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Balancing convergence and diversity has become a key point especially in many-objective optimization where the large numbers of objectives pose many challenges to the evolutionary algorithms. In this paper, an opposition-based evolutionary algorithm with the adaptive clustering mechanism is proposed for solving the complex optimization problem. In particular, opposition-based learning is integrated in the proposed algorithm to initialize the solution, and the nondominated sorting scheme with a new adaptive clustering mechanism is adopted in the environmental selection phase to ensure both convergence and diversity. The proposed method is compared with other nine evolutionary algorithms on a number of test problems with up to fifteen objectives, which verify the best performance of the proposed algorithm. Also, the algorithm is applied to a variety of multiobjective engineering optimization problems. The experimental results have shown the competitiveness and effectiveness of our proposed algorithm in solving challenging real-world problems.

In the carrier-based coverage repair problem, a single mobile robot replaces damaged sensors by picking up spare ones in the region of interest or carrying them from a base station in wireless sensor and robot networks. The objective is to find the shortest path of the robot. The problem is an extension of the traveling salesman problem (TSP). Thus, it is also called the one-commodity traveling salesman problem with selective pickup and delivery (1-TSP-SELPD). In order to solve this problem in a larger sensor distribution scenario more efficiently, we propose a two-stage approach in this paper. In the first stage, the mature and effective Lin-Kernighan-Helsgaun (LKH) algorithm is used to form a Hamiltonian cycle for all delivery nodes, which is regarded as a heuristic for the second stage. In the second stage, elliptical regions are set for selecting pickup nodes' and an edge-ordered list (candidate edge list, CEL) is constructed to provide major axes for the ellipses. The process of selecting pickup nodes and constructing the CEL is repeated until all the delivery nodes are visited. The final CEL stores a feasible solution. To update it, three operations-expansion, extension, and constriction-are applied to the CEL. The experimental results show that the proposed method reduces the computing time and achieves better results in higher-dimensional problems, which may facilitate the provision of solutions for more complicated sensor networks and can contribute to the development of effective and efficient algorithms for the one-commodity pickup-and-delivery traveling salesman problem (1-PDTSP)., Competing Interests: The authors have declared that no competing interests exist.

Due to fierce market competition, how to improve product quality and reduce development cost determines the core competitiveness of enterprises. However, design iteration generally causes increases of product cost and delays of development time as well, so how to identify and model couplings among tasks in product design and development has become an important issue for enterprises to settle. In this paper, the shortcomings existing in WTM model are discussed and tearing approach as well as inner iteration method is used to complement the classic WTM model. In addition, the ABC algorithm is also introduced to find out the optimal decoupling schemes. In this paper, firstly, tearing approach and inner iteration method are analyzed for solving coupled sets. Secondly, a hybrid iteration model combining these two technologies is set up. Thirdly, a high-performance swarm intelligence algorithm, artificial bee colony, is adopted to realize problem-solving. Finally, an engineering design of a chemical processing system is given in order to verify its reasonability and effectiveness.

To solve the control problem of time-varying state-scale nonlinear systems whose initial state is not affected by settling time, fixed-time convergence algorithms are proposed for first-order systems and higher-order systems in this paper. First, a scalar model is used to illustrate how the time-varying feedback parameter can guarantee that the system achieves asymptotic stability while achieving finite-time convergence, and it is proved that the settling time obtained in this paper is only related to the prescribed boundary. This allows us to design the settling time with an appropriate parameter based on the prescribed boundary. To exhibit the effectiveness and extensibility of the proposed algorithm for first-order scalar systems, the results are subsequently extended to general higher-order systems based on the backstepping method. By introducing numerical simulation results, this paper verifies that the proposed algorithm will make the system achieve asymptotic stability and its output can converge to a given boundary, regardless of the system's initial states. [ABSTRACT FROM AUTHOR]

Close-range photogrammetry methods are widely used for non-contact and accurate measurements of surface shapes. These methods are based on calculating the three-dimensional coordinates of an object from two-dimensional images using special digital processing algorithms. Due to the relatively complex measurement principle, the accurate estimation of the photogrammetric measurement error is a non-trivial task. Typically, theoretical estimations or computer modelling are used to solve this problem. However, these approaches cannot provide an accurate estimate because it is impossible to consider all factors that influence the measurement results. To solve this problem, we propose the use of physical modelling. The measurement results from the photogrammetric system under test were compared with the results of a more accurate reference measurement method. This comparison allowed the error to be estimated under controlled conditions. The test object was a flexible surface whose shape could vary smoothly over a wide range. The estimation of the measurement accuracy for a large number of different surface shapes allows us to obtain new results that are difficult to obtain using standard approaches. To implement the proposed approach, a laboratory system for the error estimation of close-range photogrammetric measurements was developed. The paper contains a detailed description of the developed system and the proposed technique for a comparison of the measurement results. The error in the reference method, which was chosen to be phasogrammetry, was evaluated experimentally. Experimental testing of the stereo photogrammetric system was performed according to the proposed technique. The obtained results show that the proposed technique can reveal dependencies that may not be detected by standard approaches. [ABSTRACT FROM AUTHOR]

Background: Despite its obvious relevance to computer science, computational thinking (CT) is transdisciplinary with the potential of impacting one's analytical ability. Although countless efforts have been invested across K‐12 education, there is a paucity of research at the postsecondary level about the extent to which CT can contribute to sustainable learning outcomes. Objectives: The current study examines how a series of Arduino‐based robotics learning activities capture the fuller essence of concepts related to CT. Methods: College students (n = 50) completed a series of six robotics learning activities. Think‐alouds, student reflections and performance scores were used to assess students' CT through a robotics challenge in virtual and physical learning environments. Results and Conclusions: Students verbalized CT concepts related to algorithmic thinking much more than abstraction, problem decomposition and testing and debugging. Students exposed to active learning performed better in a virtual robotics challenge compared to their peers in a traditional‐oriented classroom. Students' scores on the physical robotics challenge increased as a function of the number of references they made to CT concepts during the think‐alouds. It is possible to design pedagogical experiences that tap into various dimensions of CT at incremental levels of complexity through a series of Arduino‐based robotics activities. With the integration of an online simulation, students can visualize and transfer their CT skills between a virtual and physical learning environment, thus leading to more sustainable learning outcomes. Lay Description: What is already known about this topic: Computational thinking (CT) is the conceptual foundation for problem‐solving.CT shifted from unplugged to plugged activities.CT concepts can be fostered through educational robotics. What this paper adds: A multi‐dimensional approach to assessing CT.Applications of CT provide insights into skill transfer.An integrative theoretical approach to study the 'how' and the 'what' of CT. Implications for practice: Educational robotics activities can cater to a fuller range of CT skills.Virtual simulations with constructive feedback are beneficial for students in traditional settings.Mixed learning environments contribute to making links between lectures and labs. [ABSTRACT FROM AUTHOR]

This paper describes a novel, energy space based approach to the swing-up of an inverted pendulum. The details of the swing-up problem have been described. Equations of the velocity-controlled have been presented. Design of the controller based on energy space notion has been elaborated. The control algorithm takes into account state constraints and control signal constraints. Parameters of the controller have been optimized by means of the Differential Evolution method. A numerical simulation of the inverted pendulum driven by the proposed controller has been conducted, its results have been presented and elaborated. The paper confirms that the proposed method results in a simple and effective swing-up algorithm for a velocity-controlled inverted pendulum with state constraints and control signal constraints. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the widely spreading of fake news utilizing image manipulation and its bad effects, this paper investigates efficient image contents verification and manipulation detection algorithm performance in presence different attacks and noisy wireless channel. The presented algorithm works based on making horizontal scanning of the image blocks after segmenting it to upper and lower partition, every one of them is divided to equal number of small blocks. These blocks marks the opposite block in another image petitions utilizing different transforms, DFT, DCT, WHT, and DWT transforms. This approach is evaluated and tested in presence different attacks and over the wireless noisy channel for measuring the reliability and robustness of the presented algorithm. The WHT, DCT and DWT based algorithm performed good. The computer simulations reveled the DWT-based approach provides images with littlie quality improving. The algorithm performance over the AWGN channel is bad at the low SNR. For solve this problem the simple and less-complex error control schemes have been utilized to decrease the required SNR for achieving accepted quality of the received image. The merging algorithm is proposed also based on utilizing the mark encrypted image and encrypt marked images verifications approaches. Finally, the computer simulations proved the reliability and robustness of the DWT-based approach and its high detection sensitivity for any image manipulation in the different testing scenarios. [ABSTRACT FROM AUTHOR]

Fast and accurate detection of fault location in distribution networks, is one of the important issues in operation of the system. In this paper, an approach based on Binary Imperialistic Competitive algorithm and Nelder-Mead simplex search has been proposed for placement of fault indicators in distribution networks. By using this method, the problem of locating fault indicators can be solved faster and more accurately by minimizing of cost function. In this paper, in addition to describing the BICA-NM hybrid algorithm, the fault indicator placement has been implemented using this method on a test system, and the simulation results have been compared with Genetic algorithm (GA), Binary Shuffled Frog Leap Algorithm (BSFLA) and Improved BSFLA (IBSFLA) and the accuracy and fastness of the proposed method has been demonstrated. [ABSTRACT FROM AUTHOR]

This article aims to explore drivers of success of a crowdsourcing platform. The study conceptualizes crowdsourcing as a mode of open innovation and places such a concept within the context of the sharing economy. Upon this framework, the paper analyses the case of InnoCentive, a successful crowdsourcing platform where innovation‐driven organizations search for solutions to their R&D tasks. The system dynamics model frames main InnoCentive business processes and offers a causal perspective on the relationships between platform performance—in terms of users base—and resources structure. The discussion on simulation outputs is carried out through a dynamic performance management view of performance, so as to highlight which factors are crucial to lead crowdsourcing platform to be effective in supporting seekers organization to innovate in an open way, as well as to understand how platform managers may improve such a problem‐solving process. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The premixed turbulent V-flame is numerically simulated in this paper. The technique used in Chan et al. [Effect of intense turbulence on turbulent premixed V-flame, Int. J. Eng. Sci. 41 (2003), pp. 903-916; Simulation of a premixed turbulent flame with the discrete vortex method, Int. J. Numer. Methods Eng. 48 (2000), pp. 613-627] was based on the random vortex method and the conventional level set method. However, this paper is an improvement of the previous technique by using a conservative level set method. The new algorithm uses a bi-cubic interpolation process together with Newton's method to reconstruct the velocity field to improve the conservation of the level set method. The numerical example in this paper indicates the efficiency of the new method in solving the problem of turbulent premixed V-flame by comparing the results with available data. [ABSTRACT FROM AUTHOR]

This paper considers the problem of online optimization where the objective function is time-varying. In particular, we extend coordinate descent type algorithms to the online case, where the objective function varies after a finite number of iterations of the algorithm. Instead of solving the problem exactly at each time step, we only apply a finite number of iterations at each time step. Commonly used notions of regret are used to measure the performance of the online algorithm. Moreover, coordinate descent algorithms with different updating rules are considered, including both deterministic and stochastic rules that are developed in the literature of classical offline optimization. A thorough regret analysis is given for each case. Finally, numerical simulations are provided to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

Orienteering problem (OP) is a routing problem, where the aim is to generate a path through set of nodes, which would maximize total score and would not exceed the budget. In this paper, we present an extension of classic OP—Orienteering Problem with Functional Profits (OPFP), where the score of a specific point depends on its characteristics, position in the route, and other points in the route. For solving OPFP, we developed an open-source framework for solving orienteering problems, which utilizes four core components of OP in its modular architecture. Fully-written in Go programming language our framework can be extended for solving different types of tasks with different algorithms; this was demonstrated by implementation of two popular algorithms for OP solving—Ant Colony Optimization and Recursive Greedy Algorithm. Computational efficiency of the framework was shown through solving four well-known OP types: classic Orienteering Problem (OP), Orienteering Problem with Compulsory Vertices (OPCV), Orienteering Problem with Time Windows (OPTW), and Time Dependent Orienteering Problem (TDOP) along with OPFP. Experiments were conducted on a large multi-source dataset for Saint Petersburg, Russia, containing data from Instagram, TripAdvisor, Foursquare and official touristic website. Our framework is able to construct touristic paths for different OP types within few seconds using dataset with thousands of points of interest. [ABSTRACT FROM AUTHOR]

The paper presents the stress-strain state simulation of the compound technical structure - vulcanizer diaphragm. A set of working plates in the form of a circular plate with the thickness discretely varied in the radial direction is modeled. An algorithm to solve the contact problem is developed. The obtained calculation results are compared with the experimental ones to demonstrate their practical use. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the increasing of available network data; however, much of those data is incomplete. Link prediction, which can find the missing links of a network, plays an important role in the research and analysis of complex networks. Based on the assumption that two unconnected nodes which are highly similar are very likely to have an interaction, most of the existing algorithms solve the link prediction problem by computing nodes' similarities. The fundamental requirement of those algorithms is accurate and effective similarity indices. In this paper, we propose a new similarity index, namely similarity based on activity and connectivity (SAC), which performs link prediction more accurately. To compute the similarity between two nodes, this index employs the average activity of these two nodes in their common neighborhood and the connectivities between them and their common neighbors. The higher the average activity is and the stronger the connectivities are, the more similar the two nodes are. The proposed index not only commendably distinguishes the contributions of paths but also incorporates the influence of endpoints. Therefore, it can achieve a better predicting result. To verify the performance of SAC, we conduct experiments on 10 real-world networks. Experimental results demonstrate that SAC outperforms the compared baselines. [ABSTRACT FROM AUTHOR]

In pulsed Doppler radars, subsampling causes incorrect estimates of the radial velocity of the detected targets whenever its related Doppler frequency is greater than the pulse repetition frequency of the radar. The classic Chinese remainder theorem is a well-known method to overcome this problem. On the other hand, the robust Chinese remainder theorem is a search-based algorithm that, under some conditions, can guarantee the correct estimation of the Doppler frequency. In this paper, we have derived the exact probability of detection for both methods applied in a pulsed Doppler radar. Simulations have been compared to the analytical results and have shown an excellent agreement. [ABSTRACT FROM AUTHOR]

The envisaged usage of multiple Unmanned Aerial Vehicles (UAVs) to perform cooperative tasks is a promising concept for future autonomous military systems. An important aspect to make this usage a reality is the solution of the task allocation problem in these cooperative systems. This paper addresses the problem of tasks allocation among agents representing UAVs, considering that the tasks are created by a central entity, in which the decision of which task will be performed by each agent is not decided by this central entity, but by the agents themselves. The assumption that tasks are created by a central entity is a reasonable one, given the way strategic planning is carried up in military operations. To enable the UAVs to have the ability to decide which tasks to perform, concepts from swarm intelligence and multi-agent system approach are used. Heuristic methods are commonly used to solve this problem, but they present drawbacks. For example, many tasks end up not begin performed even if the UAVs have enough resources to execute them. To cope with this problem, this paper proposes three algorithm variants that complement each other to form a new method aiming to increase the amount of performed tasks, so that a better task allocation is achieved. Through experiments in a simulated environment, the proposed method was evaluated, yielding enhanced results for the addressed problem compared to existing methods reported in the literature. [ABSTRACT FROM AUTHOR]

This paper presents two bio-inspired algorithms for coalition formation of multiple modular robot systems. An effective and efficient coalition formation system can help modular robot system take full advantage of reconfigurability of modular robots. In this paper, the multirobot coalition formation problem is illustrated and a mathematical model for the problem is described. Two bio-inspired algorithms, ant-colony algorithm (ACA) and genetic algorithm (GA), are introduced for solving the mathematical model. With the two algorithms, it is able to form a large number of robots into many different groups for a variety of applications, such as parallel performance of multiple tasks by multiple teams of robots. The paper compares the efficiency and effectiveness of two algorithms for solving the presented problem with case study. The results for the comparison study are analyzed and discussed. Also, the implementation details of the simulation and experiment using ACA are presented in the paper. [ABSTRACT FROM AUTHOR]

Design of conventional correlation filters requires explicit knowledge of the appearance and shape of a target object, so the performance of correlation filters is significantly affected by changes in the appearance of the object in the input scene. In particular, the performance of correlation filters worsens when objects to be recognized are partially occluded by other objects, and the input scene contains a cluttered background and noise. In this paper, we propose a new algorithm for the design of a system consisting of a set of adaptive correlation filters for recognition of partially occluded objects in noisy scenes. Since the input scene may contain different fragments of the target, false objects, and background to be rejected, the system is designed in such a manner to guarantee equally high correlation peaks corresponding to parts of the target in the scenes. The key points of the system are as follows: (i) it consists of a bank of composite optimum filters, which yield the best performance for different parts of the target; (ii) it includes a fragmentation of the target into a given number of parts in the training stage to provide equal intensity responses of the system for each part of the target. With the help of computer simulation, the performance of the proposed algorithm for recognition partially occluded objects is compared with that of common algorithms in terms of objective metrics. [ABSTRACT FROM AUTHOR]

Imagine coating buildings and bridges with smart particles (also coined smart paint) that monitor structural integrity and sense and report on traffic and wind loads, leading to technology that could do such inspection jobs faster and cheaper and increase safety at the same time. In this paper, we study the problem of uniformly coating objects of arbitrary shape in the context of self-organizing programmable matter, i.e., programmable matter which consists of simple computational elements called particles that can establish and release bonds and can actively move in a self-organized way. Particles are anonymous, have constant-size memory, and utilize only local interactions in order to coat an object. We continue the study of our universal coating algorithm by focusing on its runtime analysis, showing that our algorithm terminates within a linear number of rounds with high probability. We also present a matching linear lower bound that holds with high probability. We use this lower bound to show a linear lower bound on the competitive gap between fully local coating algorithms and coating algorithms that rely on global information, which implies that our algorithm is also optimal in a competitive sense. Simulation results show that the competitive ratio of our algorithm may be better than linear in practice. [ABSTRACT FROM AUTHOR]

In this paper, the joint transceiver design issue is investigated for multi-cell systems with multiple mobile terminals (MTs). An optimization problem is formulated based on the criterion of mean squared error minimization for the joint design of the cooperative precoder of all multi-input multi-output (MIMO) cells and the cooperative decoder of all MIMO MTs. With a fixed decoder, the optimum precoder is first derived in a closed-form expression, where no exchange of data streams among all of the MIMO cells is required due to the limited capability of the backhaul. Subsequently, an optimum decoder is then obtained in an analytical expression with the fixed precoder. Based on these two expressions, an iterative algorithm with guaranteed convergence is finally developed to carry out the joint optimization of the cooperative precoders and decoders. Simulation results demonstrating the effectiveness of the proposed algorithm are also provided. [ABSTRACT FROM AUTHOR]

In this paper, an expert system is designed to derive the rephasing strategy of laterals and distribution transformers to enhance three-phase balancing of distribution systems. The heuristic rules adopted by distribution engineers are incorporated in the knowledge base of the expert system in the problem-solving process. The neutral current reduction algorithm is developed to support the inference engine to derive the rephasing strategy to reduce the neutral current of distribution feeder so that the tripping of over-current relay can be prevented and the customer service interruption cost and labor cost to execute the rephasing strategy can be justified by the power loss reduction obtained. To demonstrate the effectiveness of the proposed expert system to enhance three-phase balance, a practical distribution feeder in Taiwan Power Company (Taipower) is selected for computer simulation. By minimizing the objective function subjected to the rephasing rules, the rephasing strategy has been derived to identify the laterals and distribution transformers for phasing adjustment. After executing the proposed rephasing strategy by Taipower engineers, the phase currents and neutral current of test feeder has been collected by the SCADA system to verify the reduction of neutral current. The power loss reduction obtained by three-phase balancing has been solved by three-phase load flow analysis, which is then used to justify the customer service interruption cost and labor cost for rephasing of test feeder. [ABSTRACT FROM AUTHOR]

A Chunk by chunk-based allocation is an emerging subcarrier allocation in Single Carrier Frequency Division Multiple Access (SC-FDMA) due to its low complexity. In this paper, a combined userorder and chunk-order allocation for solving chunk allocation problem which minimizes the average BER of all users while improving the throughput in SC-FDMA uplink is proposed. The subcarrier grouping into a chunk of all users on both-order allocations are performed by averaging the BER of a contiguous subcarriers within a chunk. The sequence of allocation is according to the average of users' BER on userorder allocation and the average of chunks' BER on chunk-order allocation. The best allocation is determined by choosing one of both-order allocations which provides the smaller BER systems. The simulation results showed that the proposed algorithm can outperform the previous algorithms in term of average BER and throughput without increase the time complexity. [ABSTRACT FROM AUTHOR]

This paper presents a whole body control framework for the control of a dual arm underwater vehicle manipulator system developed in the context of the MARIS Italian research project, which deals with the control and coordination of underwater vehicles for manipulation and transportation problems. The proposed framework is the extension of the one used in the successful TRIDENT FP7 project that has been improved to be able to deal with multidimensional inequality control objectives. After the presentation of the mathematical background, the paper presents some simulation results showing the good performances of the proposed algorithm. [ABSTRACT FROM AUTHOR]

We focus on the control and the synthesis of secure timed systems which are modelled as timed automata. The security property that the system must satisfy is anon-interferenceproperty. Intuitively, non-interference ensures the absence of any causal dependency from a high-level domain to a lower level domain. Various notions of non-interference have been defined in the literature, and in this paper, we focus onstrong non-deterministic non-interference(SNNI) and two (bi)simulation-based variants thereof (cosimulation-based SNNI and bisimulation-based SNNI). These properties and their extensions have been mostly studied in the context of discrete event systems, while it is now well-known that time is an important attack vector against secure systems. At the same time, there is an obvious interest in going beyond simple verification to control problems: to be able to automatically make systems secure. We consider non-interference properties in the challenging setting of control of dense-time systems specified bytimed automataand we study the two following problems: (1) check whether it is possible to find a sub-system so that it is non-interferent; if yes, (2) compute a (largest) sub-system which is non-interferent. We exhibit decidable sub-classes for these problems, assess their theoretical complexities and provide effective algorithms based on the classical framework of timed games. [ABSTRACT FROM AUTHOR]

Coupling sensors in a sensor network with mobility mechanism can boost the performance of wireless sensor networks (WSNs). In this paper, we address the problem of self-deploying mobile sensors to reach high coverage. The problem is modeled as a multi-objective optimization that simultaneously minimizes two contradictory parameters; the total sensor moving distance and the total uncovered area. In order to resolve the aforementioned deployment problem, this study investigates the use of biologically inspired mechanisms, including evolutionary algorithms and swarm intelligence, with their state-of-the-art algorithms. Unlike most of the existing works, the coverage parameter is expressed as a probabilistic inference model due to uncertainty in sensor readings. To the best of our knowledge, probabilistic coverage of mobile sensor networks has not been addressed in the context of multi-objective bio-inspired algorithms. Performance evaluations on deployment quality and deployment cost are measured and analyzed through extensive simulations, showing the effectiveness of each algorithm under the developed objective functions. Simulations reveal that only one multi-objective evolutionary algorithm; the so-called multi-objective evolutionary algorithm with decomposition survives to effectively tackle the probabilistic coverage deployment problem. It gathers more than 78 % signals from all of the targets (and in some cases reaches 100 % certainty). On the other hand, non-dominated sorting genetic algorithm II, multi-objective particle swarm optimization, and non-dominated sorting particle swarm optimization show inferior performance down to 16-32 %, necessitating further modifications in their internal mechanisms. [ABSTRACT FROM AUTHOR]

In order to solve the problem that communication and computing resources are consumed in the first order multiagent system circle formation with communication delay, this paper introduced an event-driven control mechanism, and designed the control law for arbitrary circle formation, which was coupling with state dependent and state independent event-driven conditions. Event-triggered function was based on state error, made the information transmission between two neighbor agents and the controller updates driven by properly defined events. It proved the convergence of the control law strictly and theoretically, and the numerical simulation validated the effectiveness of control algorithm. The simulation results also s how that the updating frequency of the controller and the resource consumption of the agent are reduced with the desired system performance. [ABSTRACT FROM AUTHOR]

The aim of this work is to obtain multi-objective linear programming algorithms that can be used to solve the global problem of multi-object safety control processes in order to minimize the risk of collisions. In multi-objective linear optimization models, satisfactory trade-off assesses and resolves the conflict between different control objectives. A comparison of single-, bi-, and tri-objective linear programming algorithms allows us to adapt the appropriate optimization method to the conditions of the control process. An important outcome of the present research is the demonstration of the greater effectiveness of bi- and tri-objective optimization compared to single-objective optimization, reflecting the compromises taken into account when choosing between objects and achieving a minimum risk of collision when passing them. [ABSTRACT FROM AUTHOR]

Within the context of constraint solving, a portfolio approach allows one to exploit the synergy between different solvers in order to create a globally better solver. In this paper we present SUNNY: a simple and flexible algorithm that takes advantage of a portfolio of constraint solvers in order to compute — without learning an explicit model — a schedule of them for solving a given Constraint Satisfaction Problem (CSP). Motivated by the performance reached by SUNNY vs. different simulations of other state of the art approaches, we developed sunny-csp, an effective portfolio solver that exploits the underlying SUNNY algorithm in order to solve a given CSP. Empirical tests conducted on exhaustive benchmarks of MiniZinc models show that the actual performance of sunny-csp conforms to the predictions. This is encouraging both for improving the power of CSP portfolio solvers and for trying to export them to fields such as Answer Set Programming and Constraint Logic Programming. [ABSTRACT FROM AUTHOR]

In biological systems, the dynamic analysis method has gained increasing attention in the past decade. The Boolean network is the most common model of a genetic regulatory network. The interactions of activation and inhibition in the genetic regulatory network are modeled as a set of functions of the Boolean network, while the state transitions in the Boolean network reflect the dynamic property of a genetic regulatory network. A difficult problem for state transition analysis is the finding of attractors. In this paper, we modeled the genetic regulatory network as a Boolean network and proposed a solving algorithm to tackle the attractor finding problem. In the proposed algorithm, we partitioned the Boolean network into several blocks consisting of the strongly connected components according to their gradients, and defined the connection between blocks as decision node. Based on the solutions calculated on the decision nodes and using a satisfiability solving algorithm, we identified the attractors in the state transition graph of each block. The proposed algorithm is benchmarked on a variety of genetic regulatory networks. Compared with existing algorithms, it achieved similar performance on small test cases, and outperformed it on larger and more complex ones, which happens to be the trend of the modern genetic regulatory network. Furthermore, while the existing satisfiability-based algorithms cannot be parallelized due to their inherent algorithm design, the proposed algorithm exhibits a good scalability on parallel computing architectures. [ABSTRACT FROM AUTHOR]

Due to the massive spread of distributed computing systems their effective use has become an urgent issue. Therefore it is necessary to analyze the algorithms of such systems. This paper attempts to examine the coverage area with the specified reliability indices. A proposed method based on selection of the redundancy rate. Methods of analytical modeling and multivariate optimization were used to solve the problem. [ABSTRACT FROM AUTHOR]

In this paper the problem of patrolling an environment with a dynamic team of robots is targeted. Lately, the interest of the research community has been focused in the development of patrol strategies; however there is a deficit of studies comparing such strategies, namely in terms of their performance and team scalability in different environments. For this reason, an evaluation of five representative patrol approaches is presented in this article. Aiming to analyze the performance, ability to scale and the behavior resulting from interactions between teammates, extensive realistic simulation using ROS together with Stage was conducted. The metric used to compare the performance is the average idleness of the topological environment (i.e. graph), that represents the area to patrol. The results presented help to identify which strategies enable enhanced team scalability and which are the most suitable approaches given any environment, supporting future research directions in the field. [ABSTRACT FROM PUBLISHER]

In this paper, a novel structure has been suggested for Deferential Evolutionary (DE) Algorithm to find optimal size/location of substation. To improve simple DE algorithm, four improvement stages are applied on DE algorithm which is called Adaptive bi-Scenario Deferential Evolutionary (AbSDE) algorithm. In improvement process, at first, crossover rate and scaling factor of mutation operator are adapted. Then two scenarios are introduced for mutation operator and finally migration operator is added to DE algorithm to escape from local optimal points. The AbSDEA has been used to solve a multistage Optimal Substation Placement (OSP). The OSP is formulated in three stages; the number and size/location of substation are obtained at first and second stages, respectively. In third stage, cost of project formulated as function of geographical and electrical parameters. Simulation is done in six load levels and two novel indices are defined to analyze obtained results from proposed algorithm. To confirm capability of AbSDE algorithm to solve OSP problem, results of the algorithm have been compared with simple DE and Particle Swarm Optimization (PSO) algorithms. [ABSTRACT FROM AUTHOR]

The harmony search (HS) method is a popular meta-heuristic optimization algorithm, which has been extensively employed to handle various engineering problems. However, it sometimes fails to offer a satisfactory convergence performance under certain circumstances. In this paper, we propose and study a hybrid HS approach, HS-PBIL, by merging the HS together with the population-based incremental learning (PBIL). Numerical simulations demonstrate that our HS-PBIL is well capable of outperforming the regular HS method in dealing with nonlinear function optimization and a practical wind generator optimization problem. [ABSTRACT FROM AUTHOR]

Abstract: The partitional clustering algorithms are used more widely in text clustering. However, the traditional algorithms based on partition treat all the attributes equally in clustering process. They all suppose that the importance of each attribute is equal. These algorithms will have a lower accuracy. In order to handle this problem, this paper provides a new clustering algorithm-attribute weighted fuzzy c-means algorithm. During the iteration of this algorithm, it can find the important attributes. Moreover, this algorithm also can find the cluster structure hiding by the unimportant attributes. The simulation of this algorithm on test documents can prove the algorithm provided by this paper can gain a good computation speed, found the latent structure and can remark the different importance of each attribute. [Copyright &y& Elsevier]

Abstract: Computer simulation is an increasingly popular tool for determining the most suitable hybrid energy system type, design and control for an isolated community or a cluster of villages. This paper presents the development of the optimum control algorithm based on combined dispatch strategies, to achieve the optimal cost of battery incorporated hybrid energy system for electricity generation, during a period of time by solving the mathematical model, which was developed in Part I of this tri-series paper. The main purpose of the control system proposed here is to reduce, as much as possible, the participation of the diesel generator in the electricity generation process, taking the maximum advantage of the renewable energy resources available. The overall load dispatch scenario is controlled by the availability of renewable power, total system load demand, diesel generator operational constraints and the proper management of the battery bank. The incorporation of a battery bank makes the control operation more practical and relatively easier. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the joint design of a transmit beampattern and angular waveform (AW) for colocated multiple-input multiple-output (MIMO) radars. The importance of the AW in the proposed signal processing strategy is first clarified, and then, two optimization models are established, which are aimed at either the power spectral density (PSD) design or the spectral compatibility and similarity design of the AW. There are two main differences between the proposed models and existing models. First, instead of matching a desired template or maximizing the transmit power on specific regions, the transmit beampattern in this paper is optimized to approach several key points, which guarantees the high transmit gain and the flexible adjustment of each beam gain. Second, instead of optimizing the performance of the transmit waveform, only the characteristics of the AW are examined, and they can be constrained quantitatively according to their relationship with the transmit gain. The two models can be unified into the same framework, and an efficient algorithm is proposed to solve the problem under a constant modulus constraint. The convergence of the proposed algorithm is demonstrated, and some improvements to reduce the computational complexity are proposed. Numerical simulations showed that compared to the existing methods, the proposed approach can be used to obtain a higher transmit gain, flexibly adjust each beam gain, and more accurately control the PSD, spectral compatibility, and similarity of the AW. Moreover, numerical simulations showed that, compared to the use of existing methods, the proposed algorithm has higher computational efficiency. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on the technology and applications supported by networked cooperative autonomous systems. [ABSTRACT FROM PUBLISHER]

The Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm has been used for solving the off-line dynamic origin-destination (OD) estimation problem. While the algorithm can be used with very general formulations of the problem, it can also be unstable. The paper proposes methods and evaluates their effectiveness in improving the SPSA performance at two levels: a) scaling the step size and using a hybrid gradient estimation; and b) proposing alternative clustering strategies to be used with the c-SPSA version of the algorithm, where OD flows are estimated in clusters. The proposed enhancements are evaluated through simulation experiments on a real network. [ABSTRACT FROM AUTHOR]

With a small number of elements in uniform circular arrays (UCAs), the performance of beamspace transformation (BT) based methods for 2D direction-of-arrival (DOA) estimation in the presence of mutual coupling suffers serious degradation. The performance breakdown is associated with the residual term which is introduced by the BT. To solve this problem, a novel algorithm which modifies the beamspace data is proposed in this paper. Based on the estimated azimuth angles obtained by a UCA-RARE-like method and the estimated elevation angles obtained by a new searching method, the beamspace data is iteratively revised by removing its residual component. Thus, unbiased 2D DOA estimates can be obtained depending on the revised beamspace data. Simulation results are given to verify the effectiveness of the proposed algorithm and a comparison is made with results from existed methods. [ABSTRACT FROM AUTHOR]

This paper studies the distributed average tracking problem for multiple time-varying signals generated by linear dynamics, whose reference inputs are nonzero and not available to any agent in the network. In the edge-based framework, a pair of continuous algorithms with, respectively, static and adaptive coupling strengths is designed. Based on the boundary layer concept, the proposed continuous algorithm with static coupling strengths can asymptotically track the average of multiple reference signals without the chattering phenomenon. Furthermore, for the case of algorithms with adaptive coupling strengths, average tracking errors are uniformly ultimately bounded and exponentially converge to a small adjustable bounded set. Finally, a simulation example is presented to show the validity of theoretical results. [ABSTRACT FROM AUTHOR]

This paper presents a new Electromagnetism-like Mechanism (EM) algorithm with Split, Probe and Compare feature (SPC-EM). The proposed algorithm replaces the local search segment of a standard EM with a new search scheme named Split, Probe, and Compare (SPC). A nonlinear equation is designed to systematically and dynamically adjust the length of the probes based on the outcome of the Compare segment in each iteration. Extensive computational simulations and comparisons on 10 different benchmark problems from the literature were carried out. Results show that the new modified mechanism outperformed all other algorithms involved in the benchmarking. We thus conclude that the proposed SPC-EM works well with the designed probe-length tuning equation in solving numerical optimization problems. [ABSTRACT FROM AUTHOR]

This paper proposes a novel distributed event-triggered algorithmic solution to the multi-agent average consensus problem for networks whose communication topology is described by weight-balanced, strongly connected digraphs. The proposed event-triggered communication and control strategy does not rely on individual agents having continuous or periodic access to information about the state of their neighbors. In addition, it does not require the agents to have a priori knowledge of any global parameter to execute the algorithm. We show that, under the proposed law, events cannot be triggered an infinite number of times in any finite period (i.e., no Zeno behavior), and that the resulting network executions provably converge to the average of the initial agents’ states exponentially fast. We also provide weaker conditions on connectivity under which convergence is guaranteed when the communication topology is switching. Finally, we also propose and analyze a periodic implementation of our algorithm where the relevant triggering functions do not need to be evaluated continuously. Simulations illustrate our results and provide comparisons with other existing algorithms. [ABSTRACT FROM AUTHOR]

A novel attitude algorithm for fiber strapdown inertial navigation system (SINS) is studied in this paper. The noncommutativity rotation is one of the major errors in attitude updating for SINS. As a general rule, accuracy can be improved by increasing attitude updating frequency. However, it is limited by gyro and computer technology. To solve this problem, a modified algorithm for fiber SINS is proposed. This algorithm uses angular rate as algorithm input and estimates the noncommutativity error with the cross product of angular rates and the cross product of previous accumulated gyro outputs with current gyro outputs. Simulations and experiments are performed under coning motion and three-axis turntable environment, and the results show that the accuracy of the proposed algorithm can be improved effectively under coning motion, as well as in three-axis turntable environment, though not as greatly. [ABSTRACT FROM AUTHOR]

The long term of this ongoing research has to do with increasing degree of autonomy for robots involved in underwater intervention missions. Bearing in mind that the specific mission to face has been the intervention on a panel, in this paper some results in different development stages are presented by using the real mechatronics and the panel mockup. Furthermore, some details are highlighted describing two methodologies implemented for the required visually-guided manipulation algorithms. These algorithms are used to correct problems caused by a bad initialization or miscalibration of the arm. They consist of detecting a marker placed in a known position of the arm and updating the value of each joint after any detection. A roadmap explaining the different testbeds used for experimental validation, in increasing complexity order, is also presented. It is worth mentioning that the aforementioned results would be impossible without previously generated know-how for both the complete developed mechatronics for the autonomous underwater vehicle for intervention, and the required 3D simulation tool. In summary, thanks to the implemented approach, the intervention system is able to control the way in which the gripper approximates and manipulates the two panel devices (i.e. a valve and a connector) in an autonomous manner. Results in different scenarios demonstrate the reliability and feasibility of this autonomous intervention system in water tank and pool conditions. [ABSTRACT FROM AUTHOR]