Search

Conference organizers often face the following problem: Given a collection of submitted papers, select a subset to be presented at the conference. The bulk of the work often amounts to assembling a pool of reviewers and then sending each submitted paper to several reviewers. We present in this paper a technique for finding a good assignment of papers to reviewers. An important feature of the solution we find is that each paper is sent to at least one reviewer who is "as expert as possible" for that paper. A major component of the problem is modeled as a bottleneck version of a capacitated transshipment problem. [ABSTRACT FROM AUTHOR]

Role assignment is an important and complex task in collaboration and management. Group role assignment (GRA) pursues the optimal team performance by assigning pertinent roles to agents in a team from the team’s viewpoint. There are many factors that should be considered when conducting role assignment. Budgets are such a factor. This paper presents a challenging role assignment problem in collaboration, called GRA with budget constraints (GRABC). This paper contributes a thorough investigation of the GRABC problem including: 1) the first formalization of the proposed problem; 2) a set of practical solutions to different forms of this problem by using the IBM ILOG CPLEX optimization package (CPLEX); 3) a set of necessary conditions for these problems to possess feasible solutions to improve the CPLEX solutions; and 4) a synthesized process to deal with GRABC problems. Contribution 2) actually clarifies a list of requirements in solving the GRABC problem. Simulations, experiments and a case study are used to verify the practicability and efficiency of the proposed solutions. [ABSTRACT FROM AUTHOR]

It is an important topic to organize a team efficiently and keep it in a good state. In most cases, administrators try to avoid critical members. With this requirement, administrators prefer that their team members are able to be good at many things and expert in one (GMEO). However, that too many people are “good at many things” is definitely a waste. Role-based collaboration and its environments—classes, agents, roles, groups, and objects (E-CARGO) model are a good means to provide modeling and solutions to such a challenging problem. This paper formalizes the problem of GMEO into GMEO-1 (the fundamental form of GMEO) with the support of E-CARGO, clarifies two different forms of GMEO-1 and provides two highly practical solutions. The proposed solutions are verified by comparing with initial solutions using a linear programming solver, i.e., the IBM ILOG CPLEX Optimization Platform. The contributions of this paper are a thorough investigation of GMEO-1 that has no exact solutions even for a small group (e.g., ten people) and is normally managed by highly qualified administrators. The proposed solutions provide digital results with algorithms that form a solid foundation for decision making in dealing with similar issues. [ABSTRACT FROM AUTHOR]

In this paper, we formulated the optimal bit-allocation problem for a scalable codec for images/videos as a graph-based constrained vector-valued optimization problem with many optimal solutions, which are referred to as Pareto points. Pareto points are generally derived using weighted sum scalarization; however, it has yet to be determined whether all Pareto points can be derived using this approach. This paper addresses this issue. When presented as a theorem, our results indicate that as long as the rate-distortion function of each resolution is strictly decreasing and convex and the Pareto points form a continuous curve, then all Pareto points can be derived using scalarization. The theorem is verified using the state-of-the-art scalable coding method H.264/SVC and a scalability extension of High Efficiency Video Coding (HEVC). We highlight a number of easily interpretable Pareto points that represent a good trade-off between candidate resolutions. The proximity point is defined as the Pareto point closest to the ideal performance for each resolution. We also model the Pareto points as a function of total bit rate and demonstrate that the Pareto points at other target bit rates can be predicted. [ABSTRACT FROM AUTHOR]

This paper proposes a method for visualizing combinatorial search spaces named low-dimensional Euclidean embedding (LDEE). The proposed method transforms the original search space, such as a set of permutations or binary vectors, to $\pmb {\mathbb {R}}^{k}$ (with ${k} {=} {2}$ or 3 in practice) while aiming to preserve spatial relationships existing in the original space. The LDEE method uses the t-distributed stochastic neighbor embedding (t-SNE) to transform solutions from the original search space to the Euclidean one. In this paper, it is mathematically shown that the assumptions underlying the t-SNE method are valid in the case of permutation spaces with the Mallows distribution. The same is true for other metric spaces provided that the distribution of points is assumed to be normal with respect to the adopted metric. The embedding obtained using t-SNE is further refined to ensure visual separation of individual solutions. The visualization obtained using the LDEE method can be used for analyzing the behavior of the population in a population-based metaheuristic, the working of the genetic operators, etc. Examples of visualizations obtained using this method for the four peaks problem, the firefighter problem, the knapsack problem, the quadratic assignment problem, and the traveling salesman problem are presented in this paper. [ABSTRACT FROM AUTHOR]

Many operation optimization problems such as scheduling and assignment of interest to the automation community are mixed-integer linear programming (MILP) problems. Because of their combinatorial nature, the effort required to obtain optimal solutions increases drastically as the problem size increases. Such operation optimization problems typically need to be solved several times a day and require short solving times (e.g., 5, 10, or 20 min). The goal is, therefore, to obtain near-optimal solutions with quantifiable quality in a computationally efficient manner. Existing MILP methods, however, suffer from slow convergence and may not efficiently achieve this goal. In this paper, motivated by fast convergence of augmented Lagrangian relaxation (LR), a novel advanced price-based decomposition and coordination “surrogate absolute-value LR” (SAVLR) approach is developed. Within the method, convergence of our recent surrogate LR (SLR), which has overcome all major difficulties of traditional LR, is significantly improved by penalizing constraint violations by adding “absolute-value” penalties. Moreover, such penalties are efficiently linearized in a standard way, thereby enabling the use of MILP solvers. By exploiting the beautiful property of exponential reduction of complexity of subproblems upon decomposition, subproblems are efficiently solved and their solutions are efficiently coordinated by updating Lagrangian multipliers. Convergence is then proved under novel adjustment of penalty coefficients. A series of generalized assignment problems is considered, and for these problems, superior performance of SAVLR over other state-of-the-art and state-of-the-practice methods is demonstrated. Accompanying CPLEX codes, whereby SAVLR is implemented, are also included. Note to Practitioners—Examples of important problems that arise in automation community include scheduling and assignment problems. Because of their combinatorial nature, the effort required to obtain optimal solutions increases drastically as the problem size increases. Existing mixed-integer linear programming (MILP) methods, however, may suffer from slow convergence and may not efficiently achieve this goal. The new method revolutionizes the way such problems can be solved with major improvements on the overall performance. It is based on our recent breakthrough “surrogate Lagrangian relaxation” (LR), which has overcome all major difficulties of traditional LR while exploiting the beautiful property of exponential reduction of complexity upon decomposition. To significantly improve convergence while maintaining linearity so as to use MILP solvers, our idea is to penalize violations of relaxed constraints by the infrequently used “absolute-value” penalty functions. Although not differentiable, absolute-value penalties have the advantage of being exactly linearizable through extra variables and constraints. The difficulties caused by those extra constraints, which couple subproblems, are resolved by adaptive adjustment of penalty coefficients. A series of generalized assignment problems is considered and superior performance of the new method against state-of-the-art and state-of-the-practice methods is demonstrated. Accompanying CPLEX codes whereby the new method is implemented are also included. [ABSTRACT FROM AUTHOR]

The integration of aerial base stations carried by drones in cellular networks offers promising opportunities to enhance the connectivity enjoyed by ground users. In this paper, we propose an optimization framework for the 3–D placement and repositioning of a fleet of drones with a realistic inter-drone interference model and drone connectivity constraints. We show how to maximize network coverage by means of an extremal-optimization algorithm. The design of our algorithm is based on a mixed-integer non-convex program formulation for a coverage problem that is NP-Complete, as we prove in the paper. We not only optimize drone positions in a 3–D space in polynomial time, but also assign flight routes solving an assignment problem and using a strong geometrical tool, namely Bézier curves, which are extremely useful for non-uniform and realistic topologies. Specifically, we propose to fly drones following Bézier curves to seek the chance of approaching to clusters of ground users. This enhances coverage over time while users and drones move. We assess the performance of our proposal for synthetic scenarios as well as realistic maps extracted from the topology of a capital city. We demonstrate that our framework is near-optimal and using Bézier curves increases coverage up to 47 percent while drones move. [ABSTRACT FROM AUTHOR]

This paper analyzes the integration of Non-Orthogonal Multiple Access (NOMA) in a Fog Radio Access Network (FRAN) architecture with limited fronthaul capacity. More precisely, it proposes methods for optimizing the resource allocation for the downlink of a NOMA-based FRAN with multiple resource blocks (RB). The resource allocation problem is formulated as a mixed-integer optimization problem, which determines the user-to-RB assignment, the power allocated to each RB, and the power split levels of the NOMA users served by each RB. The optimization problem maximizes a network-wide rate-based utility function subject to fronthaul-capacity constraints. The paper proposes a feasible decoupled solution for such a non-convex optimization problem using a three-step hybrid centralized/distributed approach, which in part relies on the edge-devices computation capabilities. The paper proposes and compares two distinct methods for solving the assignment problem, namely a Hungarian-based method, and a Multiple Choice Knapsack-based method. The power allocation to RBs and the NOMA power split optimization are solved using the alternating direction method of multipliers (ADMM). Simulations results illustrate the advantages of the proposed methods compared to different baseline schemes, including the conventional Orthogonal Multiple Access (OMA), for different utility functions and different network environments. [ABSTRACT FROM AUTHOR]

In this paper, new probabilistic and dynamic (adaptive) strategies for creating multi-method ensembles based on the coral reef optimization with substrate layers (CRO-SL) algorithm are proposed. CRO-SL is an evolutionary-based ensemble approach that is able to combine different search procedures for a single population. In this work, two different probabilistic strategies to improve the algorithm are analyzed. First, the probabilistic CRO-SL (PCRO-SL) is presented, which substitutes the substrates in the CRO-SL population with tags associated with each individual. Each tag represents a different operator which will modify the individual in the reproduction phase. In each generation of the algorithm, the tags are randomly assigned to the individuals with similar probabilities, obtaining this way an ensemble that sees more intense changes with the application of different operators to a given individual than CRO-SL. Second, the dynamic probabilistic CRO-SL (DPCRO-SL) is presented, in which the probability of tag assignment is modified during the evolution of the algorithm, depending on the quality of the solutions generated in each substrate. Thus, the best substrates in the search process will be assigned higher probabilities than those which showed worse performance during the search. The performances of the proposed probabilistic and dynamic ensembles were tested for different optimization problems, including benchmark functions and a real application of wind-turbine-layout optimization, comparing the results obtained with those of existing algorithms in the literature. [ABSTRACT FROM AUTHOR]

This paper addresses the storage assignment problem in a mixed-shelves warehouse. In a mixed-shelves warehouse, a stock-keeping unit（SKU）is assigned to several storage locations, and many kinds of SKUs are assigned in neighboring areas. Such an arrangement can reduce the length of the picking tour when many different orders, each involving small amounts for each SKU, are to be filled. However, locating SKUs in several storage areas makes it difficult to decide the best position for each SKU. Moreover, when the pickers pick an SKU for an order, they must decide where to start, and which storage locations should be visited in order to ensure the shortest picking tour. In this paper, we first propose a mathematical model for the mixed-shelves problem. We then propose two methods to obtain approximate solutions to the problem. The first method uses a two-step approach in which we first decide the best SKU pairs to assign to the same storage area and then determine the best position for these SKU pairs. The other method consists of three steps. The first step decides the SKU pairs, the second decides the storage location assignment, and the third decides the routing. To produce the best solution, a simulated annealing-based algorithm for the assignment problem is proposed. A numerical experiment is conducted to verify the effectiveness of the two proposed methods relative to the exact method for solving the proposed mathematical formulation, especially for large size datasets [ABSTRACT FROM AUTHOR]

Reviewer Assignment Problem (RAP) is an important issue in peer-review of academic writing. This issue directly influences the quality of the publication and as such is the brickwork of scientific authentication. Due to the obvious limitations of manual assignment, automatic approaches for RAP is in demand. In this paper, we conduct a survey on those automatic approaches appeared in academic literatures. In this paper, regardless of the way reviewer assignment is structured, we formally divide the RAP into three phases: reviewer candidate search, matching degree computation, and assignment optimization. We find that current research mainly focus on one or two phases, but obviously, these three phases are correlative. For each phase, we describe and classify the main issues and methods for addressing them. Methodologies in these three phases have been developed in a variety of research disciplines, including information retrieval, artificial intelligence, operations research, etc. Naturally, we categorize different approaches by these disciplines and provide comments on their advantages and limitations. With an emphasis on identifying the gaps between current approaches and the practical needs, we point out the potential future research opportunities, including integrated optimization, online optimization, etc. [ABSTRACT FROM AUTHOR]

Integrating dense small cell (DSC) networks with mobile edge computing is employed by 5G to tackle the contradiction between the computation limitations of user equipment (UE) and the stringent latency requirement of services. This paper investigates the service-oriented edge resource allocation problem in DSC networks, determining where to deploy the service entity, how many service entities should be deployed at each edge cloud, and how to assign the UEs to service entities. The problem is challenging for the following three aspects: 1) Service entity deployment and UE assignment are highly coupled. 2) Due to the overlap of coverage regions of densely deployed small cells, the allocation mechanism of different base stations has mutual effects on the overall service performance. 3) Considering the limited resources of edge clouds, it is a thorny problem to encourage edge clouds to cache and share service startup images. We devote the following efforts to tackle the problem under these challenges. First, we explore blockchain’s decentralized, traceable, and secure characteristics, and propose a scheme to encourage image sharing in mobile edge computing. Second, we formulate the service-oriented edge resource allocation as mixed integer non-linear programming. Third, towards the target of reducing the computational complexity, we decouple UE assignment from service entity deployment and solve it through Gibbs sampling. Moreover, the power of Lyapunov optimization and convex optimization is incorporated to reduce the long-term power consumption and budget. Experiment results demonstrate the superiority of our approach over current notable solutions. [ABSTRACT FROM AUTHOR]

We consider the energy-efficient mobility-aware content caching problem with clustered small cells (SCs) in ultra-dense networks (UDNs). The UDN is regarded as one of the key solutions to overcome the explosive growth of mobile data traffic and consequential energy consumption. In addition, caching the popular contents at the edge of UDNs can further mitigate the challenges of traffic and energy. In this paper, we group the SCs into disjoint clusters and model the mobility of users between clusters as a Markov chain. We formulate the caching problem aimed at minimizing the overall energy consumption for content delivery to the moving users. Then we decompose the complicated task of energy consumption optimization in cache-enabled UDNs into two sub-problems. In the first sub-problem, we formulate the inter-cluster caching problem as minimizing the energy consumption for serving a user request by the macro base station. We show that the first sub-problem is NP-hard, and develop a polynomial-heuristic algorithm. Further, we define the mobility-oblivious version of the sub-problem, and provide a ${\textstyle {1 \over {2k }}}$ -approximation solution for the mobility-aware based on this mobility-oblivious version. In the second sub-problem, we consider the heterogeneity of small cells and define the intra-cluster caching problem. The aim of this sub-problem is the placement of content in different SCs within the cluster so that the energy consumption for the content delivery in that cluster is minimized. In particular, the second sub-problem fits the general framework of the generalized assignment problem, which allows us to exploit its rich literature, to determine the optimal solution for our problem. Through extensive simulations based on real wireless data and human mobility patterns, we demonstrate the benefits of our approach that is even up to 56% better compared to the conventional caching schemes. [ABSTRACT FROM AUTHOR]

This paper studies linear distributed estimation of an unknown random parameter vector in a bandwidth-constrained multisensor network. To meet the bandwidth limitations, each sensor converts its observation into a low-dimensional datum via a suitable linear transformation. Then, the fusion center estimates the parameter vector by linearly combining all the received low-dimensional data, aiming at minimizing the estimation mean square error. The main purpose of this paper is to jointly determine the compression dimension of each sensor (referred to as dimension assignment) and design the corresponding compression matrix when the total compression dimensions is limited. Such a joint design problem can be formulated as a rank-constrained optimization problem and it is shown to be NP-hard for the first time. In addition, successive quadratic upper-bound minimization (SQUM), SQUM-block coordinate descent (SQUM-BCD) and nuclear norm regularization (NNR) methods are developed to solve it approximately. Furthermore, we show that any accumulation point of the sequence generated by the SQUM method satisfies the Karush-Kuhn-Tucker conditions of the rank-constrained optimization problem, and the Phase II algorithm of the SQUM-BCD and NNR methods (both are two-phase algorithms and have the same Phase II algorithm) guarantees convergence at least to a stationary point. Numerical experiments illustrate the advantages of the proposed methods compared with the existing method. [ABSTRACT FROM AUTHOR]

In this paper, we deal with the problem of dynamical assignment of traffic light schedules in large-scale urban networks. We present a model for signalized traffic networks, based on the cell transmission model, and then a simplified model based on averaging theory. The control objective is to improve traffic, optimizing traffic indexes such as total travel distance and density balancing. We design a scheme that decides the duty cycles of traffic lights, by solving a convex program. The optimization is done in real time, at each cycle of traffic lights, so as to take into account variable traffic demands. The scalability problem is tackled through the synthesis of a distributed optimization algorithm; this reduces the computational load significantly, since the large optimization problem is broken into small local subproblems, whose size does not grow with the size of the network, together with iterative exchanges of messages with few neighbor subproblems. The performance of the proposed approach is evaluated via numerical simulations in two different scenarios: a macroscopic (MATLAB-based) Manhattan grid and a microscopic scenario (based on Aimsun simulator) reproducing a portion of the city of Grenoble, France. [ABSTRACT FROM AUTHOR]

This paper is concerned with a new variant of bin packing called priority-based bin packing with subset constraints (PBBP-SC). PBBP-SC is a variant of traditional bin packing (TBP). In a TBP instance, we are given a collection of items { t 1 , t 2 , ... t n } , where each item t i has size s i ∈ (0 , 1). The goal of TBP is to pack the items in as few unit-sized bins as possible. In a PBBP-SC instance, we are given a collection of n unit-size items and a collection of m bins of varying capacities. Associated with each item is a positive integer which is called its priority. The priority of an item indicates its importance in being included in a (possibly infeasible) packing. As with the traditional case, these items need to be packed in the fewest number of bins. What complicates the problem is the fact that each item can be assigned to only one of a select set of bins, i.e., the bins are not interchangeable. We investigate several problems associated with PBBP-SC. The first problem we study is the feasibility problem. This is the problem of checking if there is a feasible assignment to a given PBBP-SC instance. The second problem we study is the priority maximization problem. This is the problem of finding a maximum priority assignment of an infeasible PBBP-SC instance. The third problem we study is the bin minimization problem. This is the problem of finding an assignment with the fewest number of bins to pack all of the items in a feasible PBBP-SC instance. We derive a number of results from both the algorithmic and computational complexity perspectives for these problems. In particular, we show that both the feasibility and priority maximization problems are solvable in polynomial time. We also show that the bin minimization problem is log-APX-complete and cannot be solved in time o (1. 4 1 m) unless the Strong Exponential Time Hypothesis fails. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of managing an energy community hosting a fleet of electric vehicles for rent. On the day ahead, service requests for electric vehicles are submitted to the community. Then, the optimal request-to-vehicle assignment has to be found, as well as the optimal charging schedule of vehicle batteries. A suitable model is presented and included in an existing energy community architecture. The overall community management problem is formulated as a bi-level model, featuring two nested optimization problems. The optimal request-to-vehicle assignment requires the solution of a mixed-integer linear program. To reduce the computational complexity, a heuristic solution to the assignment problem is presented. Numerical results show that participation in the community grants a remarkable reduction in the electric vehicle charging cost. The adoption of the heuristic assignment solution provides a dramatic reduction in the computation time required to solve the bi-level model. At the same time, the level of suboptimality introduced appears to be negligible, being less than 1% in most of the considered cases. [ABSTRACT FROM AUTHOR]

Geospatial data conflation is aimed at matching counterpart features from two or more data sources in order to combine and better utilize information in the data. Due to the importance of conflation in spatial analysis, different approaches to the conflation problem have been proposed ranging from simple buffer-based methods to probability and optimization based models. In this paper, I propose a formal framework for conflation that integrates two powerful tools of geospatial computation: optimization and relational databases. I discuss the connection between the relational database theory and conflation, and demonstrate how the conflation process can be formulated and carried out in standard relational databases. I also propose a set of new optimization models that can be used inside relational databases to solve the conflation problem. The optimization models are based on the minimum cost circulation problem in operations research (also known as the network flow problem), which generalizes existing optimal conflation models that are primarily based on the assignment problem. Using comparable datasets, computational experiments show that the proposed conflation method is effective and outperforms existing optimal conflation models by a large margin. Given its generality, the new method may be applicable to other data types and conflation problems. [ABSTRACT FROM AUTHOR]

Pilot contamination is a limiting factor in multicell massive multiple-input multiple-output (MIMO) systems because it can severely impair channel estimation. Prior works have suggested coordinating pilot design across cells in order to reduce the channel estimation error caused by pilot contamination. In this paper, we propose a method for coordinated pilot design using fractional programming to minimize the weighted mean squared-error (MSE) in channel estimation. In particular, we apply the recently proposed quadratic transform to the MSE expression which allows the effect of pilot contamination to be decoupled. The resulting problem reformulation enables the pilots to be optimized in closed form if they can be designed arbitrarily. When the pilots are restricted to a given set of orthogonal sequences, pilot optimization reduces to an assignment problem which can be solved by weighted bipartite matching. Furthermore, we consider the max-min fairness of data rates with orthogonal pilots and obtain an extension of the proposed method to correlated Rayleigh fading. Finally, simulations demonstrate the advantage of the proposed (orthogonal and nonorthogonal) pilot designs as compared with state-of-the-art methods in combating pilot contamination. [ABSTRACT FROM AUTHOR]

Feature matching is a fundamental problem in many computer vision tasks. This paper proposes a novel effective framework for mismatch removal, named LOcality-guided Global-preserving Optimization (LOGO). To identify inliers from a putative matching set generated by feature descriptor similarity, we introduce a fixed-point progressive approach to optimize a graph-based objective, which represents a two-class assignment problem regarding an affinity matrix containing global structures. We introduce a strategy that a small initial set with a high inlier ratio exploits the topology of the affinity matrix to elicit other inliers based on their reliable geometry, which enhances the robustness to outliers. Geometrically, we provide a locality-guided matching strategy, i.e., using local topology consensus as a criterion to determine the initial set, thus expanding to yield the final feature matching set. In addition, we apply local affine transformations based on reference points to determine the local consensus and similarity scores of nodes and edges, ensuring the validity and generality for various scenarios including complex nonrigid transformations. Extensive experiments demonstrate the effectiveness and robustness of the proposed LOGO, which is competitive with the current state-of-the-art methods. It also exhibits favorable potential for high-level vision tasks, such as essential and fundamental matrix estimation, image registration and loop closure detection. [ABSTRACT FROM AUTHOR]

Spatial data conflation is aimed at matching and merging objects in two datasets into a more comprehensive one. Starting from the "map assignment problem" in the 1980s, optimized conflation models treat feature matching as a natural optimization problem of minimizing certain metrics, such as the total discrepancy. One complication in optimized conflation is that heterogeneous datasets can represent geographic features differently. Features can correspond to target features in the other dataset either on a one-to-one basis (forming full matches) or on a many-to-one basis (forming partial matches). Traditional models consider either full matching or partial matches exclusively. This dichotomy has several issues. Firstly, full matching models are limited and cannot capture any partial match. Secondly, partial matching models treat full matches just as partial matches, and they are more prone to admit false matches. Thirdly, existing conflation models may introduce conflicting directional matches. This paper presents a new model that captures both full and partial matches simultaneously. This allows us to impose structural constraints differently on full/partial matches and enforce the consistency between directional matches. Experimental results show that the new model outperforms conventional optimized conflation models in terms of precision (89.2%), while achieving a similar recall (93.2%). [ABSTRACT FROM AUTHOR]

Accurate delay analysis with distributed RC delay can be computationally expensive, and can contribute the majority of the total runtime for gate sizers. Recent works have shown that Lagrangian relaxation (LR)-based gate sizers have produced designs with the lowest power on average. But they are also very slow due to a large number of expensive timing updates spread across several tens of iterations. In this paper, we develop an LR-based discrete gate sizer for fast timing and power reduction. Our gate sizer is multithreaded and is equipped with parallelization enabling techniques, namely mutual exclusion edge (MEE) assignment and directed acyclic graph (DAG)-based netlist traversal (DNT). MEEs are dummy edges assigned to improve load sharing among different threads. DNT facilitates simultaneous resizing of gates belonging to different topological levels. Our Lagrange multiplier update strategy enables rapid convergence of our timing and power recovery algorithms. To reduce the runtime of timing updates, we propose a simple and fast-to-compute effective capacitance model. We further propose mechanisms to calibrate timing models to improve their accuracy. By calibrating the internal timing models only twice, our proposed gate sizing flow facilitates extremely fast design optimization. We benchmark our gate sizer using the ISPD 2012 and 2013 gate sizing contest benchmark suites. Compared to the state-of-the-art gate sizer, our proposed gate sizer is on average $15\times $ faster and the optimized designs have 2.5% higher leakage power. Since we tradeoff timing accuracy for larger runtime speedup, our optimized designs have small timing violations. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the scheduling design of a mobile edge computing (MEC) system, where active mobile devices with computation tasks randomly appear in a cell. Every task can be computed at either the mobile device or the MEC server. We jointly optimize the task offloading decision, uplink transmission device selection and power allocation by formulating the problem as an infinite-horizon Markov decision process (MDP). Compared with most of the existing literature, this is the first attempt to address the transmission and computation optimization with random device arrivals in an infinite time horizon to our best knowledge. Due to the uncertainty in the device number and location, the conventional approximate MDP approaches addressing the curse of dimensionality cannot be applied. An alternative and suitable low-complexity solution framework is proposed in this work. We first introduce a baseline scheduling policy, whose value function can be derived analytically with the statistics of random mobile device arrivals. Then, one-step policy iteration is adopted to obtain a sub-optimal scheduling policy whose performance can be bounded analytically. The complexity of deriving the sub-optimal policy is reduced dramatically compared with conventional solutions of MDP by eliminating the complicated value iteration. To address a more general scenario where the statistics of random mobile device arrivals are unknown, a novel and efficient algorithm integrating reinforcement learning and stochastic gradient descent (SGD) is proposed to improve the system performance in an online manner. Simulation results show that the gain of the sub-optimal policy over various benchmarks is significant. [ABSTRACT FROM AUTHOR]

Two-echelon vehicle routing problem (2E-VRP) is a challenging problem that involves both the strategic and tactical planning decisions on both echelons. The satellite locations and the customer distribution affect the cost of different components on the second echelon, thus the possibilities of satellite-to-customer assignment complicates the problem. In this paper, we propose a graph-based fuzzy evolutionary algorithm for solving 2E-VRP. The proposed method integrates a graph-based fuzzy assignment scheme into an iteratively evolutionary learning process to minimize the total cost. To resolve the possibilities of the satellite-to-customer assignment, graph-based fuzzy operator is used to take advantage of population evolution and avoid excessive fitness evaluations of unpromising moves in different satellites. Each offspring is produced via graph-based fuzzy assignment procedure out of an assignment graph from parent individuals, and fuzzy local search procedure is used to further improve the offspring. The experimental results on the public test sets demonstrate the competitiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is cost optimization of project schedules under constrained resources and alternative production processes (APPs). Design/methodology/approach: The model contains a cost objective function, generalized precedence relationship constraints, activity duration and start time constraints, lag/lead time constraints, execution mode (EM) constraints, project duration constraints, working time unit assignment constraints and resource constraints. The mixed-integer nonlinear programming (MINLP) superstructure of discrete solutions covers time–cost–resource options related to various EMs for project activities as well as variants for production process implementation. Findings: The proposed model provides the exact optimal output data for project management, such as network diagrams, Gantt charts, histograms and S-curves. In contrast to classic scheduling approaches, here the optimal project structure is obtained as a model-endogenous decision. The project planner is thus enabled to achieve optimization of the production process simultaneously with resource-constrained scheduling of activities in discrete time units and at a minimum total cost. Practical implications: A set of application examples are addressed on an actual construction project to display the advantages of proposed model. Originality/value: The unique value this paper contributes to the body of knowledge reflects through the proposed MINLP model, which is capable of performing the exact cost optimization of production process (where presence and number of activities including their mutual relations are dealt as feasible alternatives, meaning not as fixed parameters) simultaneously with the associated resource-constrained project scheduling, whereby that is achieved within a uniform procedure. [ABSTRACT FROM AUTHOR]

In recent years, the smart inverter that is an advanced inverter has been investigated as a solution to voltage problem in the distribution network in which a lot of photovoltaic(PV)s are introduced Among multiple functions of the smart inverter, voltage‐reactive power control (Volt‐Var) and voltage‐active power control (Volt‐Watt) have been attracted attention because of their effectiveness. However, the problem is how to determine optimal curves setting of Volt‐Var and Volt‐Watt. In this paper, we suggest the determination method of optimal Volt‐Var and Volt‐Watt curves, and verify the effectiveness of the optimal curves on distribution network. [ABSTRACT FROM AUTHOR]

The weapon target assignment (WTA) problem is an important task to tactical arrangements in military commitment operations. It describes the optimal method to allocate defense in opposition to threats in fighting situations. It is an NP-complete issue in which no accurate outcome for all conceivable situations is known. The time performance of created algorithms is a major challenge in modeling the WTA problem, which has only been lately considered in related papers. This article improves the recently developed algorithm called improved Tunicate Swarm Algorithm (iTSA) which is inspired by the natural behavior of tunicates to solve the WTA problem. The suggested method is compared with well-known metaheuristic approaches. The experimental findings show that the method presented works better than previous competing metaheuristic approaches. [ABSTRACT FROM AUTHOR]

This paper investigates the integration of reconfigurable intelligent surface (RIS) with downlink non-orthogonal-multiple-access (NOMA) in a multi-user two-cell network assisted by the joint-transmission coordinated multipoint (JT-CoMP). Specifically, the RIS is deployed at the edge of two adjacent cells to assist the JT-CoMP from these two cells to multiple far NOMA users located at their edges. Under this setup, we jointly optimize the power allocation (PA) coefficients at the base stations (BSs), the user clustering (UC) policy, and the phase-shift (PS) matrix of the RIS with the objective of maximizing the network sum-rate subject to a target quality-of-service, defined in terms of the minimum required data rate at each cellular user, and the successive interference cancellation (SIC) constraints. The formulated problem ends to be a non-convex mixed-integer non-linear program that is difficult to be solved in a straightforward manner. To alleviate this issue, and with the aid of alternating optimization (AO), the original optimization problem is decomposed into two sub-problems, a joint PA and UC sub-problem and a PS sub-problem, that are solved in an alternating way. For the first sub-problem, we invoke the bi-level optimization approach to decouple the PA sub-problem from the UC sub-problem. For the PA sub-problem, closed-form expressions for the optimal PA coefficients are derived. On the other hand, the UC problem is projected to multiple 2-dimensional assignment problems, each of which is solved using the Hungarian method. Finally, the PS sub-problem is formulated as a difference-of-convex problem and an efficient solution is obtained using the successive convex approximation technique. The numerical results reveal that the network sum-rate of the proposed RIS-assisted CoMP NOMA networks outperforms the conventional CoMP NOMA scheme without the assistance of the RIS, the RIS-assisted CoMP orthogonal multiple access (OMA) scheme, and RIS-assisted NOMA scheme, especially for low transmit power from the BSs. [ABSTRACT FROM AUTHOR]

Industrial environments such as manufacturing and transportation industries usually involve fleets of identical systems that must carry out several missions interspersed with scheduled finite breaks. Given the limited amount of maintenance resources and time available, only a restricted number of maintenance actions can be performed on selected components to ensure a pre-specified performance level of the fleet for the next mission. Such a maintenance strategy is known as fleet-level selective maintenance (FSM). The FSM is more complex than the selective maintenance problem as it adds the total number of systems in the fleet as another level of combinations to be explored during the optimization process. Most FSM models consider the replacement or perfect repair of system components as the only maintenance option. Furthermore, they consider a single repair channel and disregard the assignment of repairpersons and the impact of their variable skillsets on the maintenance costs and duration. In this paper, an approach is proposed to help in more realistic decision making for FSM where several imperfect maintenance levels and multiple repair channels are available. A novel integrated non-linear programming formulation of the FSM problem where maintenance and repairpersons assignment decisions are made jointly is proposed. All relevant parameters and terms of this non-linear optimization problem are developed and discussed. A two-phase modeling approach is then used to transform the original nonlinear problem into a binary integer optimization model. To demonstrate the validity and the added value of the proposed approach, multiple sets of numerical experiments are investigated and managerial implications are provided. [ABSTRACT FROM AUTHOR]

In this paper, an aerial heterogeneous wireless network consists of a high altitude platform (HAP) and low altitude platforms (LAPs) as aerial base stations (ABSs) in a downlink orthogonal division multiple access (OFDMA) transmission is investigated. Two types of spectrum sharing, including spectrum underlay access (SUA) and spectrum overlay access (SOA) are employed to evaluate the inter-tier interference. Distributed problem formulation and solving method are used to reduce the computational complexity, communication overhead and system latency. Subcarrier assignment and power allocation in spectral efficiency (SE), energy efficiency (EE) and transmission power optimization problems are accomplished by the proposed two-layer algorithm. In the proposed algorithm, first, subcarrier assignment based on constraints of the optimization problems is carried out to cover both SUA and SOA with low computational complexity. Second, the proposed algorithm performs power allocation. The simulation results show the relation between spectrum reuse and inter-tier interference, such that system SE, EE and the number of served users are maximized when both spectrum reuse and inter-tier interference management are performed together. [ABSTRACT FROM AUTHOR]

Crowdsourced live streaming (CLS), such as Twitch.tv and Inke.tv, has emerged as an important multimedia application in recent years. Video delivery in such CLS service involves two steps: 1) video uploading—video streaming (i.e., a live channel) generated from a broadcaster is uploaded to the server, which we call the “first mile” network and 2) video distribution—the video streaming is then delivered to viewers in the channel. Today’s CLS services usually use conventional content delivery network solutions to address the video distribution problem, while little attention has been paid to improve the video uploading quality. Our measurement study shows that the first mile network causes 17% viewer rebuffers, and some viewers quit the channel once encountering rebuffer. In this paper, we propose a content harvest network (CHN) architecture to address the uploading problem in the CLS service. Specifically, the CHN architecture employs edge devices in the network as relays to receive the streaming uploaded by broadcasters and then forward to the central servers. On one hand, we need to reduce the latency since it is live streaming; on the other hand, we must provide sustainable upload bandwidth. It is challenging to achieve both at the same time, especially in such high dynamic system as CLS. In order to provide global optimal and real-time assignment, we propose a hybrid solution, i.e., centralized and distributed assignment. Specifically, we formulate the centralized relay assignment problem as an optimization problem to achieve both low latency and sustainable bandwidth. To cope with the frequent channel establishments, we use a multi-armed bandit method to characterize the time-variant network condition. Experiment results on a large-scale trace provided by Inke.tv show that our solution can reduce the overall viewer cost by 40% compared to state-of-the-art solutions. The viewers’ rebuffer can also be reduced by 50%. [ABSTRACT FROM AUTHOR]

Obtaining a multiresource allocation scheme for multitask influenced by uncertain factors is a critical problem in a collaborative logistics network. This paper presents an optimal allocation model of fuzzy resources for multistage random logistics tasks based on the six-point trapezoidal fuzzy number and the membership function. Besides, considering task demands and resource constraints, a new cost–time–quality multiobjective programming of N–N task–resource assignment is introduced, which can be divided into minimize total logistics cost and execution time, maximize total service quality. Furthermore, by setting the different simulation scenarios, the results show that if the decision maker has a higher risk preference and pursues the optimization of single or multiobjective, the higher degree membership and satisfaction function values can be obtained with a larger compensation coefficient. The allocation scheme of task–resource assignment generated by proposed model has a high global level of utilization efficiency, which can effectively utilize fuzzy resources in collaborative logistics network, and avoid resource shortage caused by the excessive occupation of local resources. [ABSTRACT FROM AUTHOR]

Inverted generational distance (IGD) has been widely considered as a reliable performance indicator to concurrently quantify the convergence and diversity of multiobjective and many-objective evolutionary algorithms. In this paper, an IGD indicator-based evolutionary algorithm for solving many-objective optimization problems (MaOPs) has been proposed. Specifically, the IGD indicator is employed in each generation to select the solutions with favorable convergence and diversity. In addition, a computationally efficient dominance comparison method is designed to assign the rank values of solutions along with three newly proposed proximity distance assignments. Based on these two designs, the solutions are selected from a global view by linear assignment mechanism to concern the convergence and diversity simultaneously. In order to facilitate the accuracy of the sampled reference points for the calculation of IGD indicator, we also propose an efficient decomposition-based nadir point estimation method for constructing the Utopian Pareto front (PF) which is regarded as the best approximate PF for real-world MaOPs at the early stage of the evolution. To evaluate the performance, a series of experiments is performed on the proposed algorithm against a group of selected state-of-the-art many-objective optimization algorithms over optimization problems with 8-, 15-, and 20-objective. Experimental results measured by the chosen performance metrics indicate that the proposed algorithm is very competitive in addressing MaOPs. [ABSTRACT FROM AUTHOR]

The growth of aviation industry in Indonesia accelerates the escalation of Airport Gate Assignment Problem in the Soekarno Hatta International Airport. The gate assignment issue mainly influence passenger satisfaction over the general flight service. This research was conducted to develop Gate Assignment Optimization in the Soekarno Hatta International Airport, focused on minimizing the number of Un-gated Flights. This paper propose a metaheuristic approximation approach namely simulated annealing to solve the Gate Assignment Problem. Output of this paper is the aircraft Gate Assignment Order at Terminal 2 Soekarno Hatta International Airport. The result suggests, decreasing number of Un-gated Flights occurred with the implementation of the proposed method. This research may be developed in the future by adding another objective function such as minimizing passenger walking distance and constructing real time based model. [ABSTRACT FROM AUTHOR]

With the proliferation of GPS-enabled smart devices and increased availability of wireless network, spatial crowdsourcing (SC) has been recently proposed as a framework to automatically request workers (i.e., smart device carriers) to perform location-sensitive tasks (e.g., taking scenic photos, reporting events). In this paper, we study a destination-aware task assignment problem that concerns the optimal strategy of assigning each task to proper worker such that the total number of completed tasks can be maximized whilst all workers can reach their destinations before deadlines after performing assigned tasks. Finding the global optimal assignment turns out to be an intractable problem since it does not imply optimal assignment for individual worker. Observing that the task assignment dependency only exists amongst subsets of workers, we utilize tree-decomposition technique to separate workers into independent clusters and develop an efficient depth-first search algorithm with progressive bounds to prune non-promising assignments. In order to make our proposed framework applicable to more scenarios, we further optimize the original framework by proposing strategies to reduce the overall travel cost and allow each task to be assigned to multiple workers. Extensive empirical studies verify that the proposed technique and optimization strategies perform effectively and settle the problem nicely. [ABSTRACT FROM AUTHOR]

In this paper, we study the handover (HO), and power allocation problem in a two-tier heterogeneous network (HetNet), which consists of a macro base station, and some millimeter-wave (mmWave) small base stations. We establish an HO management, and power allocation scheme to maximize the overall throughput while reducing the HO frequency. In particular, considering the interrelationship among decisions made by different user equipments (UEs), we first model the HO, and power allocation problem as a fully cooperative multi-agent task, in which all agents, i.e., UEs, have the same target. Then, to solve the multi-agent task, and get decentralized policies for each UE, we develop a multi-agent reinforcement learning (MARL) algorithm based on the proximal policy optimization (PPO) method, by introducing the centralized training with decentralized execution framework. That is, we use global information to train policies for each UE, and after the training is finished, each UE obtains a decentralized policy, which can be implemented only based on each UE's local observation. Specially, we introduce the counterfactual baseline to address the credit assignment problem in centralized learning. Due to the centralized training, the decentralized polices learned by multi-agent PPO (MAPPO) can work more cooperatively. Finally, the simulation results demonstrate that our method can achieve better performance comparing with other existing works. [ABSTRACT FROM AUTHOR]

The application of network slicing to mobile edge computing (MEC) systems has aroused great interests from both academia and industry. However, the optimization of network slicing and MEC in most existing research works only focuses on resource slicing, energy scheduling, and power allocation from the perspective of mobile devices, without considering the operator's revenue. In this paper, we propose a novel framework for network slicing in MEC systems, including slice request admission and a revenue model, to investigate the operator's revenue escalation problem while considering traffic variations. The revenue model is mainly composed of the longer-term revenue and short-term revenue. Particularly, we jointly optimize slice request admission in the long-term and resource allocation in the short-term to maximize the operator's average revenue. By employing the Lyapunov optimization technique, we develop an algorithm without requiring any prior-knowledge of traffic distributions, referred to as the DNSRA, to solve the problem. To reduce the computational complexity of directly solving the DNSRA, we decouple the optimization variables for efficient algorithm design. By this, the strategies for user association and CPU-cycle frequency are obtained in closed forms, respectively. Power allocation and subcarriers assignment are obtained by employing the successive convex approximation approach. Meanwhile, we develop a heuristic algorithm to obtain the dynamic slice request admission decision. Simulation results show that the proposed DNSRA can strike a flexible balance between the average revenue and the average delay, and can significantly increase the operator's revenue against existing schemes. [ABSTRACT FROM AUTHOR]

Network slicing has been considered as a promising candidate to provide customized services for vehicular applications that have extremely high requirements of latency and reliability. However, the high mobility of vehicles poses significant challenges to resource management in such a stochastic vehicular environment with time-varying service demands. In this paper, we develop an online network slicing scheduling strategy for joint resource block (RB) allocation and power control in vehicular networks. The long-term time-averaged total system capacity is maximized while guaranteeing strict ultra-reliable and low-latency requirements of vehicle communication links, subject to stability constraints of task queues. The formulated problem is a mixed integer nonlinear stochastic optimization problem, which is decoupled into three subproblems by leveraging Lyapunov optimization. In order to tackle this problem, we propose an online algorithm, namely JRPSV, to obtain the optimal RB allocation and power control at each time slot according to the current network state. Furthermore, rigorous theoretical analysis is conducted for the proposed JRPSV algorithm, indicating that the system capacity and the system average latency obey a $[ {{\mathcal O}({1/V}),{\mathcal O}(V)} ]$ trade-off with the control parameter $V$. Extensive simulation results are provided to validate the theoretical analysis and demonstrate the effectiveness of JRPSV as well as the impacts of various parameters. [ABSTRACT FROM AUTHOR]

Tracking the temporal evolution of features in time-varying data is a key method in visualization. For typical feature definitions, such as vortices, objects are sparsely distributed over the data domain. In this paper, we present a novel approach for tracking both sparse and space-filling features. While the former comprise only a small fraction of the domain, the latter form a set of objects whose union covers the domain entirely while the individual objects are mutually disjunct. Our approach determines the assignment of features between two successive time-steps by solving two graph optimization problems. It first resolves one-to-one assignments of features by computing a maximum-weight, maximum-cardinality matching on a weighted bi-partite graph. Second, our algorithm detects events by creating a graph of potentially conflicting event explanations and finding a weighted, independent set in it. We demonstrate our method's effectiveness on synthetic and simulation data sets, the former of which enables quantitative evaluation because of the availability of ground-truth information. Here, our method performs on par or better than a well-established reference algorithm. In addition, manual visual inspection by our collaborators confirm the results’ plausibility for simulation data. [ABSTRACT FROM AUTHOR]

The paper proposes a novel framework based on the contract theory and Lyapunov optimization for content sharing in a wireless content delivery network (CDN) with edge caching and device-to-device (D2D) communications. The network is partitioned into a set of clusters. In a cluster, users can share contents via D2D links in coordination with the cluster head. Upon receiving the content request from any user in its cluster, the cluster head either delivers the content itself or forwards the request to another node, i.e., a base station (BS) or another user in the cluster. The content access at the BS and in each cluster is modeled as a queuing system, where arrivals represent the content requests directed to respective nodes. The objective is to assign content delivery nodes to stabilize all queues while minimizing the time-averaged network cost given incomplete information about content sharing costs of the users and unknown distribution of the network state defined by users’ locations and their cached/requested content. The proposed framework allows the users to truthfully reveal their content sharing expenditures, minimize the time-averaged network cost and stabilize the queuing system representing the CDN. Based on this framework, a distributed content access and delivery algorithm where the node assignments are made by every cluster head independently is developed. It is shown that the algorithm converges to the optimal policy with the trade-off in total queue backlog and achieves a superior performance compared with some other D2D content sharing policies. [ABSTRACT FROM AUTHOR]

Multiuser video transmission, where the server transmits videos to multiple users that require different contents at the same time, becomes more and more popular with the development of wireless communication technology. One key problem in multiuser video transmission is how to optimally allocate system resources such as transmission power and channels to multiple users to achieve the best system performance. To resolve the problem, in this paper, we propose an uncoded multiuser video streaming system, which exploits diversities of video contents and channel conditions of multiple users. We first solve the channel assignment problem with known power allocation by taking into account the intra-block energy diffusion and inter-block energy aliasing. Then, with the obtained channel assignment, we derive a closed-form solution to the multiuser power allocation optimization problem. Finally, we conduct simulations to evaluate the proposed uncoded multiuser video streaming system by comparing with three other approaches, and the simulation results show that the proposed method can achieve the best system performance. [ABSTRACT FROM AUTHOR]

This paper reports on an application of network-flow integer programming to a vacation timeshare exchange problem. A typical timeshare owner has purchased yearly access to a specific week at a specific resort. The resulting lack of vacation variety is mitigated by systems that allow owners to exchange owned weeks for different weeks at different resorts according to their preferences, the assessed value of what they are exchanging, their contractual priority, and resort availability. The timeshare exchange problem is similar to other preference-based assignment problems such as labor scheduling, preferential bidding, and traditional timetabling, but different in the formulation of the objective function. This paper demonstrates how the effectiveness of timeshare exchange processes can be improved through mathematical optimization, as measured by increased satisfaction of participant preferences. Optimization also presents exchange managers with the opportunity to more precisely manage preference and priority trade-offs among various classes of participants. The trade-off decisions are aided by sensitivity analysis utilizing a minmax criterion. [ABSTRACT FROM AUTHOR]

In wireless network virtualization, guaranteeing service contracts with different mobile virtual network operators (MVNOs) and optimizing energy efficiency are crucial for the success of the virtualization scheme deployed by an infrastructure provider (InP). In this paper, a novel design framework is proposed for resource allocation in an OFDMA virtualized wireless network (VWN). Treating the virtual resources for a VWN as commodities, the InP wants to maximize its revenue by leasing the infrastructure and resources to the MVNOs while meeting certain contract agreements. Moreover, MVNOs want to serve their users at the best performance and pay the minimum cost to the InP. A Lyapunov based online algorithm is proposed to solve the InP's long-term optimization problem. The short-term optimization problem of the InP is considered as a combinatorial nonconvex problem. A multiple time-scale framework is proposed to solve the optimization problem of the InP, which decomposes the pricing decision, base station assignment, and resource allocation into different time-scale algorithms to achieve the design objectives. First, a distributed matching based algorithm is proposed to solve the base station assignment problem. Second, we propose a successive convex approximation approach to solve the joint subchannel assignment and energy efficiency problem. Finally, we propose a branch and bound based algorithm to optimally solve the price decision problem. Simulation results show the trade-off between energy efficiency, InP's revenue, and the isolation provisioning. [ABSTRACT FROM AUTHOR]

In this paper, a novel system, denoted as MPLM (Multi Process/Product Line Management), for production goaling and control of semiconductor manufacturing lines is outlined. Goaling is the process of allocating targets on amount of work (wafer lots) to be produced by each workstation (tool-set), at each of its process steps, over a tactical planning horizon (e.g. one shift). In MPLM, the production goaling problem is represented as a series of sequential allocation problems of assigning lots to tools. Each of these sequential allocation problems is then converted into a maximal efficiency flow problem and solved optimally, for a parametric planning horizon, and consequently aggregated into a legible and detailed manufacturing plan. This plan is integrated into a real-time shop-floor dispatching system for execution of the plan seamlessly. After the successful implementation of MPLM in several semiconductor manufacturing facilities at Intel, significant performance benefits were recorded relative to previously used methods for production goaling and WIP management. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) is a well-known paradigm for data representation. Traditional NMF-based classification methods first perform NMF or one of its variants on input data samples to obtain their low-dimensional representations, which are successively classified by means of a typical classifier [e.g., $k$ -nearest neighbors (KNN) and support vector machine (SVM)]. Such a stepwise manner may overlook the dependency between the two processes, resulting in the compromise of the classification accuracy. In this paper, we elegantly unify the two processes by formulating a novel constrained optimization model, namely dual embedding regularized NMF (DENMF), which is semi-supervised. Our DENMF solution simultaneously finds the low-dimensional representations and assignment matrix via joint optimization for better classification. Specifically, input data samples are projected onto a couple of low-dimensional spaces (i.e., feature and label spaces), and locally linear embedding is employed to preserve the identical local geometric structure in different spaces. Moreover, we propose an alternating iteration algorithm to solve the resulting DENMF, whose convergence is theoretically proven. Experimental results over five benchmark datasets demonstrate that DENMF can achieve higher classification accuracy than state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes an effective ensemble framework (EF) for tackling multiobjective optimization problems, by combining the advantages of various evolutionary operators and selection criteria that are run on multiple populations. A simple ensemble algorithm is realized as a prototype to demonstrate our proposed framework. Two mechanisms, namely competition and cooperation, are employed to drive the running of the ensembles. Competition is designed by adaptively running different evolutionary operators on multiple populations. The operator that better fits the problem’s characteristics will receive more computational resources, being rewarded by a decomposition-based credit assignment strategy. Cooperation is achieved by a cooperative selection of the offspring generated by different populations. In this way, the promising offspring from one population have chances to migrate into the other populations to enhance their convergence or diversity. Moreover, the population update information is further exploited to build an evolutionary potentiality model, which is used to guide the evolutionary process. Our experimental results show the superior performance of our proposed ensemble algorithms in solving most cases of a set of 31 test problems, which corroborates the advantages of our EF. [ABSTRACT FROM AUTHOR]

This paper investigates a buffer-aided cooperative NOMA system, where a source is connected to multiple destinations through a buffer-aided NOMA relaying. We aim to maximize a long-term average network utility by jointly optimizing working mode selection, admission control, and power allocation in each time slot, subject to queue stability constraints and both the peak and average power constraints at the relay. To solve this problem, by leveraging the recently-developed Lyapunov optimization framework, we design an Online Adaptive Transmission (OAT) strategy, which converts the original long-term optimization problem into a series of online admission control and power allocation subproblems in each time slot, without loss of optimality. By considering the fact that the power allocation problem is non-convex in nature and thus cannot be solved with a standard method, we transform it into an equivalent convex optimization based on the specific feature of the formulation, and then propose a Fast Decision Criteria (FDC) based algorithm to derive the optimal solution with high computational efficiency. Extensive simulation results are provided to evaluate the performance of the proposed OAT strategy with FDC based power allocation for the buffer-aided cooperative NOMA system, and demonstrate that the proposed strategy can significantly outperform multiple benchmark schemes. [ABSTRACT FROM AUTHOR]