. New decision strategies to compare candidate solutions are developed that take into account all constraints and objective functions. The new constraint handling strategy uses the concept of Pareto dominance to rank the candidate solutions based on their constraint violation value. In order to improve the performance of the algorithm, a set of genetic operators and differential evolution operators are combined. In addition, the paper proposes an algorithm to perform parallel evolution in a way that the diversity of the final population is preserved after migrations. Another goal of the algorithm is to handle problems with a mix of integers and real-valued variables. Numerical experiments investigate the robustness and the performance of the algorithm through multiple benchmark optimisation problems. Finally, two engineering applications are studied, namely: (1) the topology optimisation of trusses; and (2) the economical dispatch problem in power generation. Results show that the algorithm is capable of handling optimisation problems with a mix of integer and real-valued variables with constraints and multiple objectives. [ABSTRACT FROM AUTHOR]

In this paper, a newly surfaced nature-inspired optimization technique called moth-flame optimization (MFO) algorithm is utilized to address the optimal reactive power dispatch (ORPD) problem. MFO algorithm is inspired by the natural navigation technique of moths when they travel at night, where they use visible light sources as guidance. In this paper, MFO is realized in ORPD problem to investigate the best combination of control variables including generators voltage, transformers tap setting as well as reactive compensators sizing to achieve minimum total power loss and minimum voltage deviation. Furthermore, the effectiveness of MFO algorithm is compared with other identified optimization techniques on three case studies, namely IEEE 30-bus system, IEEE 57-bus system and IEEE 118-bus system. The statistical analysis of this research illustrated that MFO is able to produce competitive results by yielding lower power loss and lower voltage deviation than the selected techniques from literature. [ABSTRACT FROM AUTHOR]

Multiple Traveling Repairmen Problem (mTRP) is a class of NP-hard combinatorial optimization problems with many practical applications. In this paper, a general variant of mTRP, also known as the Bounded Single-Depot Multiple Traveling Repairmen Problem (Bounded-mTRP), is introduced. In the BoundedmTRP problem, a fleet of identical vehicles is dispatched to serve a set of customers. Each vehicle that starts from the depot is only allowed to visit the number of customers within a predetermined interval, and each customer must be visited exactly once. Such restrictions appear in real-life applications where the purpose is to have a good balance of workloads for the repairmen. The goal is to find the order of customer visits that minimizes the sum of waiting times. In our work, the proposed algorithm is encouraged by the efficiency of the algorithms in [15, 19, 20] that are mainly based on the principles of the VNS [14]. The penalty VNS extends the well-known VNS [14] by including constraint penalization, to solve the BoundedmTRP effectively. Extensive numerical experiments on benchmark instances show that our algorithm reaches the optimal solutions for the problem with 76 vertices at a reasonable amount of time. Moreover, the new best-known solutions are found in comparison with the state-of-the-art metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given. [ABSTRACT FROM AUTHOR]