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Intelligent generating test paper can be thought of as a typical fuzzy mathematical logic problem. First, the paper analyzes the quantitative evaluation criteria of the traditional test paper generation questions and shows the existing problems in practical application of it. Then the paper introduces two different fuzzy constraints principal disjunctive normal form concept and proposes an evaluation system combining quantitative with qualitative. Viewing at the particularity of constraint satisfaction problems of intelligent generating test paper, the paper uses heuristic knowledge and applies constraint satisfaction method combining generation test with membership correction constraint satisfaction methods to realize intelligent generating test paper. The experiment shows that this method obtains good results. [ABSTRACT FROM AUTHOR]

Dominance breaking is a powerful technique in improving the solving efficiency of Constraint Optimization Problems (COPs) by removing provably suboptimal solutions with additional constraints. While dominance breaking is effective in a range of practical problems, it is usually problem specific and requires human insights into problem structures to come up with correct dominance breaking constraints. Recently, a framework is proposed to generate nogood constraints automatically for dominance breaking, which formulates nogood generation as solving auxiliary Constraint Satisfaction Problems (CSPs). However, the framework uses a pattern matching approach to synthesize the auxiliary generation CSPs from the specific forms of objectives and constraints in target COPs, and is only applicable to a limited class of COPs. This paper proposes a novel rewriting system to derive constraints for the auxiliary generation CSPs automatically from COPs with nested function calls, significantly generalizing the original framework. In particular, the rewriting system exploits functional constraints flattened from nested functions in a high-level modeling language. To generate more effective dominance breaking nogoods and derive more relaxed constraints in generation CSPs, we further characterize how to extend the system with rewriting rules exploiting function properties, such as monotonicity, commutativity, and associativity, for specific functional constraints. Experimentation shows significant runtime speedup using the dominance breaking nogoods generated by our proposed method. Studying patterns of generated nogoods also demonstrates that our proposal can reveal dominance relations in the literature and discover new dominance relations on problems with ineffective or no known dominance breaking constraints. [ABSTRACT FROM AUTHOR]

This paper presents a robust decoupling Model Predictive Controller (MPC) for constrained linear systems with bounded disturbances. The usual explicit decouplers are combined with a suitable augmented state–space representation in order to ensure recursive feasibility, robust constraint satisfaction, and cross-coupling reduction during set-point changes. The proposed approach is flexible with respect to the decoupler choice and the robust MPC algorithm. A robust MPC for tracking piecewise constant references with an artificial target based on nominal prediction is proposed in order to achieve offset-free piecewise constant reference tracking. Two case studies are presented to illustrate the effectiveness of the proposed decoupling strategy. • A robust decoupling MPC for tracking piecewise constant references has been proposed. • A new augmented model is used to provide a decoupled set-point tracking response. • Recursive feasibility and constraint satisfaction are ensured despite disturbances. [ABSTRACT FROM AUTHOR]

This paper proposes an active fault-tolerant control (FTC) approach based on the controller management and virtual actuator idea for linear discrete-time systems subject to unknown L 2 -bounded disturbances, input constraint, and time-varying additive actuator faults. The closed-loop faulty system, which includes the modified nominal controller, the fault and state estimator, and the virtual actuator, suppresses the effects of disturbances and faults, while ensuring input-constraint satisfaction. The management of the nominal controller is performed through an online optimization method – in the form of a standard quadratic programming problem – by manipulating the reference input and intervening in the nominal controller evolution. The proposed method proves the input-to-state stability (ISS) criterion of the overall closed-loop faulty system. The problem of minimizing the ultimate bound of the ISS criterion is formulated in terms of tractable linear matrix inequality (LMI) conditions that allow the fault and state estimation errors to converge to a small neighborhood of the origin. To illustrate the capabilities and advantages of the proposed control strategy, comparative simulation results are presented for a flexible joint robotic system, tracking control of a DC motor's angular velocity, and the multivariable VTOL aircraft. • Proposing a novel controller management scheme for active observer-based fault-tolerant tracking with the capability of suppressing the effect of fault and disturbance and input constraint satisfaction. • Using linear matrix inequality tools to assure input-to-state stability with minimized ultimate bound for closed-loop disturbed faulty system. • Introducing a new approach called as "MPC-based ROG-like unit" for input constraint satisfaction in the presence of fault and L 2 bounded disturbances. • Considering different performance indicators including input-to-state stability, decay rate, to achieve fast convergence and efficiency in transient and steady-state behavior. [ABSTRACT FROM AUTHOR]

This paper proposes a method for automatically monitoring and analyzing the evolution of complex geographic objects. The objects are modeled as a spatiotemporal graph, which separates filiation relations, spatial relations, and spatiotemporal relations, and is analyzed by detecting frequent sub-graphs using constraint satisfaction problems (CSP). The process is divided into four steps: first, the identification of complex objects in each satellite image; second, the construction of a spatiotemporal graph to model the spatiotemporal changes of the complex objects; third, the creation of sub-graphs to be detected in the base spatiotemporal graph; and fourth, the analysis of the spatiotemporal graph by detecting the sub-graphs and solving a constraint network to determine relevant sub-graphs. The final step is further broken down into two sub-steps: (i) the modeling of the constraint network with defined variables and constraints, and (ii) the solving of the constraint network to find relevant sub-graphs in the spatiotemporal graph. Experiments were conducted using real-world satellite images representing several cities in Saudi Arabia, and the results demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Constraint satisfaction problem (CSP) has been actively used for modeling and solving a wide range of complex real-world problems. However, it has been proven that developing efficient methods for solving CSP, especially for large problems, is very difficult and challenging. Existing complete methods for problem-solving are in most cases unsuitable. Therefore, proposing hybrid CSP-based methods for problem-solving has been of increasing interest in the last decades. This paper aims at proposing a novel approach that combines incomplete and complete CSP methods for problem-solving. The proposed approach takes advantage of the group search algorithm (GSO) and the constraint propagation (CP) methods to solve problems related to the remote sensing field. To the best of our knowledge, this paper represents the first study that proposes a hybridization between an improved version of GSO and CP in the resolution of complex constraint-based problems. Experiments have been conducted for the resolution of object recognition problems in satellite images. Results show good performances in terms of convergence and running time of the proposed CSP-based method compared to existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• A dynamic task mechanism is designed to improve the generality of the algorithm. • The main task processes constraints with higher constraint priority in turn. • Auxiliary task P 2 stops the evolution adaptively after converging to UPF. • The entire solution process is divided into exploration and exploitation stages. Compared with common multi-objective optimization problems, constrained multi-objective optimization problems demand additional consideration of the treatment of constraints. Recently, many constrained multi-objective evolutionary algorithms have been presented to reconcile the relationship between constraint satisfaction and objective optimization. Notably, evolutionary multi-task mechanisms have also been exploited in solving constrained multi-objective problems frequently with remarkable outcomes. However, previous methods are not fully applicable to solving problems possessing all types of constraint landscapes and are only superior for a certain type of problem. Thus, in this paper, a novel dynamic-multi-task-assisted constrained multi-objective optimization algorithm, termed DTCMO, is proposed, and three dynamic tasks are involved. The main task approaches the constrained Pareto front by adding new constraints dynamically. Two auxiliary tasks are devoted to exploring the unconstrained Pareto front and the constrained Pareto front with dynamically changing constraint boundaries, respectively. In addition, the first auxiliary task stops the evolution automatically after reaching the unconstrained Pareto front, avoiding the waste of subsequent computational resources. A series of experiments are conducted with eight mainstream algorithms on five benchmark problems, and the results confirm the generality and superiority of DTCMO. [ABSTRACT FROM AUTHOR]

Using infeasible information to balance objective optimization and constraint satisfaction is a very promising research direction to address constrained multi-objective problems (CMOPs) via evolutionary algorithms (EAs). The existing constrained multi-objective evolutionary algorithms (CMOEAs) still face the issue of striking a good balance when solving CMOPs with diverse characteristics. To alleviate this issue, in this paper we develop a two-stage different evolution with a dynamic population assignment strategy for CMOPs. In this approach, two cooperative populations are used to provide feasible driving forces and infeasible guiding knowledge. To adequately utilize the infeasibility information, a dynamic population assignment model is employed to determine the primary population, which is used as the parents to generate offspring. The entire search process is divided into two stages, in which the two populations work in weak and strong cooperative ways, respectively. Furthermore, multistrategy-based differential evolution operators are adopted to create aggressive offspring. The superior exploration and exploitation ability of the proposed algorithm is validated via some state-of-the-art CMOEAs over artificial benchmarks and real-world problems. The experimental results show that our proposed algorithm gained a better, or more competitive, performance than the other competitors, and it is an effective approach to balancing objective optimization and constraint satisfaction. [ABSTRACT FROM AUTHOR]

In this paper, the utilization of inverse-optimality-based prescribed-time explicit reference governor is investigated for missile intercepting against unknown maneuvering targets under performance and control input constraints. With an arctangent-based disturbance observer equipped for disturbance elimination, incorporating the inverse optimality approach into the missile interception guarantees the minimization of a performance index. In the framework of prescribed-time explicit reference governor, the control constraint is translated into a restriction of time-varying invariant set, and it follows the prescribed-time regulation of the applied reference along with the restriction satisfaction. The combined prescribed-time explicit reference governor approach could be transformed into a linear matrix inequality optimization problem, and its online solution over a receding horizon gives a Lyapunov function value for reference regulation and then control decisions in the continuous time. Simulation studies are performed to illustrate the performance of the proposed guidance control law. • Arctangent-based disturbance observer compensates lumped external disturbances for better performance. • Inverse optimality guidance approach guarantees the minimization of a performance index. • Prescribed-time ERG generates a time-varying invariant set for constraint satisfaction. • The online solution of LMI gives a Lyapunov function value for reference regulation. [ABSTRACT FROM AUTHOR]

The control problem of the motor driven joint system under the state and input constraints is discussed in this paper. Firstly, a state transform function is introduced to transfer the state-constrained motor driven joint system to a transformed system, which no longer has the state constraints. Secondly, an adaptive robust control (ARC) with the specified performance bounds is proposed for this transformed system, where the ARC algorithm combined with an auxiliary variable are used to ensure the semi-globally uniformly ultimately bounded of all the closed-loop signals, and a time-varying barrier Lyapunov function (BLF) is designed to constrain all the tracking errors within the specified performance bounds. Thirdly, the above results are extended to the motor driven joint system. Namely, the boundedness of the states in the transformed system are converted into the satisfaction of the state constraints in the motor driven joint system, and the fast transient response and high steady-state tracking accuracy can be achieved by designing the appropriate specified performance bounds in the time-varying BLF. Finally, a simulation is carried out, and the results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Striking a balance between diverse constraints and conflicting objectives is one of the most crucial issues in solving constrained multi-objective optimization problems (CMOPs). However, it remains challenging to existing methods, due to the reduced search space caused by the constraints. For this issue, this paper proposes a Pareto fronts relationship identification-based two-stage constrained evolutionary algorithm called RITEA, which balances objective optimization and constraint satisfaction by identifying and utilizing the relationship between the unconstrained Pareto front (UPF) and the constrained Pareto front (CPF). Specifically, the evolutionary process is divided into two collaborative stages: training stage and reinforcement stage. In the training stage, a relationship identification method is developed to estimate the relationship between UPF and CPF, which guides the population search direction. In the reinforcement stage, the corresponding evolutionary strategies are designed based on the identified relationship to enhance the accurate search on the CPF. Furthermore, a dynamic preference fitness function (termed DPF) is designed to adaptively maintain the balance of search preference between convergence and diversity. Compared to seven state-of-the-art algorithms on 36 benchmark CMOPs in three popular test suites, RITEA obtains 77.8% of the best IGD values and 66.7% of the best HV values. The experimental results show that RITEA exhibits highly competitively when dealing with CMOPs. [Display omitted] • RITEA, a two-stage constrained evolutionary algorithm for CMOPs. • Identify and utilize the relationship between UPF and CPF to balance optimization and constraint satisfaction. • Utilizes collaborative training stage and reinforcement stage to guide population search accurately. • Introduction of a dynamic preference fitness function to adaptively balance the search preferences between convergence and diversity. [ABSTRACT FROM AUTHOR]

• Communication-free protection of MGs/SGs, using non-standard relay characteristics. • Considering N-1 contingency configurations and different operation modes. • Facilitating coordination constraints satisfaction and speed up the protection system. • Linearizing and solving the proposed COP using GA-PSO-LP and WCA-MFO-LP algorithm. • Around a 36 % improvement in the speed of the proposed scheme compared to the available ones. Smart grids (SGs), meshed active distribution networks (ADNs), and microgrids (MGs) frequently experience reconfigurations and changes in their operation modes (grid-connected and islanded), which leads to protection miscoordination and speed challenges. Several research works have studied the protection of MGs/SGs, considering different configurations. On the other hand, non-standard relay characteristics provide some flexibility in protection design. However, the literature review shows that less attention has been paid to the optimal protection of MGs/SGs, incorporating non-standard relay curves and consideration of N-1 contingency configurations and operation modes. This paper aims to respond to this research gap. Also, the slow tripping of backup relays, a common drawback of recent works, is concerned with a modified objective function (OF). The presented coordination optimization problem (COP) is formulated in a mixed-integer nonlinear programming-linear programming (MINLP-LP) form and solved using hybrid heuristic-linear programming algorithms: Genetic algorithm-particle swarm optimization-linear programming (GA-PSO-LP) and water cycle algorithm-moth flame optimization-linear programming (WCA-MFO-LP). The introduced research is applied to the distribution portion of the IEEE-14 test system. The analysis and discussions emphasize that the proposed protection scheme speed is improved by over 36 % compared to available ones. Also, there is no coordination violation under different system configurations. [ABSTRACT FROM AUTHOR]

The Cognitive Radio Network, incorporating Device-to-Device communication and a heterogeneous network, has garnered significant attention due to its ability to address spectrum shortage issues and optimize the efficient utilization of spectrum resources. However, resource allocation considering stochastic behavior has not been considered in existing studies. In this paper, our work aimed to maximize the throughput of the overall network considering multiple users under the umbrella of the Cognitive radio network assisted by amplify and forward relay. The constraints are treated as chance constraints with a probability of satisfaction in them, which leads to a non-convex mixed integer nonlinear problem which is an NP-Hard problem. To solve this problem an exhaustive search solution for optimal results is required. However, the computational burden always increases with the user equipment. Therefore, to obtain an optimal solution with having low computational burden, the Outer Approximation Algorithm is utilized in this research. To evaluate the desired results, extensive simulations have been carried out. The effectiveness of the proposed algorithm is verified by results in terms of throughput maximization under the impact of chance constraint formulation in the cognitive radio networks. [ABSTRACT FROM AUTHOR]

There is a wide number of application problems included in multi-agent systems and distributed artificial intelligence which can be described as the issue of finding a consistent combination satisfying all the existing inter-agent constraints, that is, they are distributed constraint satisfaction problems (DisCSPs). In this paper, a new technique for solving such kind of issues when they can be modelled as a succession of interrelated elements with constraints among an agent and its immediately following, having one of the agents the ability to notify either that the problem has been solved or that there are not any possible solutions, is presented. It has been developed using Asynchronous Backtracking, one of the most known algorithms for solving DisCSPs as a basis. To validate its applicability, it has been tested in a wire rod rolling mill for rolls replacement decision making process. It was implemented using the PADE multi-agent framework. The results demonstrate the method is valid to solve this kind of problems usually solved with recursive algorithms such as backtracking. [ABSTRACT FROM AUTHOR]

This paper introduces a new distributed approach to solve multi-objective planning problem applied to multimodal transport network planning (MTNP) problem. In this problem, the commodities should be transported on the international network by at least two different transport modes. The main goal is to find the best multimodal transportation modes and itineraries. The aim of the new approach has assured us that a distributed optimization. We split the MTNP problem into two sub-problems. These sub-problems are the assignment and the planning problems. Each sub-problem is solved at a corresponding layer. Each layer is executed by an agent. These agents interact, collaborate and communicate together to solve the MTNP problem. In this paper, we contribute by introducing a multi-layer distributed approach to solve real case's problems. Firstly, we define the MTNP problem as a distributed constraint satisfaction multi-criteria optimization problem (DCSMOP). Secondly, we show that the split of the main problem reduces the computational complexity and the communication between the planner agent and the modes agents lead to faster convergence. The experimental results are proof of this work efficiently. This method proves their efficiency, according to the complexity of the problem and the exchange of information, the computational time and the solution quality. [ABSTRACT FROM AUTHOR]

Extended Partial Dynamic Backtracking (EPDB) is a repair algorithm based on PDB. It deals with Dynamic CSPs based on ordering heuristics and retroactive data structures, safety conditions, and nogoods which are saved during the search process. In this paper, we show that the drawback of both EPDB and PDB is the exhaustive verification of orders, saved in safety conditions and nogoods, between variables. This verification affects remarkably search time, especially since orders are often indirectly deduced. Therefore, we propose a new approach for dynamically changing environments, the Lazy Repairing Backtracking (LRB), which is a fast version of EPDB insofar as it deduces orders directly through the used ordering heuristic. We evaluate LRB on various kinds of problems, and compare it, on the one hand, with EPDB to show its effectiveness compared to this approach, and, on the other hand, with MAC-2001 in order to conclude, from what perturbation rate resolving a DCSP with an efficient approach can be more advantageous than repair. [ABSTRACT FROM AUTHOR]

In the last few years, there has been an increasing interest in Random Constraint Satisfaction Problems (CSP) from both experimental and theoretical points of view. To consider a variant instance of the problems, we used a random benchmark. In the present paper, some work has been done to find the shortest Hamiltonian circuit among specified nodes in each superimposed graph (SGs). The Hamiltonian circuit is a circuit that visits each node in the graph exactly once. The Hamiltonian path may be constructed and adjusted according to specific constraints such as time limits. A new constraint satisfaction optimization problem model for the circuit Hamiltonian circuit problem in a superimposed graph has been presented. To solve this issue, we propose amelioration for the genetic algorithm using Dijkstra's algorithm, where we create the improved genetic algorithm (IGA). To evaluate this approach, we compare the CPU and fitness values of the IGA to the results provided by an adapted genetic algorithm to find the shortest Hamiltonian circuit in a superimposed graph. [ABSTRACT FROM AUTHOR]

within constraint satisfaction problems (CSPs), in this paper we introduce a honeybee algorithm that uses new template concept for image segmentation. Our approach uses the guided local search and the graph theory to achieve an improved image segmentation. Compared to previous well known heuristics, our approach is giving better experimental results. [ABSTRACT FROM AUTHOR]

within constraint satisfaction problems (CSPs), our work proposes a new search heuristic. The latter is basically inspired by the template concept introduced by E.P.K. TSANG. It consists of a new template concept and could be classified as a min-conflict heuristic. In this paper we introduce a new guided local search that uses our heuristic in local search (LS) for Maximal constraint satisfaction problems (Max-CSPs). Compared to previous well known guided local search, our approach changes experimental results for the better. [ABSTRACT FROM AUTHOR]

Web Service Composition has been studied intensively in the past two decades, considering the many aspects of the problem, like: semantic modeling, general QoS metrics, services state, and much more. In this paper, our focus is on integrating throughput metric with the automation formalism, without any limitation on automation, i.e. allowing full automation with meaningful throughput propagation. The user request and each service in the respiratory are described by their throughput values that are: an extra requirement for the request; and a maximum allowed load for services. In the proposed version, it is possible that to satisfy a request's throughput, multiple traditional compositions are used concurrently, adding up for the required throughput. This highly increases the problem complexity both in how to model the combining throughput values, and also computationally, as the algorithms are required to search for multiple compositions. However, we also propose an algorithm that reduces the original problem to multiple constraint satisfaction problem instances and can find compositions for requests and considerable-sized repositories, which we prove through a synthetic evaluation. [ABSTRACT FROM AUTHOR]

This paper introduces the multi-objective cuckoo search (MOCS) method for solving the multi-objective optimal power flow (MOOPF) which is a multi-variable, multi-constraint and nonlinear programming problem. In order to speed up convergence and enhance the quality of solution, the quasi-opposition based learning mechanism is adopted to propose the multi-objective quasi-oppositional cuckoo search (MOQOCS) algorithm in this paper. In MOCS and MOQOCS methods, the feasibility-prior domination principle (FDP) is presented to ensure the feasibility of simulation result which is based on the objective values and the sum of constraint violation values. The crowding-distance sorting is considered to enhance the diversity of Pareto-optimal solutions and obtain better distributed Pareto optimal front. IEEE 30-bus and IEEE 57-bus systems have been considered to check the performance of the proposed methods. The simulation results confirm that MOQOCS algorithm obtains superior compromise solution and produces better distributed Pareto optimal front compared to other methods. [ABSTRACT FROM AUTHOR]

Media, image processing, and geometric-based systems and applications need data structures to model and represent different geometric entities and objects. These data structures have to be time efficient and compact in term of space. Many structures in use are proposed to satisfy those constraints. This paper introduces a novel compact data structure inspired by the XOR-linked lists. The subject of this paper concerns the triangular data structures. Nevertheless, the underlying idea could be used for any other geometrical subdivision. The ability of the bitwise XOR operator to reduce the number of references is used to model triangle and vertex references. The use of the XOR combined references needs to define a context from which the triangle is accessed. The direct access to any triangle is not possible using only the XOR-linked scheme. To allow the direct access, additional information are added to the structure. This additional information permits a constant time access to any element of the triangulation using a local resolution scheme. This information represents an additional cost to the triangulation, but the gain is still maintained. This cost is reduced by including this additional information to a local sub-triangulation and not to each triangle. Sub-triangulations are calculated implicitly according to the catalog-based structure. This approach could be easily extended to other representation models, such as vertex-based structures or edge-based structures. The obtained results are very interesting since the theoretical gain is estimated to 38% and the practical gain obtained from sample benches is about 34%. [ABSTRACT FROM AUTHOR]

This paper is concerned with the control of linear systems with switched actuators subject to lower bounds on dwell times. The problem addressed herein consists of driving the system state to a desired periodic trajectory through suitable perturbations in the switching times. For this purpose, a linearization procedure is derived to describe the relationship between the switching-time perturbations and the resulting state trajectory. This procedure allows the constrained predictive control problem to be cast in the form of a convex quadratic program, which is a key contribution of the present work. Simulation examples with single-input and two-input models are presented for illustration. In all cases, the system state is driven to the desired trajectory with satisfaction of the dwell-time constraints on the switched input signal. [ABSTRACT FROM AUTHOR]

The relative state constraints is inevitable in multi-agent systems(MASs) because of limited sensing capabilities of the sensors. In this paper, the consensus problem of nonlinear MASs with relative state constraints is investigated in an edge perspective. First, the consensus problem is proved to be equivalent to a stabilization problem characterized by the edge dynamics. Then the edge system is confined to a hypercube for the satisfaction of relative constraints. Finally, an output event-triggered control protocol that can exponentially stabilize the edge dynamics with constraints is proposed. Sufficient conditions on system stability are provided, the Singular triggering and the Zeno-behaviour are also excluded. A simple learning-based iterative algorithm is proposed to calculate the diagonally dominant matrix, which is crucial to the controller gain design. The effectiveness of the results is illustrated through numerical examples. • Edge Laplacian framework is used to transform the consensus problem with relative state constraints into stabilization problem with edge dynamics, and being exponentially stabilized by the ETC protocol. • Singular-triggering and Zeno-behaviour will not occur under the edge-based framework. • An iterative-learning algorithm is designed to compute the diagonally dominant matrix. • The proposed method has been extended to solve the consensus problem with connectivity preservation. [ABSTRACT FROM AUTHOR]

In this paper the proposed constrained gravitational search algorithm (CGSA) is extended and used to solve multi-reservoir operation optimization problem. Tow constrained versions of GSA named partially constrained GSA (PCGSA) and fully constrained GSA (FCGSA) are outlined to solve this optimization problem. In the PCGSA, the problem constraints are partially satisfied, h owever, in the FCGSA, all the problem constraints are implicitly satisfied by providing the search space for each agent which contains only feasible solution and hence leading to smaller search space for each agent. These proposed constrained versions of GSA are very useful when they are applied to solve large scale multi-reservoir operation optimization problem. The constrained versions of GSA are formulated here for both possible variables of the problem means considering water release or storage volumes as the decision variables of the problem and therefore first and second formulations of these algorithms are proposed. The proposed algorithms are used to solve the well-known four and ten reservoir operation optimization problems and the results are presented and compared with those of original form of the GSA and any available results in the literature. The results indicate the superiority of the proposed algorithms and especially FCGSA over existing methods to optimally solve large scale multi-reservoir operation optimization problem. [ABSTRACT FROM AUTHOR]

Boundary layers generation has recently become one of the most sought-after features in mesh generation. But behind this single feature hides a lot of different capacities that range from the most basic, like extruding a layer from a surface, to imprinting, cross- imprinting and multi-normals capabilities that are much more challenging to develop. This paper aims at implementing all those features using hexahedral elements only and is based on a previously developed automated hexahedral mesh generator based on the octree method. Both points, octree and hexahedra, implying some constraints that this paper will address in a very pragmatic way. Indeed, we view automatic meshing from the industrial user's point of view: no mesh, no run , consequently this work's philosophy has been always to favor automaticity and genericity over element's quality and geometric approximation. [ABSTRACT FROM AUTHOR]

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The priority ascent strategy of feasibility judgment and soft constraint adjustment has been playing a significant role in two-layer model predictive control for decades of years. Nevertheless, our latest researches indicate the present priority ascent strategy is incomplete in some extent, which could have been repaired through some technical adjustment. Therefore, an augmented method is proposed to fundamentally settle the problem in the paper. Firstly, a special case is presented to verify the incompleteness of the present strategy. Secondly, some existing methods which can partly solve the problem are carried out and discussed. Then, an augmented priority ascent strategy which considers all hard constraints in advance is proposed to solve the incompleteness of the present strategy. Furthermore, a brief proof is given to demonstrate the completeness of the proposed approach. Finally, some simulations are discussed to indicate its feasibility and effectiveness. • The incompleteness of the present priority ascent strategy is first discovered. • An augmented priority ascent strategy is proposed to solve the incompleteness. • A brief proof is carried out to prove the completeness of the proposed method. [ABSTRACT FROM AUTHOR]

Control of Solid Oxide Fuel Cells (SOFCs) is a challenging task since they are nonlinear dynamic systems and it is essential to precisely satisfy the existing technological constraints which must be imposed on the manipulated variable (the fuel flow) and on fuel utilisation. This paper details a constrained computationally efficient nonlinear Model Predictive Control (MPC) algorithm for the SOFC process. The predicted voltage and fuel utilisation trajectories are successively linearised on-line which leads to a simple to solve quadratic optimisation MPC problem. The emphasis is put on three aspects: (a) selection of tuning parameters, (b) feasibility of the constrained MPC optimisation problem, (c) good control quality and low computational burden. At first, tuning is thoroughly described. It is demonstrated that soft fuel utilisation constraints lead to feasible MPC optimisation. It is shown that control accuracy and constraints' satisfaction ability of the algorithm are very similar to those of the "ideal" MPC strategy with nonlinear on-line optimisation, but its computational burden is much lower. Finally, it is shown that the algorithm is much more precise than the simple MPC algorithm with successive on-line model linearisation and the classical MPC algorithm based on a parameter-constant linear model. • Nonlinear Model Predictive Controller (MPC) for a solid oxide fuel cell is described. • The constraints imposed on fuel flow and fuel utilisation are taken into account. • On-line trajectory linearisation is used which leads to simple quadratic optimisation. • Accuracy of the algorithm is the same as that of MPC with nonlinear optimisation. • The classical MPC algorithms give much worse results. [ABSTRACT FROM AUTHOR]

This paper deals with the design of a switched Nonlinear Model Predictive Controller (NMPC) for collaborative ecodrive control of railway vehicles. Relying on a discrete, switched and nonlinear model of the train, the NMPC optimizes the handle position while fulfilling constraints on velocity and journey time. Specifically, the optimizer provides a set of operating modes, which the human driver is able to implement to modulate traction or braking forces and such that the corresponding driving style is constrained by predefined driving sequences. At network level, a Dissension based Adaptive Law (DAL) is then proposed to adjust the parameters of the NMPC cost so as to efficiently share the available regenerated braking energy among the trains connected to the same substation, while negotiating between constraint satisfaction and control aggressiveness. The effectiveness of the proposed strategy is finally demonstrated on a realistic simulation case study. [ABSTRACT FROM AUTHOR]

This paper addresses the control problem of Timed Event Graphs (TEGs) with disturbance transitions, which are subject to marking constraints. In these graphical models, disturbances are uncontrollable input transitions and constraints correspond to the maximum limits of tokens in some places which must not be exceeded. An analytical methodology based on the use of Min-Plus formalisms to synthesize feedback control laws that guarantee the concerned constraints is established. For this, Min-Plus linear models describe the dynamic behavior of the TEGs, and marking constraints are represented by inequalities in Min-Plus algebra. Sufficient conditions are defined to verify the existence of feedback control laws guaranteeing constraint satisfaction in disturbed TEGs. To verify the effectiveness of these proposed theoretical approaches, a real case study of a disturbed disassembly system with limited stocks of components is processed. [ABSTRACT FROM AUTHOR]

The paper discusses the problem of the induction of a minimal nondeterministic finite automaton (NFA) consistent with a given set of examples and counterexamples. The main contribution of the paper is the proposal of an efficient parallel algorithm transforming the NFA induction problem into a family of constraint satisfaction problems (CSP). Two original techniques for fast CSPs evaluation are proposed and discussed. The efficacy of the parallel algorithm is evaluated experimentally on selected benchmarks. The proposed algorithm solves all analyzed benchmark examples, so called Tomita languages, in the time below half a minute, which should be considered an important achievement, as compared to our previous efforts which required minutes or hours to solve some of the aforementioned benchmarks. [ABSTRACT FROM AUTHOR]

Requirement Engineering demands a granular level of requirement specifications with key objectives, design constraints and relevant artefacts. There exist some structured approaches, but still these are not complete and do not have open formats that describe requirements of a system/project with its artefacts. This paper introduces RDS (Requirement Description Schema), an XML-based versatile specification approach for the structural representation of functional and non-functional requirements (NFR). The approach is an efficient way of managing requirement metadata and comprehensive artefacts of requirements like status, priority, version, stability, elicitation source etc. The paper comprises a case study of online examination system for validating the instances with RDS. [ABSTRACT FROM AUTHOR]

Mobile Cloud-Computing (MCC) is a term introduced by Marc Baccue in 2009 20 that popularized the idea of using cloud-hosted components as a means to overcome the resource-constraints of mobile devices. But as the smartphones and tablets overcame their resource-constraints, the meaning of the term MCC changed. Nowadays MCC is mainly associated with using mobile devices to engage cloud-hosted services and to a lesser extend with combining multiple mobile devices (e.g. cloud of devices). However, as the number of users with multiple mobile devices increases there is a growing demand for enabling apps on mobile devices to share hardware and software resources. This in turn leads to questions regarding decentralized interaction, coordination and resource sharing among multiple mobile devices. This paper focusses on the “horizontal scalability” of apps e.g. the ability to combine multiple mobile devices (executing the same mobile app) into a single compute environment that utilizes all available hardware and software resources in a decentralized manner. One possible approach to achieve this is by designing mobile apps as sets of RESTful micro-services and to allow these services to communicate via low-bandwidth IoT communication protocols. This paper presents the results of our performance evaluations using RESTful micro-services on mobile devices that communicate via the IoT protocol CoAP in different WIFI environments. [ABSTRACT FROM AUTHOR]

The multi-objective scheduling of maximizing the profit and minimising the emission of a Hydro -Thermal System (HTS) using Lagrangian Relaxation-Evolutionary Programming (LR-EP) technique has been presented in this paper. The various constraints of the hydro and thermal power system network considered are power balance, reservoir storage, turbine flow rate and loading limits of both thermal and hydro plants. Numerical results for a sample test system consist of four hydro and three thermal plants have been presented. The increase in demand for electricity has lead to the introduction of hydro, nuclear and tidal power stations. Under such cases, it is essential to interconnect hydro and thermal plants so that effective utilization of all the available energy resources can be carried out and demand can be met easily. Since the operational cost of hydropower plant is low, it can be integrated with thermal power plant so that most of the demand can be met by hydro power plant resulting in less usage of fossil fuel consumption for thermal power plants. Unit Commitment (UC) for thermal plants in the deregulated environment has been dealt with in the earlier works showing considerable profit. Here, in this paper a new attempt has been carried out for multiobjective scheduling of hydrothermal power plants for a Generating company (GENCO). It is observed that there is a increase in profit on the inclusion of non-renewable energy source such as hydro power plants, since it reduces the fuel consumption of thermal power plants. This proves the improved effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A new sub-category of MANET is vehicular ad hoc network where vehicles communicate with each other. Because of constraint roads and very high speed of vehicles, routing is an issue in VANET. Most of the papers analyzed the performance of topology based routing protocols. This paper analyzed the performance of distance-effect routing algorithm for mobility (DREAM) and location aided routing (LAR) location-based protocols for city and highway vehicular environment. Metrics for analysis of performance like PDR, routing overhead, delay, throughput and lost packet ratio are evaluated using ns2 with IEEE802.11p. The simulation results are analyzed using AWK programming script. [ABSTRACT FROM AUTHOR]

This paper proposes a novel 3T2R labelling robot with hybrid mechanism for the end-faces of round steels. The hybrid mechanism consists of a three degrees-of-freedom (DOFs) translation (3T) parallel mechanism and a two DOFs rotation (2R) mechanism. Based on the Jacobian matrix of the hybrid mechanism, the author analyzed the influence of each dimension parameter on three performance indices, namely, kinematics, transmission and stiffness of the mechanism, under the constraints of the workspace. Based on the Pareto frontier approach, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was adopted to find the optimal values of the three performance indices, and identify the dimension parameters that bring the best overall performance of the whole mechanism. The research results shed new light on the design of light weight labelling robot. [ABSTRACT FROM AUTHOR]

Normative Multi-agent system (NMAS) forms a promising approach to software engineering for the development of autonomous systems, in norms-based environments. During the construction of these systems, different requirements can be considered: functional and non-functional requirement. Existing research in NMAS have mostly focused on the study of functional and behavioral requirements specification and verification, while Non-Functional Requirements (NFR) are crucial in the development of systems. They have dealt with the NFR implicitly through the capabilities of functional requirements. An important research issue refers to NFR in NMAS, i.e. how to specify and enforce NFR in NMAS that involves heterogeneous and autonomous agents. The NFR define constraints for the system and specify the qualities that a system can have, for example, security, performance, reliability, comfort, availability. Also, NFR might conflict among each other (for example, cost and quality, comfort and economy). A conflict between two NFR occurs when the fulfillment of one NFR violates another NFR. One of the main challenges currently faced in NMAS research is that of NFR specification and verification. In particular, how to specify and verify NFR in autonomous systems? In this paper, we propose a normative approach that allows the specification and verification of NFR for autonomous systems. To do this, we propose a new type of norms, called Non-Functional Norms (NFN). Indeed, our approach provides (i) a BNF normative language for specifying NFN, and (ii) a mechanism, based on Constraint Satisfaction Problem (CSP) technique, for the detection and resolution of normative conflicts. Our normative approach is also illustrated by a case study describing Smart City Management System (SCMS). [ABSTRACT FROM AUTHOR]

Robust output feedback model predictive control (MPC) of linear time-varying (LTV) systems subject to bounded state and output disturbances is considered in this paper. The output feedback controller is designed in such a way that the observer is time-varying so the unknown state and the observer state are bounded by the tubes whose center is the state of the nominal LTV system that is steered to the origin. The on-line computational complexity is similar to that required for nominal MPC for LTV systems. Robust stability is ensured by using the notion of Lyapunov functions. The controller design is illustrated via two examples in chemical processes. The results show that the proposed output feedback controller can guarantee robust stability and constraint satisfaction of the LTV systems in the presence of bounded state and output disturbances. [ABSTRACT FROM AUTHOR]

Abstract The goal of this paper is to define abstractions for multi-agent systems with feedback interconnection in their dynamics. In the proposed decentralized framework, we specify a finite or countable transition system for each agent which only takes into account the discrete positions of its neighbors. The dynamics of each agent consist of a feedback component which can guarantee certain system and network requirements and induces the coupled constraints, and additional input terms, which can be exploited for high level planning. In this work, we provide sufficient conditions for space and time discretizations which enable the abstraction of the system's behavior through a discrete transition system. Furthermore, these conditions include design parameters whose tuning provides the possibility for multiple transitions, and hence, the construction of transition systems with motion planning capabilities. [ABSTRACT FROM AUTHOR]

Conditional simple temporal networks with uncertainty (CSTNUs) allow for the representation of temporal plans subject to both conditional constraints and uncertain durations. Dynamic controllability (DC) of CSTNUs ensures the existence of an execution strategy able to execute the network in real time (i.e., scheduling the time points under control) depending on how these two uncontrollable parts behave. However, CSTNUs do not deal with resources. In this paper, we define conditional simple temporal networks with uncertainty and resources (CSTNURs) by injecting resources and runtime resource constraints (RRCs) into the specification. Resources are mandatory for executing the time points and their availability is represented through temporal expressions, whereas RRCs restrict resource availability by further temporal constraints among resources. We provide a fully-automated encoding to translate any CSTNUR into an equivalent timed game automaton in polynomial time for a sound and complete DC-checking. [ABSTRACT FROM AUTHOR]

Abstract Dantzig–Wolfe reformulation of an integer program convexifies a subset of the constraints, which yields an extended formulation with a potentially stronger linear programming (LP) relaxation. We would like to better understand the strength of such reformulations in general. As a first step we investigate the classical edge formulation for the stable set problem. We characterize weakest possible Dantzig–Wolfe reformulations (with LP relaxations not stronger than the edge formulation) and strongest possible reformulations (yielding the integer hull). We (partially) extend our findings to related problems such as the matching problem and the set packing problem. These are the first non-trivial general results about the strength of relaxations obtained from decomposition methods, after Geoffrion's seminal 1974 paper about Lagrangian relaxation. [ABSTRACT FROM AUTHOR]

This paper presents a new model and solution for the multi-objective Vehicle Routing Problem with Soft Time Windows ( VRPSTW ) using a hybrid metaheuristic technique. The proposed methodology is developed on the basics of a new swarm based Artificial Bee Colony ( ABC ) algorithm combined with two-step constrained local search for neighborhood selection. VRPSTW involves computing the routes of a set of vehicles with fixed capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. Here, the time window constraints are relaxed into “soft”, that is penalty terms are added to the solution cost whenever a vehicle serves a customer outside of his time window. The solution of routing problems with soft time windows has valuable practical applications. This paper uses a direct interpretation of the VRPSTW as a multi-objective optimization problem where the total traveling distance, number of window violations and number of required vehicles are minimized while capacity and time window constraints are met. Our work aims at using ABC inspired foraging behavior of honey bees which balances exploration and exploitation to avoid local optima and reach the global optima. The algorithm is applied to solve the well known benchmark Solomon׳s problem instances. Experimental results show that our suggested approach is quite effective, as it provides solutions that are competitive with the best known results in the literature. Finally, we present an analysis of our proposed algorithm in terms of computational time. [ABSTRACT FROM AUTHOR]

Surface quality and accuracy are the most important factors that affect the performance and life cycle of products. Surface deviations are confirmed to be nonstationary random signals. They show characteristics of randomness, irregularity and multiscale. For precise components, those deviations, i.e., the location/orientation deviations, form deviations, waviness and roughness are constrained by different scales of tolerances. In addition, tolerancing principles are specified to deal with the relationship between geometrical and dimensional deviations of the workpiece. Conventional modeling methods are unable to express surfaces with those features. This paper proposed a new method for geometrical modeling of multiscale toleranced workpiece with consideration of tolerancing principles. The workpiece deviations are decomposed into different scale of deviations. Each scale of deviations is expressed as the products of the normalized deviations and deviation factors. To balance the conflict between computing time and accuracy, a multilevel displaying method is developed. Those deviations are presented at different levels. In the simulation, each scale of tolerances and the tolerancing principles are integrated into the geometrical model of the workpiece. At the end of this paper, a case study was carried out to validate the proposed method. [ABSTRACT FROM AUTHOR]

Environmental economic dispatch of fixed head of hydrothermal power systems is viewed as a mulitobjective optimization problem in this paper. The practical hydrothermal system possesses various constraints which make the problem of finding global optimum difficult. This paper develops an improved multiobjective estimation of distribution algorithm to solving the above problem. A local learning operation is added into the original regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) in the improved approach so as to improve the local search ability and enhance the convergence efficiency. Furthermore, a repair mechanism is employed to repair the searched infeasible solutions in order to be able to search in the feasible region. In the experiment, the results obtained by the proposed approach have been compared with those from other three MOEAs: NSGA-II, NNIA, and RM-MEDA. Results from some pervious reported methods have also been employed to compare with our method. In addition, the results demonstrate the superiority of this proposed method as a promising MOEA to solve this power system multiobjective optimization problem. [ABSTRACT FROM AUTHOR]

This paper presents a novel parameter automation strategy for particle swarm optimization algorithm for solving non-convex emission constrained economic dispatch (NECED) problems. Many evolutionary techniques such as particle swarm optimization, differential evolution have been applied to solve these problems and found to perform in a better way in comparison with conventional optimization methods. But often these methods converge to a sub-optimal solution prematurely. This paper presents a new improved particle swarm optimization technique called self-organizing hierarchical particle swarm optimization technique with time-varying acceleration coefficients (SOHPSO_TVAC) for non-convex emission constrained economic dispatch (NECED) problems to avoid premature convergence. Generator ramp rate limits and prohibited operating zones are taken into account in problem formulation. Non-convex emission constrained economic dispatch (NECED) problem is obtained by considering both the economy and emission objectives. The performance of the proposed method is demonstrated on two sample test systems. The results of the proposed method are compared with other methods. It is found that the results obtained by the proposed method are superior in terms of fuel cost, emission output and losses. [ABSTRACT FROM AUTHOR]

This paper considers a unified approach for virtual sensing in smart buildings that utilizes equation-based modeling and optimization-based parameter estimation. A modeling method is introduced to describe relationships among existing sensory information and application-oriented parameters in a reusable manner. Measurement errors in existing sensors are also described in this equation-based model. When parameters to be estimated and existing sensors used for virtual sensing are identified, relevant equations are selected and combined as simultaneous equations. The parameter estimation method takes an optimization-based approach to cope with uncertainty in sensory information. Simultaneous equations corresponding to target sensing are used as a constraint in the optimization problem. In cases where many different sensors are utilized to increase accuracy, we may need to consider compromising contradictions among sensory information due to sensing errors. If the target building does not have enough sensors for the intended parameter estimation, we may need certain assumptions to determine the value. Such over- or under-defined situations are automatically detected and considered in our parameter estimation mechanism. For this purpose, we introduce a method, based on an idea inspired by randomized algorithms, for repeatedly solving an optimization problem with different weight factors in the objective function and for analyzing any fluctuations in estimated values. This is computationally inexpensive and can be applied to big real-world problems. We applied this method to building occupancy estimations for efficient air-conditioning control and to customer attribute analysis for an office building cafeteria during lunch time. [Display omitted] • A unified method for virtual sensing uses various types of sensors for higher accuracy. • Equation-based modeling enables sensor modeling in a reusable manner. • Optimization-based parameter estimation copes with uncertainty in sensory information. • Over- or under-defined situations are detected and considered in parameter estimation. • Occupant estimation and people flow analysis problems are successfully solved. [ABSTRACT FROM AUTHOR]

Monitoring environmental evolutions, one of the most crucial axes on which sustainable development is based, requires the knowledge of information on the observed geographical scenes. Due to the continuous technological developments in the remote sensing field, the data sources increase exponentially over time, and the information contained in satellite images becomes increasingly rich. These characteristics make the extraction, processing and resolution of such data a complicated task that varies according to the situation. It is, therefore, necessary to develop tools adapted to satellite images interpretation and analysis problems. In this context, the constraint satisfaction problem (CSP) seems to be one of the methods to solve these problems. Despite some challenges, the CSP approach has performed well and proven its value in various fields. The effectiveness of CSP is based on two key points: first, the definition of constraints, and second, the choice of resolution methods. Therefore, a synthesis document covering CSP resolution methods, both static and dynamic, becomes relevant and necessary. This paper represents the first comprehensive review of CSP. We begin by listing CSP methods, detailing their principles, and presenting the corresponding algorithms. We then illustrate the execution process of CSP methods using examples applied in the remote sensing domain for each one. Following this, we present a complete comparative study arranged according to key characteristics, which is intended to guide researchers to help them select the most appropriate resolution method for any given context. Finally, we present a set of challenges and future directions designed to suggest and drive further research in this promising field. • A comprehensive survey of resolution methods for constraint networks: static and dynamic algorithms • This paper represents the first comprehensive review of CSP resolution methods. • A guide of the most appropriate CSP resolution method for a given context • A set of challenges and future directions designed to suggest and drive future research in the field of CSP. [ABSTRACT FROM AUTHOR]

Abstract: This paper reviews the design, engineering principles and applications of machine tools specially developed for large parts. Large workshop machines are commonly used for manufacturing, where the impact of the general engineering principles differs substantially from those applied to conventional size machines. Portable machines are used during assembly and operation due to mobility, agility and energy constraints. Such large dimensions produce an amplification factor of any error source, so verification and calibration of such large or portable machines becomes even more critical than in conventional machines. The paper also includes future trends and unsolved challenges. [Copyright &y& Elsevier]

Abstract: Energy consumption has been the focus of many studies on Wireless Sensor Networks (WSN). It is well recognized that energy is a strictly limited resource in WSNs. This limitation constrains the operation of the sensor nodes and somehow compromises the long term network performance as well as network activities. Indeed, the purpose of all application scenarios is to have sensor nodes deployed, unattended, for several months or years. This paper presents the lifetime maximization problem in “many-to-one” and “mostly-off” wireless sensor networks. In such network pattern, all sensor nodes generate and send packets to a single sink via multi-hop transmissions. We noticed, in our previous experimental studies, that since the entire sensor data has to be forwarded to a base station via multi-hop routing, the traffic pattern is highly non-uniform, putting a high burden on the sensor nodes close to the base station. In this paper, we propose some strategies that balance the energy consumption of these nodes and ensure maximum network lifetime by balancing the traffic load as equally as possible. First, we formalize the network lifetime maximization problem then we derive an optimal load balancing solution. Subsequently, we propose a heuristic to approximate the optimal solution and we compare both optimal and heuristic solutions with most common strategies such as shortest-path and equiproportional routing. We conclude that through the results of this work, combining load balancing with transmission power control outperforms the traditional routing schemes in terms of network lifetime maximization. [Copyright &y& Elsevier]