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Based on Differential Evolution and Cuckoo Search, a hybrid optimization algorithm, called DDECS, is proposed in this paper for solving the ship pipe route layout optimization problem. The purpose of DDECS algorithm is to quickly and accurately optimize the ship pipe route under various kinds of constraints. Firstly, this paper proposed a vector coding method for ship pipe routes by studying the existing coding methods and ship pipe routes' attributes. The proposed vector coding method has a concise structure, strong compatibility and is independent of the gridding space. And then, the Discrete Hybrid Differential Evolution and Cuckoo Search (DDECS) algorithm was implemented by using the proposed vector coding. The DDECS algorithm has few parameters and high computational efficiency, and it can effectively find the global optimal solution by maintaining the diversity of the population. Finally, three comparative simulations were presented to validate the effectiveness and engineering practicality of DDECS algorithm. The results showed that, the proposed DDECS algorithm can get an optimization scheme with fewer elbows and more remaining areas, which is significant for the layout optimization of the ship pipe route. • According to the pipe route attributes, this paper proposes a concise, universal, and independent coding for ship pipes. • Combining Differential Evolution and Cuckoo Search, a hybrid algorithm is proposed for optimizing the ship pipe route. • Three comparative simulations are used to reveal that the proposed coding and algorithm have feasibility and practicability. [ABSTRACT FROM AUTHOR]

The optimisation of the corridor allocation problem (CAP) belongs to the optimisation of the efficiency of the automated production line. The goal is to reduce the material handling cost (MHC) in the production process through a reasonable layout of the facilities, so as to save expenses for the enterprise. In recent years, with the acceleration of market changes, product design and production process adjustments have become more frequent, and more attention has been paid to the research on the layout of facilities under the condition of changes in the flow of materials between production facilities over time. On the basis of the CAP model, this paper considers the optimisation problem of row layout when the flow of materials between facilities fluctuates in a certain range. The new model can be utilised to obtain the overall optimisation solution under the condition of the floating material flow matrix, so as to achieve the goal of optimising the total MHC in the entire production process. As the new model introduces more variables and intermediate parameters, a two-stage solution method is previously required, which greatly increases the time to solve the problem. This paper proposes a targeted meta-heuristic algorithm optimisation method combining the advantages of tabu search algorithm and harmony search algorithm, which simplifies the solution phase of calling the precise solver in the two-stage algorithm of row facility layout problem, improves the problem solving efficiency, and makes the solution of large-scale problems become possible. The proposed model is verified through Lingo software, and then the model and the hybrid algorithm in the MATLAB environment are verified with each other. Finally, the proposed simplified algorithm is utilised to solve the large-scale problems that could not be solved by the two-stage algorithm before. [ABSTRACT FROM AUTHOR]

This paper develops a robust strategy based on integrating three mutation phases and adapted control parameters into the Adaptive Guided Differential Evolution algorithm called (mAGDE) to improve diversity and exploration of the original AGDE. The mAGDE performance is evaluated using IEEE CEC'2020 test suite. Furthermore, the mAGDE is employed to identify the solid oxide fuel cell (SOFC) model optimal parameters. Two modes of SOFC operation are investigated, steady and transient states. The results obtained from the proposed mAGDE are compared with a number of recent, well-established and reputed meta-heuristics, including Particle swarm optimization, Teaching learning-based optimization, Whale optimization algorithm, Harris hawks optimization, Marine predators algorithm, Archimedes optimization algorithm, Differential evolution, and the original AGDE. Additionally, the statistical parameters that measure the performance of the proposed optimizer and the other competitors are calculated. The main finding demonstrated the preference and robustness of the suggested mAGDE in constructing the SOFC circuit that closely converges to the actual one. During the steady-state operation, the best fitness value obtained via the suggested mAGDE for operation at 1273 K is 2.2995E−06, while in the transient-state operation, the best SMSE is 1.04. The average cost function is decreased by 43.33% compared to the one obtained by the original AGDE. From the aforementioned assessments, it can be concluded that the proposed mAGDE is outstanding and promising. • This paper introduces a modified mAGDE based on the integration of a modified three-phases AGDE with the DE_SPA algorithm. • A modified three-phases AGDE has been introduced to maintain diversity and convergence of the evolutionary process. • The CEC'2020 test suite problems are used to demonstrate the performance mAGDE against other competitors. • Applying for first time mAGDE to identify the optimal parameters of SOFC model. • The robustness of the proposed strategy-based mAGDE is proved. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multi-step subgradient scheme to solve distributed optimization problem with a balanced digraph. To this end, matrix diameter and radius are first introduced to describe the convergence rate of the adjacency matrix. Then, we propose a hierarchical and recursive observer to estimate the desired optimal solution, and we prove that the proposed strategy can asymptotically obtain an optimal solution, while all networked systems achieve a consensus among estimates in a finite-time. Simulation results verified the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Highlights • The article addresses reliability-redundancy allocation problem (RRAP). • We present a new model for RRAP using the theory of survival signature. • A new adaptive penalty function is proposed for solving RRAP. • The dimension of RRAP is reduced using the proposed approach. Abstract In recent research, the major focus on reliability-redundancy allocation problems has been on the possibility of using more efficient and effective algorithms to improve convergence speed and solution accuracy of the optimization model. But the model of reliability-redundancy allocation itself has not been investigated further. In this paper, we try to simplify the optimization model of the reliability-redundancy allocation problem by using the theory of survival signature. To achieve this, the information of the structure of a system is summarized by the survival signature. The reliability-redundancy allocation problem is formulated as an optimization problem with the objective of maximizing system reliability under some constraints. A new adaptive penalty function is proposed to transfer the constraint optimization problem to an unconstraint one. Then a heuristic algorithm called stochastic fractal search is applied to solve the unconstraint optimization. Moreover, the (joint) structure importance is used to measure the relative importance of components to concretely allocate the redundancy level of each component. The proposed method only needs to calculate the survival signature once, reduces the dimension of the optimization problem and provides insight into system reliability-redundancy allocation. [ABSTRACT FROM AUTHOR]

Highlights • A scaled accuracy based power allocation framework is proposed to address multitarget tracking problem in a colocated MIMO radar system. • The proposed strategy can allocate the transmit power adaptively as tracking accuracy requirement change over time and space. • The proposed strategy yields better power allocation options than the existing strategy. • Besides the colocated MIMO radar, the proposed framework can be used in other radar configurations such as the phased array radar and distributed MIMO radar systems. Abstract A colocated MIMO radar system can offer enhanced multi-target tracking (MTT) performance due to its agile multibeam working mode, in which multiple transmit beams are synthesized simultaneously using probing signals from individual colocated transmitters. To ensure that the limited transmitting power resource of a colocated MIMO radar is exploited effectively, in this paper, a scaled accuracy based power allocation framework (SA-PAF) is proposed for MTT. For a given total power budget, an overall MTT performance objective is optimized by adjusting the transmit power of each beam based on the feedback information in the tracking recursion cycle. An overall cost function (OCF), which is a combination of individual target cost functions (ITCF), is designed as the overall performance objective of the optimization problem. The necessity of ITCF is due to the fact that the impact of the state estimation error on the objective function should be different for each target due to its unique importance (or threat level) and the corresponding tracking accuracy requirement among multiple targets. The proposed OCF can quantify the global MTT performance while properly taking into account different target priorities through ITCFs. To formulate the OCF based framework as an optimization problem, we use the exponential function that employs the posterior Cramér–Rao lower bound (PCRLB) as the tracking performance measure as the instantiation of ITCF. It is shown that the proposed scheme is non-convex in the presence of a clutter. Then, the Zoutendijk method of feasible directions (ZMFD) is used to find the suboptimal power allocation solutions. Simulation results verify the superiority of the proposed power allocation algorithm in terms of achieving the effective overall performance of MTT while taking into account the differences targets priorities. [ABSTRACT FROM AUTHOR]

Highlights • Approximately achieve tradeoff between capacity and Jain's fairness that cannot increase fairness while not decrease capacity. • Analog beamforming and user grouping are designed to suppress interference. • Complexity is comparable to most of the existing scheduling approaches. Abstract This paper investigates user scheduling in millimeter-wave multiple-input multiple-output systems that can achieve a tradeoff between capacity and Jain's fairness index. By decomposing the original problem into digital beamforming design, duration allocation, and analog beamforming design, each subproblem is solvable. It is proved that the duration allocation problem possesses a property such that the convex optimization algorithm can be employed for the near optimal result. Moreover, the analog beamforming vectors and the user grouping are designed to avoid interference caused by the reuse of the same subspace. Finally, the computational complexity of the proposed approach is analyzed and is compared with that of other approaches. The simulations verify that the proposed approach can achieve the highest fairness and a higher capacity than approaches of close fairness. [ABSTRACT FROM AUTHOR]

Highlights • An adaptive reversible data hiding scheme is proposed. • The expansion bins selection for pairwise embedding is formulated as an optimal path determination problem. • A dynamic programming algorithm is proposed to solve the optimization problem with low computational complexity. • Proposed scheme can well exploit image redundancy and shows obvious advantage over some state-of-the-art works. Abstract As an efficient technique for high-dimensional reversible data hiding (RDH), pairwise prediction-error expansion (pairwise PEE) has achieved better performance comparing with the conventional PEE. With pairwise PEE, the correlations among prediction-errors are well utilized by modifying the generated two-dimensional prediction-error histogram (2D-PEH). However, its performance can be further improved since the histogram modification manner (i.e., the employed modification mapping) of pairwise PEE is fixed and independent of image content. To better utilize image redundancy, instead of embedding data based on an empirically designed modification mapping, a content dependent pairwise embedding scheme is proposed in this paper. Based on a specific division of 2D-PEH, the expansion bins selection is formulated as an optimal path determination problem, and the histogram modification mapping is adaptively determined by taking the optimal expansion bins. To reduce the computation cost, a dynamic programming algorithm is proposed to solve the optimization problem with low computational complexity. Moreover, by combining the proposed optimal expansion path with the existing one-dimensional adaptive embedding mechanism, the embedding performance can be further enhanced. The proposed method performs well and its superiority is experimentally verified comparing with pairwise PEE and some other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Highlights • Developing a new Spiking Neural P Grey Wolf Optimization System. • Novel strategies for solving non-determinism problem of Spiking Neural P systems. • Design a new mathematical model of the grey wolf optimization algorithm. • Control the copying spikes process among neurons is developed. • Consider the time control approach to avoid non-determinism inside neurons. Abstract Spiking neural P systems (SN P systems, in short) are the latest branch of membrane computing; inspired by the biological behavior of spiking neurons. They are considered true distributed and parallel systems; modeled to solve time consumption problem and presented the concept of parallelism usage in the computing field. This paper proposes novel strategies for solving non-determinism problem of SN P systems. The proposed algorithm relies on the parallelism feature to simulate the social hierarchy, tracking, encircling, and attacking behaviors in the grey wolf optimizer. It is modeled by collaboration between a set of SN P systems to get a feasible solution in polynomial time. Moreover, a new method named the power of signal is proposed to control the copying spikes process between neurons and differentiate between the arithmetic operations. Additionally, a time control approach is proposed to avoid non-determinism inside neurons that applied the determinism feature during firing rules. The theoretical and empirical experiments proved that the algorithm can successfully halt and in addition to the effectiveness of the proposed neural systems in getting an optimal solution in a reasonable time. As a result, this study is counted as a significant advancement in intelligent and optimization systems, whereas it has a direct impact on enhancing the performance of these systems and their applications. [ABSTRACT FROM AUTHOR]

Highlights • The proposed algorithm considers a double pair of evolutionary operators. • The proposed approach uses local consensus formulations with reactive responses. • The proposed method guarantees leaderless movement decisions. • The proposed algorithm outperforms several evolutionary methods. Abstract Evolutionary Computation (EC) algorithms have been proposed as stochastic methods to solve complex optimization problems. The design of EC methods typically involves the construction of empirical operators based on abstractions of animal behaviors or physical and biological phenomena. Through its operators, every EC approach proposes a particular solution to the exploration-exploitation balance which is currently considered an unsolved problem within EC literature. On the other hand, multi-agent systems have been utilized as intelligent, cooperative and self-organized structures where the synergy of simple rules creates complex interactions among agents. In this paper, a novel EC algorithm called Neighborhood-based Consensus for Continuous Optimization (NCCO) is presented. NCCO is based on typical processes present in multi-agent systems, such as local consensus formulations and reactive responses. These operations are conducted by using appropriate operators that are applied in each evolutionary stage. A traditional EC algorithm considers in its operation the application of every operator without examining its final impact in the searching process. In contrast to other EC techniques, the proposed method uses additional operators to avoid the undesirable effects produced by the over-exploitation or suboptimal exploration of conventional operations. In order to illustrate the performance and accuracy of the proposed NCCO approach, it is compared to several well-known, state-of-the-art algorithms over a set of benchmark functions and real-world design applications. The experimental results demonstrate that NCCO's performance is superior to the test algorithms. [ABSTRACT FROM AUTHOR]

Highlights • Depending on clustering algorithm used, the results may change. • Based on Multi-Algorithm Voting, robustness of cluster solutions can be improved. • OMAV is developed that uses an optimization algorithm as integrative method. • OMAV is applied to the example of country clustering using GLOBE data. • Increased robustness and reduced subjectivity are demonstrated. Abstract Currently, the influence of a single statistical cluster algorithm on the results of clustering procedures represents a major threat to the objectivity in clustering. To exemplify this question, this paper refers to country clustering in cross-cultural research. In this field, previous research has determined differing numbers of clusters, depending on choices available for the clustering procedure, leading to a high number of inconsistent results. Hence, it is argued that the variety in cluster solutions induced by the choice of different statistical cluster algorithms should be reduced. To this end, this study builds on Multi-Algorithm Voting (MAV) procedure introduced by Bittmann and Gelbard (2007) and presents an advancement to the MAV method. Specifically, MAV procedure is refined for the analysis of larger data sets using the simulated annealing algorithm for optimization. The use of this Optimized MAV (OMAV) is then demonstrated for country clustering in cross-cultural research. Specifically, a set of 57 countries is divided into 12 clusters based on work-related values obtained from GLOBE database reported in House et al. (2004). Thus, results clearly show that the objectivity of clustering results can be significantly improved based on OMAV. Implications for expert and intelligent systems on the use of OMAV are discussed. Namely, OMAV represents a powerful tool supporting the decision-making process in cluster analysis reducing the number of subjective and arbitrary decisions. Taken together, this study contributes to existing literature by providing an integrative and robust method of country clustering using OMAV and by presenting country clusters applicable to various settings. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Ensemble local kernel principal components analysis (ELKPCA) is proposed. • ELKPCA model makes width parameter selection easier. • Both global and local structure information of the original data are preserved. • E T 2 and E S P E are constructed by Bayesian inference approach. • Two case studies demonstrate the effectiveness of the ELKPCA model. Abstract A new nonlinear process monitoring algorithm called ensemble local kernel principal component analysis (ELKPCA) is proposed. Conventionally, the performance of kernel-based model depends on the width parameter selected empirically in Gaussian kernel function, which means a single parameter corresponds to a single model. Once a poor width parameter is decided, a single kernel-based model may be only effective for part faults. As a typical kernel-based method, the local kernel principal component analysis (LKPCA), considering both global and local structure information of the original data, faces the problem of width parameter selection as well. Since a single model is one-sided, an available way is to combine different single models and take advantage of them. The ensemble kernel principal component analysis (EKPCA) uses single KPCA models as its sub-models and ensemble learning approach is used to combine them. Due to inherit drawbacks from KPCA models, EKPCA only preserves global structure information of data, but ignores important local structure information. In this paper, to solve the above issues, both LKPCA and EKPCA is unified in the proposed framework. First, single LKPCA models are chosen instead and combined by using ensemble learning strategy. Then two monitoring statistics are turned into fault probabilities through Bayesian inference approach and weighted combination strategy, which makes the monitoring behavior easier and more clear. The result shows that ELKPCA model can not only take advantage of sub-LKPCA models effectively, allowing it selecting width parameter more easily and stably, but also retain both global and local structure information from input data by introducing the local structure analysis in the EKPCA model. Case studies on synthetic example and Tennessee Eastman process demonstrate the proposed method outperforms LKPCA and EKPCA and enhances monitoring performance significantly. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A general operation optimization model based on superstructure is established. • Comprehensive optimization of topological structure and equipment operation is carried out. • The optimization effect is improved by the integration of hydraulic balance knowledge and network structure rule. • Optimization ability and computational efficiency of the algorithm are improved by fusion of domain knowledge. • Simulation experiments based on actual network data are conducted to verify this method. Abstract Circulating cooling water system (CCWS) is an important auxiliary system in the industrial production process, and it is also one of the main energy-consuming units in the whole process. This paper presents a comprehensive operation optimization method of water and energy, and aims to design an optimal operation plan of the CCWS with improved system operation status and reduced system operation cost. To this end, the operation optimization model based on superstructure method is established, which is comprehensive and practical compared with previous researches in two aspects. Firstly, this model is further extended to realize the comprehensive description and synchronous optimization of network structure, equipment operation mode and equipment operation status, and the optimal operation plan of the system is thus obtained. Secondly, the application range of this model is expanded and thus the proposed method is made more general for various of networks. Moreover, proposed optimization model and the solution algorithm are improved with the fusion of domain knowledge. To be specific, the knowledge of system hydraulic balance and system operation are fused into the operation optimization problem, which improves the energy-saving optimization effect and the algorithm calculation efficiency. Finally, the practicality of the proposed operation optimization method is verified through simulation experiments based on the actual network data, and the comprehensive energy-saving rate reaches 31.3%, so the system operation costs are effectively reduced. [ABSTRACT FROM AUTHOR]

Highlights • Two binary variants of the butterfly optimization algorithm (BOA) are proposed. • Two transfer functions are used to map the continuous search space to discrete one. • Twenty one UCI datasets are utilized in the experiments. • A superior performance of the proposed binary variants is proved in the experiments. Abstract In this paper, binary variants of the Butterfly Optimization Algorithm (BOA) are proposed and used to select the optimal feature subset for classification purposes in a wrapper-mode. BOA is a recently proposed algorithm that has not been systematically applied to feature selection problems yet. BOA can efficiently explore the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The two proposed binary variants of BOA are applied to select the optimal feature combination that maximizes classification accuracy while minimizing the number of selected features. In these variants, the native BOA is utilized while its continuous steps are bounded in a threshold using a suitable threshold function after squashing them. The proposed binary algorithms are compared with five state-of-the-art approaches and four latest high performing optimization algorithms. A number of assessment indicators are utilized to properly assess and compare the performance of these algorithms over 21 datasets from the UCI repository. The experimental results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper-based algorithms, which proves the ability of BOA algorithm in searching the feature space and selecting the most informative attributes for classification tasks. [ABSTRACT FROM AUTHOR]

Highlights • A novel model based on online sequential outlier robust extreme learning (OSORELM) is proposed. • The hybrid mode decomposition (HMD) method is used to deeply decompose the original wind speed. • The OSORELM with HMD decomposition is used for short-term multi-step wind speed prediction. • The crisscross optimization (CSO) is applied to optimize the parameters of the model. Abstract As the wind energy developing, wind speed prediction is important for the reliability of wind power system and the integration of wind energy into the power network. This paper proposed a novel model based on hybrid mode decomposition (HMD) method and online sequential outlier robust extreme learning machine (OSORELM) for short-term wind speed prediction. In data pre-processing period, wind speed is deeply decomposed by HMD, which is comprised of variational mode decomposition (VMD), sample entropy (SE) and wavelet packet decomposition (WPD). The crisscross algorithm (CSO) is applied to optimize the input-weights and hidden layer biases for OSORELM, which have impact on the forecasting performance. The experiment results show that: (a) HMD is an effective way of wind speed decomposition, which can capture the characteristics of wind speed time series accurately and thus promote the prediction performance; (b) the OSORELM performs better than offline models in practical forecasting; (c) the proposed forecasting model has greatly improved the accuracy in multi-step wind speed forecasting. [ABSTRACT FROM AUTHOR]

Highlights • Quantitative approach to select master degrees of freedom in shear building models. • Massive simulation were executed and their results analyzed. • Three modal error norms and one displacement error signal norm are defined. • Symmetric choice of master degrees of freedom yields the smallest errors. • Results quantitatively confirm "educated" choices made in Civil Engineering. Abstract A quantitative criteria to select master degrees of freedom when identifying reduced order models (ROMs) of one and three dimensional building models is proposed. The goal is achieved by performing intensive simulations of seismic responses of a large set of regular shear building models, that is constructed around a nominal model, whose inter-story mass and stiffness are randomly modified. Different levels of model reduction are selected and, for each one, an exhaustive selection of master degrees of freedom is realized. The parameters corresponding to all the ROMs are recovered by applying recursive least squares (RLS). Later, the response of the full order model (FOM) is compared with each one of the multiple ROMs response to the same seismic excitation signal, using for that purpose three error norms related with modal information: period, mode shape and participation factors and one error norm related to displacement errors. To analyze results, ROMs are classified in three groups that depend on their selection; the first group corresponds to lower-middle-upper story selection, the second group for lower-upper stories and the third group to all other possible cases. Results indicate first that the modal error norms can be used as a good predictor of the displacement error norms and second that symmetric selections of stories in the ROMs are the most accurate. These results are consistent with qualitative or "educated" choices made normally in Civil Engineering, that however, lacked a quantitative criteria similar to the one presented in this paper. Finally, a simulated example where a twelve stories model is reduced to a three stories model is presented to illustrate the results. [ABSTRACT FROM AUTHOR]

Highlights • Algorithm for high-dimensional dynamic OD calibration for large-scale networks. • High-dimensional problem with 16,200 decision variables for a large-scale Singapore network. • Proposed method improves the objective function by 1 order of magnitude compared to benchmark methods. • Improves fit to link counts by 77% compared to benchmark methods. Abstract This paper considers offline dynamic OD (origin-destination) calibration problems for large-scale simulation-based network models. We formulate the problem as a simulation-based optimization (SO) problem and propose a scalable and efficient metamodel SO algorithm. For a network with n links and a problem with T time intervals and Z OD pairs per time interval, the corresponding SO problem is a high-dimensional problem of dimension T · Z. At each iteration of the SO algorithm, we solve a set of T independent analytical and differentiable metamodel optimization problems, each of dimension Z. The T analytical problems are constrained by n nonlinear equality constraints. Hence, they scale linearly with the number of links in the network and independently of other network attributes, such as the dimension of the route choice set or the link lengths. The temporal correlation, of the link performance metrics across time intervals, is approximately captured through the parameters of the metamodel. Since the T analytical optimization problems are decoupled and can be solved independently, the proposed approach scales independently of the number of calibration time intervals, making it suitable for the calibration of demand over numerous time periods. The approach is efficient and scalable. It is suitable to address high-dimensional calibration problems for large-scale network models. It is benchmarked on both a toy network and a Singapore network versus two general-purpose algorithms: SPSA and a derivative-free pattern search algorithm. The validation experiments indicate that the proposed method identifies points with objective function estimates that outperform the benchmark methods by 1 to 2 orders of magnitude. As the problem dimension and the temporal correlation, across time intervals, increase, so does the magnitude by which the proposed method outperforms the benchmark methods. The Singapore case study considers a problem of dimension 16,200. This is 2 orders of magnitude higher than what is currently considered high-dimensional for continuous SO problems. The proposed method identifies solutions with an estimated objective function that is 1 order of magnitude better than those of the benchmark methods. It yields solutions that provide an average, across time intervals, improvement to link counts of 77%, compared to the benchmark methods. The case study also illustrates the robustness of the method to the quality of the initial points. [ABSTRACT FROM AUTHOR]

Abstract The aim of this paper is to present a signal processing algorithm that, applied to the raw Locked Mode signal, allows us to obtain a disruption indicator in principle exploitable on different tokamaks. A common definition of such an indicator for different machines would facilitate the development of portable systems for disruption prediction, which is becoming of increasingly importance for the next tokamak generations. Moreover, the indicator allows us to overcome some intrinsic problems in the diagnostic system such as drift and offset. The behavior of the proposed indicator as disruption predictor, based on crossing optimized thresholds of the signal amplitude, has been analyzed using data of both JET and ASDEX Upgrade experiments. A thorough analysis of the disruption prediction performance shows how the indicator is able to recover some missed and tardy detections of the raw signal. Moreover, it intervenes and corrects premature or even wrong alarms due to, e.g., drifts and/or offsets. [ABSTRACT FROM AUTHOR]

Abstract Bacterial foraging optimization (BFO), based on the social foraging behaviors of bacteria, is a new intelligent optimizer. It has been widely accepted as an optimization algorithm of current interest for a variety of fields. However, compared with other optimizers, the BFO possesses a poor convergence performance over complex optimization problems. To improve the optimization capability of the BFO, in this paper a bare bones bacterial foraging optimization (BBBFO) algorithm is developed. First, a chemotactic strategy based on Gaussian distribution is incorporated into this method through making use of both the historical information of individual and the share information of group. Then the swarm diversity is introduced in the reproduction strategy to promote the exploration ability of the algorithm. The performance of BBBFO is verified on various benchmark functions, the comparative results reveal that the proposed approach is more superior to its counterparts. [ABSTRACT FROM AUTHOR]

Abstract As one of the most advanced non-destructive analytical techniques for nuclear wastes, tomographic Gamma Scanning (TGS) is able to give accurate quantitative and qualitative measurements of nuclear waste barrels. OSEM (Ordered Subsets Expectation Maximization) has been used in transmission TGS image reconstruction on account of its good reconstruction quality and ideal convergence rate. In this paper, an improved method—NMO-OSEM (Non-minimization optimization OSEM) was proposed, it's an iterative algorithm with corrected initial values optimized by non-minimization optimization method. To evaluate its performance, a TGS system is used to perform transmission measurements on barreled nuclear wastes. The results show: ①Compared with the reconstructed images by traditional OSEM under 6 transmission energies (122 keV, 344 keV, 779 keV, 964 keV, 1112 keV, 1408 keV), the improved NMO-OSEM has a great advantage in reducing the artifacts and effectively improving the quality of the reconstructed images. ②the attenuation coefficients values of 72 voxels under 3 emission energies (662 keV, 1173 keV and 1332 keV) reconstructed by the proposed algorithm are more accurate (range of error: 0.22%–13.83%) than reconstructed by traditional OSEM (range of error: 1.31%–32.21%), which proves this method has more stable reconstruction precision and could be regarded as an ideal option for real applications. Highlights • An improved initial value optimization algorithm (NMO-OSEM) is proposed for transmission reconstruction in TGS. • A TGS system is adopted for transmission measurements on simulated barreled nuclear wastes. • NMO-OSEM can preferable suppress image noise and reduce image artifacts. • Reconstructed values of NMO-OSEM has smaller numerical error (0.22%-13.83%) than that of traditional OSEM. [ABSTRACT FROM AUTHOR]

Highlights • Introduction and modeling of the Packaging and Shipping Problem motivated by a real-life e-commerce application. • Design of a three-phase matheuristic to efficiently tackle the Packaging and Shipping Problem. • Computational comparison with a state-of-the art commercial solver. Abstract E-commerce has been continuously growing in the last years to a primary retail market. Recently in France, the threshold of 1 billion of online transactions was overcome. Due to a high demand fluctuation of e-commerce, the workforce sizing for the logistic chain is a challenging problem. Companies have to develop good strategies to have a sustainable workforce size while guaranteeing a high-level service. In this paper, we consider the management of the workforce for a warehouse of an e-commerce company. Specifically, we address issues as i) How the workforce at the warehouse can be determined; ii) What is the daily operational production planning; iii) How the demand peaks can be smoothed, and the production maintained ideally constant over the time horizon. To provide answers to these issues, we introduce the Packaging and Shipping Problem (PSP). The PSP looks for a solution approach that jointly determines the workforce over a multi-period horizon and daily operational plans while minimizing the total logistics cost. We consider two strategies that aim to enhance the flexibility of the process and the efficiency of resources use: reassignment and postponement. To tackle the Packaging and Shipping Problem we propose a model, and a three-phase matheuristic. This heuristic is proved to be competitive with respect to the direct solution of the model with a commercial solver on real-life based instances. [ABSTRACT FROM AUTHOR]

Highlight • This paper proposes an improved grasshopper optimization strategy (GOA) for continuous optimization. • We have applied the improved GOA successfully to the financial stress prediction problem. • Levy flight, Gaussian mutation and opposition-based learning are embedded in GOA. • The experimental results reveal the improved performance of the proposed algorithm. Abstract This study proposed an improved grasshopper optimization algorithm (GOA) for continuous optimization and applied it successfully to the financial stress prediction problem. GOA is a recently proposed metaheuristic algorithm inspired by the swarming behavior of grasshoppers. This algorithm is proved to be efficient in solving global unconstrained and constrained optimization problems. However, the original GOA has some drawbacks, such as easy to fall into local optimum and slow convergence speed. To overcome these shortcomings, an improved GOA which combines three strategies to achieve a more suitable balance between exploitation and exploration was established. Firstly, Gaussian mutation is employed to increase population diversity, which can make GOA has stronger local search ability. Then, Levy-flight strategy was adopted to enhance the randomness of the search agent's movement, which can make GOA have a stronger global exploration capability. Furthermore, opposition-based learning was introduced into GOA for more efficient search solution space. Based on the improved GOA, an effective kernel extreme learning machine model was developed for financial stress prediction. As the experimental results show, the three strategies can significantly boost the performance of GOA and the proposed learning scheme can guarantee a more stable kernel extreme learning machine model with higher predictive performance compared to others. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a comprehensive and accurate algorithm based on circuit model to design reflect-array antennas (RAAs). The proposed algorithm can extract geometry of RAA structure corresponding to radiation parameters without employing any approximation or optimization intervention into the algorithm. Simulation results for some comprehensive examples, verify the validity of the proposed algorithm. This method is simple, fast, accurate, and can be expanded to all species of RAAs with arbitrary geometries. One of the main problems confronted in RAAs is low bandwidth characteristics. To attain wide bandwidth, we exploited the proposed algorithm with combination of multi-layer design of RAAs and subwavelength element. Convergence is observed between simulation and fabrication results, asserting good accuracy in method. In 10–15 GHz band, the maximum gain and -1dB bandwidth of two-layer antenna is 31.4 dB and 18.5%, respectively. [ABSTRACT FROM AUTHOR]

Abstract Multi-label learning is the problem where each instance is associated with multiple labels simultaneously. Binary Relevance (BR) is a representative algorithm for multi-label learning. However, it may suffer the class-imbalance issue especially when the label space is large. Besides, it ignores the label correlations, which is of importance to improve the performance. Moreover, labels might have positive and negative correlations in real applications, but existing methods seldom exploit the negative label correlations. In this paper, we propose a novel Cost-sensitive multi-label learning model with Positive and Negative Label pairwise correlations (CPNL), which extends BR to tackle the above issues. The kernel extension of the linear model is also provided to explore complex input–output relationships. Moreover, we adopt two accelerated gradient methods (AGM) to efficiently solve the linear and kernel models. Experimental results show that our approach CPNL achieves a competitive performance to some state-of-the-art approaches for multi-label learning. Highlights • We propose a novel cost-sensitive multi-label learning model with positive and negative label pairwise correlations. • We introduce a new regularizer to exploit the negative label pairwise correlations. • Kernel extension of the linear model is provided to explore nonlinear input–output relationships. • Two accelerated gradient methods (AGM) are adopted to efficiently solve the linear and kernel model. • Extensive experiments have verified the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Abstract Existing multi-view clustering algorithms require that the data is completely or partially mapped between each pair of views. However, this requirement could not be satisfied in many practical settings. In this paper, we tackle the problem of multi-view clustering on unmapped data in the framework of NMF based clustering. With the help of inter-view constraints, we define the disagreement between each pair of views by the fact that the indicator vectors of two samples from two different views should be similar if they belong to the same cluster and dissimilar otherwise. The overall objective of our algorithm is to minimize the loss function of NMF in each view as well as the disagreement between each pair of views. Furthermore, we provide an active inter-view constraints selection strategy which tries to query the relationships between samples that are the most influential and samples that are the farthest from the existing constraint set. Experimental results show that, with a small number of (either randomly selected or actively selected) constraints, the proposed algorithm performs well on unmapped data, and outperforms the baseline algorithms on partially mapped data and completely mapped data. [ABSTRACT FROM AUTHOR]

Highlights • we use background weight map to provide seeds for manifold ranking. • we extend the traditional manifold ranking to second-order formula. • we establish a third-order smoothness framework to optimize the saliency map. Abstract Graph-based approaches for saliency detection have attracted much attention and been exploited widely in recent years. In this paper, we present a new method to promote the performance of existing manifold ranking algorithms. Initially, we use background weight map to provide seeds for manifold ranking; Next, we extend the traditional manifold ranking to second-order formula and add a weight mask to its fitting term. Finally, for further improvement of the performance, we establish a third-order smoothness framework to optimize the saliency map. In the experiments, we compare two versions (manifold ranking with and without optimization) of our model with seven previous methods and test them on several benchmark datasets. Different kinds of strategies are also adopted for evaluation and the results demonstrate that our method achieves the state-of-the-art. [ABSTRACT FROM AUTHOR]

Highlights • A hybrid DE/WOA method is proposed for parameter extraction of PV models. • DE/WOA can combine the exploration of DE with the exploitation of WOA well. • Thirteen numerical benchmark functions are firstly employed to verify DE/WOA. • DE/WOA is then applied to three different benchmark test PV models. • DE/WOA is finally applied to a practical PV power station in China. Abstract Parameter extraction of solar photovoltaic (PV) models is a typical complex nonlinear multivariable strongly coupled optimization problem. The original differential evolution (DE) is good at exploring the search space and locating the region of global optimum, but it is slow at exploitation of the solutions. Quite the opposite, the original whale optimization algorithm (WOA) is good at exploiting the population information, but it easily suffers from premature convergence. In such a context, in this paper, an effective hybrid method named DE/WOA by combining the exploration of DE with the exploitation of WOA is proposed for extracting the accurate parameters of PV models. A set of 13 numerical benchmark functions with different characteristics is firstly employed to verify the performance of DE/WOA. Then, DE/WOA is applied to parameter extraction of three PV models, i.e., single diode, double diode, and PV module models. Finally, DE/WOA is implemented to a practical PV power station in the Guizhou Power Grid of China to further validate its effectiveness under different irradiances, temperatures, and dynamic weather conditions. All the experimental results comprehensively demonstrate that DE/WOA performs significantly better than the original DE, WOA, and five advanced variants of them and is highly competitive with some recently-proposed parameter extraction methods. [ABSTRACT FROM AUTHOR]

This paper proposes an improved version of the grasshopper optimization algorithm (GOA) based on the opposition-based learning (OBL) strategy called OBLGOA for solving benchmark optimization functions and engineering problems. The proposed OBLGOA algorithm consists of two stages: the first stage generates an initial population and its opposite using the OBL strategy; and the second stage uses the OBL as an additional phase to update the GOA population in each iteration. However, the OBL is applied to only half of the solutions to reduce the time complexity. To investigate the performance of the proposed OBLGOA, six sets of experiment series are performed, and they include twenty-three benchmark functions and four engineering problems. The experiments revealed that the results of the proposed algorithm were superior to those of ten well-known algorithms in this domain. Eventually, the obtained results proved that the OBLGOA algorithm can provide competitive results for optimization engineering problems compared with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The aim of this paper is to design an algorithm based on nonsmooth optimization techniques to solve the minimum sum-of-squares clustering problems in very large data sets. First, the clustering problem is formulated as a nonsmooth optimization problem. Then the limited memory bundle method [Haarala et al., 2007] is modified and combined with an incremental approach to design a new clustering algorithm. The algorithm is evaluated using real world data sets with both the large number of attributes and the large number of data points. It is also compared with some other optimization based clustering algorithms. The numerical results demonstrate the efficiency of the proposed algorithm for clustering in very large data sets. [ABSTRACT FROM AUTHOR]

This paper presents a novel metaheuristic algorithm called queuing search (QS), which is inspired from human activities in queuing. Some common phenomena are considered in QS: (1) customers actively follow the queue that provides fast service; (2) each customer service is mainly affected by the staff or customer itself; and (3) a customer can be influenced by others during the service when the queue order is not strictly maintained. The performance of QS is tested on 30 bound-constrained benchmark functions scalable with 30 and 100 dimensions from CEC 2014, 5 standard and 4 challenging constrained engineering optimization problems. Meanwhile, comparisons are performed among the results of QS and some state-of-the-art or well-known metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Abstract This paper describes three variants of a counterexample guided inductive optimization (CEGIO) approach based on Satisfiability Modulo Theories (SMT) solvers. In particular, CEGIO relies on iterative executions to constrain a verification procedure, in order to perform inductive generalization, based on counterexamples extracted from SMT solvers. CEGIO is able to successfully optimize a wide range of functions, including non-linear and non-convex optimization problems based on SMT solvers, in which data provided by counterexamples are employed to guide the verification engine, thus reducing the optimization domain. The present algorithms are evaluated using a large set of benchmarks typically employed for evaluating optimization techniques. Experimental results show the efficiency and effectiveness of the proposed algorithms, which find the optimal solution in all evaluated benchmarks, while traditional techniques are usually trapped by local minima. Highlights • We describe three counterexample guided inductive optimization (CEGIO) approaches based on Satisfiability Modulo Theories. • CEGIO-Generalized is used for solving any constrained optimization problem. • CEGIO-Simplified is employed if information about the minima location is known. • CEGIO-Fast is employed for convex functions to speed up the optimization process. • Our proposed approaches outperform traditional optimization techniques employed for convex and non-convex functions. [ABSTRACT FROM AUTHOR]

Highlights • An architecture of microgrid energy management system was established. • The architecture was designed for China in the context of Energy Internet. • Two-stage robust optimization model and algorithm were proposed. • Case study was conducted using a real case of Beijing Jinfeng Industrial Park. Abstract In order to address the practical challenges posed by the increasing penetration of distributed energy resources and electric vehicles, the evolution from traditional power systems to Energy Internet and the rapidly changing market and policy environments in China, this paper proposes an Energy-Internet-oriented architecture of microgrid energy management system for China, considering specific technical requirements, markets and policies in China. Following the proposed architecture, the robust operation of a multi-energy microgrid with renewable energy and load uncertainties is formulated as a two-stage robust optimization problem and the column and constraints generation algorithm is used to solve it. A real-life case study in China, Beijing Goldwind Industrial Park, is conducted to demonstrate the proposed architecture and method. Simulation results show that by exporting surplus generation to the external customers and bulk power grid, as an important feature of the market dimension of the proposed architecture, the revenue of the microgrid is improved significantly compared to the traditional business mode. Moreover, as important features of technology and regulation dimensions of the proposed architecture, it is shown that smart charging of electric vehicles is able to improve the revenue of the microgrid as well. Finally, the results also show that the proposed robust operation method achieves robustness at the cost of lower revenue in non-worst scenarios. [ABSTRACT FROM AUTHOR]

Abstract Containing many classic optimization problems, the family of vertex deletion problems has an important position in algorithm and complexity study. The celebrated result of Lewis and Yannakakis gives a complete dichotomy of their complexity. It however has nothing to say about the case when the input graph is also special. This paper initiates a systematic study of vertex deletion problems from one subclass of chordal graphs to another. We give polynomial-time algorithms or proofs of NP-completeness for most of the problems. In particular, we show that the vertex deletion problem from chordal graphs to interval graphs is NP-complete. [ABSTRACT FROM AUTHOR]

Abstract Nonlinear Auto-Regressive model with eXogenous input (NARX) is one of the most popular black-box model classes that can describe many nonlinear systems. The structure determination is the most challenging and important part during the system identification. NARX can be formulated as a linear-in-the-parameters model, then the identification problem can be solved to obtain a sparse solution from the viewpoint of the weighted l 1 minimization problem. Such an optimization problem not only minimizes the sum squares of model errors but also the sum of reweighted model parameters. In this paper, a novel algorithm named Bayesian Augmented Lagrangian Algorithm (BAL) is proposed to solve the weighted l 1 minimization problem, which is able to obtain a sparse solution and enjoys fast computation. This is achieved by converting the original optimization problem into distributed suboptimization problems solved separately and penalizing the overall complex model to avoid overfitting under the Bayesian framework. The regularization parameter is also iteratively updated to obtain a satisfied solution. In particular, a solver with guaranteed convergence is constructed and the corresponding theoretical proof is given. Two numerical examples have been used to demonstrate the effectiveness of the proposed method in comparison to several popular methods. [ABSTRACT FROM AUTHOR]

Given a graph G = ( V , E ) of order n and maximum degree Δ , the NP -complete S - labeling problem consists in finding a labeling of G , i.e. a bijective mapping ϕ : V → { 1 , 2 … n } , such that SL ϕ ( G ) = ∑ u v ∈ E min { ϕ ( u ) , ϕ ( v ) } is minimized. In this paper, we study the S - labeling problem, with a particular focus on algorithmic issues. We first give intrinsic properties of optimal labelings, which will prove useful for our algorithmic study. We then provide lower bounds on SL ϕ ( G ) , together with a generic greedy algorithm, which collectively allow us to approximate the problem in several classes of graphs—in particular, we obtain constant approximation ratios for regular graphs and bounded degree graphs. We also show that deciding whether there exists a labeling ϕ of G such that SL ϕ ( G ) ≤ | E | + k is solvable in O ∗ ( 2 2 k ( 2 k ) ! ) time, thus fixed-parameterized tractable in k . We finally show that the S - Labeling problem is polynomial-time solvable for two classes of graphs, namely split graphs and (sets of) caterpillars. [ABSTRACT FROM AUTHOR]

Plant-wide oscillation detection is an important task in the maintenance of large-scale industrial control systems, owing to the fact that in an interactive multi-loop environment oscillation generated in one loop may propagate to the different parts of the plant. In such a scenario, it is required that different loops oscillating due to a common cause and hence similar frequency may be grouped together. In this paper an adaptive method for plant-wide oscillation detection based on multivariate empirical mode decomposition (MEMD) along with a grouping algorithm is proposed. The method can identify multiple oscillation groups among different variables as well as variables with random noise only. The proposed method is also applicable to both non-linear and non-stationary time series where the techniques based on the conventional Fourier analysis are prone to errors. Within each group that oscillate due to a common cause, the method can also indicate the location of the probable root cause of oscillations. The efficacy of the proposed method is established with the help of both simulation and industrial case studies. [ABSTRACT FROM AUTHOR]

In this paper, an integrated coal gasification combined cycle system for the production of hydrogen and electricity is optimized in terms of energy and exergy efficiencies, and the amount and cost of the produced hydrogen and electricity. The integrated system is optimized by focusing on the conversion process of coal to syngas. A novel optimization process is developed which integrates an artificial neural network with a genetic algorithm. The gasification system is modeled and simulated with Aspen Plus for large ranges of operating conditions, where the artificial neural network method is used to represent the simulation results mathematically. The mathematical model is then optimized using a genetic algorithm method. The optimization demonstrates that the lower is the grade of coal of the three considered coals, the less expensive is the hydrogen and electricity that can be produced by the considered integrated gasification combined cycle (IGCC) system. [ABSTRACT FROM AUTHOR]

In this paper, we examine the mathematical structure of chemical reaction networks with the goals of identifying reaction invariant states and determining their physical significance. A combined species-reaction graph/convex analysis approach is developed to find semi-positive invariant states associated with a reaction network. Application of this graphical/algebraic reaction network analysis approach to four different chemical processes reveals that reaction invariants can represent conserved quantities other than elemental balances. [ABSTRACT FROM AUTHOR]

Artificial gas-lift (AGL) technique is commonly used to enhance oil production when the reservoir pressure in wells is not enough to sustain acceptable oil flow rate. However, the gas-lift wells are prone to instability, characterized by regular oscillations of pressure and flow. This phenomenon is known as casing-heading instability. It results in production loss and negative impact on downstream equipment, and has been a challenging problem to both industry and academia. In this paper, a novel concept of optimal tracking control is proposed for stabilization and operating mode transition in gas-lift wells when casing-heading phenomenon occurs. The stability of artificial gas-lift process is ensured by manipulating both gas lift choke and oil production choke, where the openings of both choke valves can vary from fully closed to fully open. Through the simulation of the open-loop system, a stability map of AGL process is produced. Then a trajectory optimization algorithm is developed based on this stability map, which is synthesized with a tracking controller to achieve trajectory optimization control. Also, a nonlinear state observer is designed to ensure estimation of unmeasurable variables. Through simulation studies, the effectiveness of proposed trajectory optimization control is demonstrated. [ABSTRACT FROM AUTHOR]

As the airline industry is ever expanding, companies are increasing their fleet sizes to obtain greater market shares. Moreover, as the airlines seek more growth, the complexity and size of the airline crew scheduling problem, which is one of the major planning problems in the industry, is also increasing. For this reason, companies dedicate resources to solve this problem, and lease software at great expense from external sources. Rigorous mathematical models and algorithms are used in solving these problems. This paper presents a survey of airline crew scheduling problems, and their proposed solutions from the literature. As a conclusion, prospective studies will be proposed and discussed with the aim of developing better solutions for airline crew scheduling problems in the future. [ABSTRACT FROM AUTHOR]

Highlights • We consider Infeasibility resolution for multi-purpose batch process scheduling. • We present a systematic approach for infeasibility diagnosis. • We exploit the known structure of scheduling models to isolate interpretable infeasible sets of constraints. • We demonstrate the power of the algorithm on infeasible instances of the Westenberger–Kallrath multipurpose batch process. Abstract Scheduling decisions give rise to some of the most challenging optimization problems in the process industry. Formulating mathematical models for scheduling problems and devising tailored solution algorithms for these models has been the main thrust of previous literature. In this work, we focus on analyzing and resolving the cause of bottlenecks and infeasibilities in generic scheduling problems. We present a systematic approach for infeasibility diagnosis. Our approach exploits the known structure of scheduling models to isolate interpretable infeasible sets of constraints. We demonstrate the power of the algorithm on infeasible instances of the Westenberger–Kallrath multipurpose batch process modeled using a state-task network (STN) representation. The methodology presented in the paper is able to successfully analyze the cause of infeasibility and provide recommendations for resolving it. We also demonstrate how these insights and recommendations can be presented to scheduling operators with little or no optimization expertise in an intuitive manner. [ABSTRACT FROM AUTHOR]

Highlights • A two phase algorithm for constrained black-box problems is presented. • It does not require a feasible initial point and can handle hard constraints. • Performance compared to NOMAD and COBYLA by applying on large set of problems. • Applied to optimize a process involving nonlinear algebraic and partial differential equations. Abstract This paper presents an algorithm for constrained black-box and grey-box optimization. It is based on surrogate models developed using input-output data in a trust-region framework. Unlike many current methods, the proposed approach does not require feasible initial point and can handle hard constraints via a novel optimization-based constrained sampling scheme. A two-phase strategy is employed, where the first phase involves finding feasible point through minimizing a smooth constraint violation function (feasibility phase). The second phase improves the objective in the feasible region using the solution of the feasibility phase as starting point (optimization phase). The method is applied to solve 92 test problems and the performance is compared with established derivative-free solvers. The two-phase algorithm outperforms these solvers in terms of number of problems solved and number of samples used. We also apply the algorithm to solve a chemical process design problem involving highly-coupled, nonlinear algebraic and partial differential equations. [ABSTRACT FROM AUTHOR]

Abstract The transformation of imbalanced networks into balanced ones has been widely studied, but the reverse transformation of balanced structure has received little attention. A balanced structure may sometimes reflect undesirable collective action, and breaking this balance could be socially beneficial. In this paper, we study the reverse transformation of balanced structure and propose a model for reversing structural balance. We suggest a greedy algorithm and a memetic algorithm that break structural balance by changing node attributes. Experiments show that our algorithms can produce this reversed structural balance quite efficiently. Highlights • We introduce the concept of reverse transformation of balanced structure. • Reversing structural balance is modeled as an optimization problem. • We propose a memetic algorithm to optimize the reversed structural balance. • The experiments show that our algorithm can efficiently break structural balance. [ABSTRACT FROM AUTHOR]

Optical tomography is an imaging modality that explores the distribution of optical parameters in tissues. In this paper, the regularization jointing both T V and L 1 norm is studied for absorption parameter identification based on radiative transport equation. The T V + L 1 framework is introduced containing L 2 data fidelity, T V regularizer and L 1 regularizer. We demonstrate the existence, stability and convergence of the minima with respect to this T V + L 1 regularization. A novel algorithm for solving related optimization problem is proposed based on reweighted method and technique of split-Bregman. Simulations are performed to show that the proposed reweighted T V + L 1 regularization is more capable of preserving geometric structure of inclusions, quantifying values of absorption parameter and promoting fast convergence compared with T V or L 1 regularization, and is potential for breast cancer imaging. Moreover, it is robust to noise. [ABSTRACT FROM AUTHOR]

This paper deals with the design and implementation of a Model Predictive Control (MPC) algorithm for an industrial recirculating cooling water system. The plant is composed of three large reservoirs, four pumping units, three cooling towers with modulating fans, and a piezometric tower. A physical model of the system has been derived from balance equations and has been validated with experimental data. Then, a nonlinear state observer has been developed to estimate unmeasured quantities, such as the water flow rates and temperatures recirculating from the utilities. Finally, an MPC algorithm has been designed by reformulating the original nonlinear system in terms of a linear time-varying one to improve the computational efficiency of the method. The presence of on/off actuators, pumps, and fans, makes the resulting optimization problem a Mixed Integer Quadratic Programming one. Extensive simulation results and comparisons with the relay-based control scheme currently implemented witness the potentialities of the proposed solution in terms of energy savings, better control, and lifetime of the equipments. The proposed MPC algorithm is currently being tested on the real plant, its very satisfactory performance confirm the results of the simulation experiments. [ABSTRACT FROM AUTHOR]

Compliant mechanism-based microdevices with multiple inputs and multiple outputs have a wide range of applications in precision mechanics, e.g., cell manipulation, electronic microscopy and MEMS (Micro-Electro-Mechanical Systems). In designing this kind of microdevice, the movement coupling among the microdevices becomes critical because many inputs and outputs are involved. This paper presents a systematic method for designing fully decoupled compliant mechanisms with multiple degrees of freedom by using topology optimization. An optimization formulation is posed by considering both output coupling and input coupling issues to achieve fully decoupled motion. The SIMP (Solid Isotropic Material with Penalization) and MMA (Method of Moving Asymptotes) methods are adopted to identify the optimized material distribution in the design domain. Several numerical examples are presented to demonstrate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we provide a novel fractional particle swarm optimization (FPSO) algorithm. The traditional PSO is one of the most well-known bio-inspired algorithms used in optimization problems, which basically consists of a number of particles that collectively move in search of the global optimum. Nevertheless, despite its success over the past 20 years, the PSO is also known to be unable to converge, and even stagnate, in many complex problems with multiple local optima. In order to overcome this drawback, this paper proposes a modified version of the PSO algorithm, considering a fractional calculus approach. Stability results evaluation is carried out to analytically prove the convergence of the fractional extensions. This is naturally followed by simulation results to test the fractional-based PSOs under several well-known objective functions, thus highlighting the relationship between the fractional order velocity and position of particles with the convergence of the algorithm. Experimental results show that the FPSO and its variants significantly outperform the traditional PSO. [ABSTRACT FROM AUTHOR]

Visual recognition of crowd dynamics has had a huge impact on several applications including surveillance, situation awareness, homeland security and intelligent environments. However, the state-of-the-art in crowd analysis has become diverse due to factors such as: (a) the underlying definition of a crowd, (b) the constituent elements of the crowd processing model, (c) its application, hence (d) the dataset and (e) the evaluation criteria available for benchmarking. Although such diversity is healthy, the techniques for crowd modelling thus developed have failed to establish credibility therefore becoming unreliable and of questionable validity across different research disciplines. This paper aims to give an account of such issues by deducing key statistical evidence from the existing literature and providing recommendations towards focusing on the general aspects of techniques rather than any specific algorithm. The advances and trends in crowd analysis are drawn in the context of crowd modelling studies published in leading journals and conferences over the past 7 years. Finally, this paper shall also provide a qualitative and quantitative comparison of some existing methods using various publicly available crowd datasets to substantiate some of the theoretical claims. [ABSTRACT FROM AUTHOR]

Distributed generation can be defined as power generation by small scale generating units that are installed at distribution systems. The penetration of distributed generation (DG) units in electric distribution systems is continually increasing. The process of finding optimal type, location and size of DG units is called “DG allocation”. DG allocation is a hot area of research and represents a difficult problem in electrical power engineering. In this paper, the existing research works on DG allocation problem are reviewed from viewpoint of their used optimisation algorithms, objectives, decision variables, DG type, applied constraints and kind of uncertainty modelling. Based on the review of existing research works, the research gaps are identified and some helpful recommendations for future research on DG allocation will be provided. The author strongly believes that this paper can be helpful for researchers and engineers in the related field. [ABSTRACT FROM AUTHOR]

In mobile wireless sensor network, coverage and energy conservation are two prime issues. Sensor movement is required to achieve high coverage. But sensor movement is one of the main factors of energy consumption in mobile wireless sensor network. Therefore, coverage and energy conservation are correlated issues and quite difficult to achieve at the same time. In this paper, these conflicting issues are considered, using one of the latest Bio-inspired algorithms, known as Glowworm Swarm Optimization algorithm. Considering the limited energy of sensors, this paper presents an Energy Efficient Multi-Parameter Reverse Glowworm Swarm Optimization (EEMRGSO) algorithm, to move the sensors in an energy efficient manner. Our proposed algorithm reduces redundant coverage area by moving the sensors from densely deployed areas to some predefined grid points. In this proposed algorithm, energy consumption is reduced by decreasing the number of moving sensors as well as the total distance traversed. Simulation results show that, our proposed EEMRGSO algorithm reduces total energy consumption utmost 60% compared to the existing approach based on Glowworm Swarm Optimization algorithm. At the same time, our proposed algorithm reduces the number of overlapped sensors significantly and achieves an effective coverage of 80–89% approximately. [ABSTRACT FROM AUTHOR]