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An edge-bicolored graph G = ( V , R ∪ B ) is called Red/Blue-split graph if there exists a partition I B and I R of V such that I B and I R are independent sets in ( V , B ) and ( V , R ) , respectively. Red/Blue-split graphs generalize several well studied graph classes including split graphs, bipartite graphs and König–Egerváry graphs. In this paper we consider the algorithmic complexity of various optimization problems like minimum edge (or vertex) deletion and maximum edge (or vertex) induced subgraph related to Red/Blue split graphs. All these problems are NP -hard and thus we look at them from algorithmic paradigms that are meant for coping with NP -hardness. We obtain various hardness as well as algorithmic results for these problems in the realm of approximation algorithms and parameterized complexity. The main tool we use to obtain all our results is polynomial time transformations between appropriate problems. On the way, we also resolve some problems related to inapproximability about certain optimization problems mentioned by Korach et al. (2006) [17] . [ABSTRACT FROM AUTHOR]

Constructing a visual appearance model is essential for visual tracking. However, relying only on the visual model during appearance changes is insufficient and may even interfere with achieving good results. Although several visual tracking algorithms emphasize motional tracking that estimates the motion state of the object center between consecutive frames, they suffer from accumulated error during runtime. As neither visual nor motional trackers are capable of performing well separately, several groups have recently proposed simultaneous visual and motional tracking algorithms. However, because tracking problems are often NP-hard, these algorithms cannot provide good solutions for the reason that they are driven top-down with low flexibility and often encounter drift problems. This paper proposes a spiral visual and motional tracking (SVMT) algorithm which, unlike existing algorithms, builds a strong tracker by cyclically combining weak trackers from both the visual and motional layers. In the spiral-like framework, an iteration model is used to search for the optimum until convergence, with the potential for achieving optimization. Three learned procedures including visual classification, motional estimation, and risk analysis are integrated into the generalized framework and implement corresponding modifications with regard to their performances. The experimental results demonstrate that SVMT performs well in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

Comparing to the low-level local features, transform invariant low-rank textures (TILT) can in some sense globally rectify a large class of low-rank textures in 2D images, and thus more accurate and robust. However, the existing algorithms based on the alternating direction method (ADM) and the linearized alternating direction method with adaptive penalty (LADMAP), suffer from the weak robustness and the local minima, especially with plenty of corruptions and occlusions. In this paper, instead of exploiting optimization methods, we propose to build a hierarchical Bayesian model to TILT and then a variational method is implemented for Bayesian inference. Instead of point estimation, the proposed Bayesian approach introduces the uncertainty of the parameters, which has been proven to have much less local minima. Experimental results on both synthetic and real data indicate that our new algorithm outperforms the existing algorithms especially for the case with corruptions and occlusions. [ABSTRACT FROM AUTHOR]

The segmentation of tunnel boring machine (TBM) time series plays a crucial role in analyzing TBM operating statuses and mining potential information from the collected signals. In this paper, a novel algorithm named SDP–RLR is proposed to segment multivariate TBM time series with outliers caused by harsh operating environments and changeable tunneling statuses. In this algorithm, the 3 σ rule is extended to externally studentized residuals of a linear regression and used to identify the outliers in each segment of the input time series. An outlier removal penalty term is added to the segment errors to avoid regarding borderline data points as outliers. As a segmentation optimization algorithm, dynamic programming (DP) is improved to scalable dynamic programming (SDP) in this paper by combining time series in two stages to reduce computational costs. In the first stage, consecutive time points are integrated to reduce the calculations required for DP, while the second stage refines the segmentation results obtained from the first stage. Experiments are conducted on synthetic datasets to evaluate the performance of the SDP–RLR algorithm, and a complete TBM time series in terms of three key variables validates its effectiveness for segmentation and outlier detection tasks. The SDP–RLR algorithm can segment a TBM time series into four different statuses, which assists in analyzing the operating statuses of TBMs. In addition, comparative experiments indicate that the proposed algorithm can handle the segmentation of complex TBM time series when they cannot be successfully processed using the segmentation approach after outlier detection. [ABSTRACT FROM AUTHOR]

We introduce a multidimensional multiblock clustering (MDMBC) algorithm in this paper. MDMBC can generate overlapping clusters with similar values along clusters of dimensions. The parsimonious binary vector representation of multidimensional clusters lends itself to the application of efficient meta-heuristic optimization algorithms. In this paper, a hill-climbing (HC) greedy search algorithm has been presented that can be extended by several stochastic and population-based meta-heuristic frameworks. The benefits of the algorithm are demonstrated in a bi-clustering benchmark problem and in the analysis of the Leiden higher education ranking system, which measures the scientific performance of 903 institutions along four dimensions of 20 indicators representing publication output and collaboration in different scientific fields and time periods. • Multiblock clustering algorithm generates overlapping clusters along clusters of dimensions. • Generalized parsimonious binary vector-based representation for higher-dimensions. • The proposed algorithms are demonstrated via an open multidimensional dataset. [ABSTRACT FROM AUTHOR]

• Discrete time Consensus Based Optimization (CBO) algorithm on a convex set is introduced. • The error between optimal value and consensus value is rigorously calculated and further extended from previous paper. • Portfolio optimization problem was solved using our proposed constrained CBO algorithm. In this paper, we propose a predictor-corrector type Consensus Based Optimization(CBO) algorithm on a convex feasible set. Our proposed algorithm generalizes the CBO algorithm in [11] to tackle a constrained optimization problem for the global minima of the non-convex function defined on a convex domain. As a practical application of the proposed algorithm, we study the portfolio optimization problem in finance. In this application, we introduce an objective function to choose the optimal weight on each asset in an asset-bundle, which yields the maximal expected returns given a certain level of risks. Simulation results show that our proposed predictor-corrector type model is successful in finding the optimal value. [ABSTRACT FROM AUTHOR]

The equiareality of NURBS surfaces highly affects their rendering and tessellation results. To improve the equiareality of NURBS surfaces, an optimization algorithm using freeform transformations is presented in this paper. The equiareality energy is first formulated and its numerical approximation is then constructed using the composite Simpson's rule. By constructing the parameterization of the initial freeform transformation using a low-stretch-mesh-parameterization method, the optimal freeform transformation is obtained by the Levenberg–Marquardt method, which is further interleaved with the freeform refinement procedure to generate a hierarchical NURBS surface with bounded equiareality deviations. Experimental results are given to show the performance of our algorithm for rendering and tessellation applications. • An optimization method is presented to improve the equiareality of NURBS surfaces using the freeform transformation. • The equiareality energy of the hierarchical NURBS surfaces is formulated. • The gradient of the discretized equiareality energy is derived in this paper. [ABSTRACT FROM AUTHOR]

• Particle Swarm Optimization to find optimal values for image reconstruction by Phase Retrieval. • Faster convergence and better reconstruction results for 16-bit images. • Image reconstruction process is automated, eliminating the manual search of best values for reconstruction. Imaging through scattering media has become an important aspect in optical imaging. To achieve this, different approaches have been presented to recover the phase of an imaging object from its scattered speckle behind a scattering medium. However, all these algorithms require initial parameters which are obtained based on prior knowledge of the imaging object. This is impractical, as prior knowledge of the imaging object is not always available in real imaging applications. In this paper, we propose using a Particle Swarm Optimization algorithm to find these optimal parameters without requiring prior knowledge of the imaging object. These optimal values are then adopted in the Phase Retrieval algorithm based on object-modulated speckles for object image reconstruction behind a scattering medium in a single-shot imaging system. The obtention of the values for the standard deviation for Gaussian filter and the threshold control allows Phase Retrieval to converge faster and avoids the need for running the algorithm more than once in the search for the best reconstruction. [ABSTRACT FROM AUTHOR]

The tensor low-rank approximation and tensor CANDECOMP/PARAFAC (CP) decomposition are useful in various fields such as machine learning, dimension reduction, tensor completion, data visualization etc. A symmetric tensor is a higher order generalization of a symmetric matrix. Comon et al. (2008) show that every symmetric tensor has a symmetric CP-decomposition. In this paper, we study numerical methods of real-valued symmetric CP-decomposition of symmetric tensors. We present an alternate gradient descent method, an alternate BFGS method and an alternate Levenberg–Marquardt (L–M) method for real-valued symmetric rank-R approximation of symmetric tensors. Moreover, we prove the convergence and effectiveness of the algorithms. Numerical examples show that the alternate gradient descent method costs more computing time than the other two methods and the latter two methods have high success rate and good stability. [ABSTRACT FROM AUTHOR]

In order to solve the uncapacitated facility location problem (UFLP) quickly and effectively, an enhanced group theory-based optimization algorithm (EGTOA) is proposed in this paper. Firstly, a new local search operator, One Direction Mutation Operator, is proposed, which is suitable for solving UFLP. Secondly, a Redundant Checking Strategy is presented to further optimize the quality of feasible solutions. To verify the performance of EGTOA, 15 benchmark instances of UFLP is selected in OR-Library, the comparison results with the 16 existing algorithms show that the solution obtained by EGTOA is better than other algorithms, moreover its speed is much faster than state-of-the-art algorithms. These demonstrates that EGTOA is a fast and effective algorithm for solving UFLP. • The one direction mutation operator is proposed for UFLP. • A redundant checking strategy is proposed to optimize UFLP's solution. • An enhanced group-theory optimization algorithm is proposed for solving UFLP. • Experiment confirms the superiority of the new algorithm for UFLP. [ABSTRACT FROM AUTHOR]

Optimization algorithms have shown significant advantages in solving diverse several real-world problems, especially where are limitations in computations and hardware requirements. However, they might get trapped in local optima or fast convergence. To solve these challenges, the exploration ability of an optimization algorithm should be evaluated and improved. This paper proposes a new approach for solving global optimization and feature selection problems to overcome these drawbacks and balance exploitation and exploration abilities. We propose a hybrid approach combining both, Gradient-Based Optimizer (GBO) using Slime Mould Algorithm (SMA), which exploits the strength factors of Gradient-Based Optimizer and Slime Mould Algorithm. The SMA is applied as a local search for the GBO to enhance its ability to explore more regions in the search space. This strategy is employed to achieve a balance between both exploration and exploitation. Two main experiments are applied to evaluate the performance of the GBOSMA. The first one solves a well-known global optimization problem (CEC2017), whereas the second one solves feature selection problems using several benchmark datasets. The GBOSMA is compared to the standard GBO, SMA, and the most recent optimization algorithms in both experiments. The results also show that the GBOSMA approach obtains promising results in both experiments outperforming other algorithms. Comparing with eight well-known optimization algorithms, the results (for both global optimization and feature selection problems) demonstrate that the GBOSMA overcomes others in terms of performance, speed and stability. • Improving the search ability of the GBO optimizer using the SMA algorithm. • Evaluating the performance of the proposed method using CEC2017 functions. • Selecting the most relative features to increase the classification accuracy. [ABSTRACT FROM AUTHOR]

Solving multi-objective optimization problems plays an important role in several applications. Recently, the Marine Predator Algorithm (MPA) was introduced for solving single objective optimization problems inspired by the behaviour of marine predator in their search for a prey. This paper proposes a modified MPA based framework called Guided Multi-objective Marine Predator Algorithm (GMOMPA) to solve multi-objective optimization problems. The proposed GMOMPA incorporates an external archive to help store the optimal Pareto set solution and guide the particles during the exploitation of the search space. For obtaining non-dominated solutions, the Pareto dominance concept is utilized while the epsilon dominance is considered to update the archive's sorted solutions. In this context, the epsilon dominance concept extends the diversity and exploration of the solutions. Further, a fast non-dominated solution and crowding distance are introduced to update the particle's position, while maintaining the diversity and ensuring a fast convergence towards the Pareto optimal. The proposed GMOMPA is evaluated on several different benchmark test functions including multi-objective ZDT, DTLZ, UF, and WFG test functions as well as the recent multi-objective multimodal CEC 2020 test functions. Moreover, the performance of the proposed GMOMPA is compared with well-known multi-objective optimization algorithms. The obtained results show that the proposed GMOMPA is a good tool for multi-objective optimization and has significant advantages over several state-of-the-art algorithms in almost all of the test functions. [ABSTRACT FROM AUTHOR]

In order to avoid damage during shield tunnel construction to adjacent buildings on the ground, it is of paramount importance to precisely predict the Maximum Surface Settlement (MSS) deformation induced by shield tunneling under the surface. In this paper, a hybrid algorithm model incorporating the Extreme Gradient Boosting (XGBoost) with the Golden-sine Seagull Optimization Algorithm (GSOA) is proposed, namely the GSOA-XGBoost model. A total of 323 datasets including torque, penetration rate, thrust, cutterhead rotation speed, slurry pressure, grouting pressure, cover depth, and distance between cutting face and monitored section are taken as input parameters, and the corresponding measured MSS deformation is adopted as the output parameter. This dataset is employed to train the MSS prediction algorithm model. The analysis results indicate that the proposed GSOA-XGBoost hybrid algorithm model is much superior to other models in terms of accuracy, stability and computation time, and strong correlations are discovered between the predicted and measured settlements. Our model can predict the MSS deformation caused by shield tunnel construction with higher reliability, strong robustness and fault-tolerant capacity, and greatly reduces the engineering risk. Simultaneously, the consistency of the slurry pressure and grouting pressure values is calculated by the SOA-XGBoost and the grid search method, which demonstrates the feasibility and rationality of proposed hybrid algorithm model. [ABSTRACT FROM AUTHOR]

Driver pathways have been acknowledged to play critical roles in the initiation and progression of cancers►hence it is essential for precision medicine related studies to develop accurate and efficient methods to identify them. Although previous approaches have shown promising results by integrating multi-omics data, their preset artificial parameters may decrease the convenience to use and limit the application scalability. In this paper, a novel integration approach is proposed to incorporate four omics data, i.e., construct a weighted non-binary mutation matrix without presetting artificial parameters. A parameter-free identification model is put forward based on it. It takes advantage of the association between genes in the PPI network as well as balances the contribution of coverage and mutual exclusivity with the harmonic mean. Furthermore, a cooperative co-evolution algorithm is proposed for solving this model. In the algorithm, a particle swarm optimization algorithm suitable for solving combinatorial problems is presented. Three cooperative operators are devised to construct the cooperation among the populations and the swarm to increase the population diversity. Both real biological datasets and simulated ones were exerted to perform experimental comparisons among the proposed method and six other state-of-the-art ones. The gene sets identified by the presented method generally contain more genes involved in known signaling pathways than those obtained by the other methods. Simultaneously, both high accuracy and high efficiency of the proposed method were verified in experiments, making it practical in realistic applications and an effective supplementary tool to identify driver pathways. • A non-binary mutation matrix is constructed without artificial adjustable parameters. • The model incorporates gene association and balances coverage and mutual exclusivity. • A PSO algorithm suitable for solving combinatorial problems is presented. • A co-operative co-evolution algorithm is put forward for solving the presented model. • The performance is compared among the proposed method and seven state of the art ones. [ABSTRACT FROM AUTHOR]

In this paper we have first reviewed operations of a hydrogen gas reformer and provided its linearized mathematical model. Then, we have simplified an existing algorithm for a two-stage design of feedback controllers for linear continuous-time time-invariant systems. The proposed design significantly reduces the computational requirements and provides flexibility of designing different type of controllers for different dynamic parts of the system. Since the hydrogen gas reformer (also known as a fuel processing system) possesses slow and fast modes (state variables), the newly proposed design is further simplified and specialized for this class of systems. The obtained algorithm is efficiently applied with very high accuracy to the hydrogen gas reformer. As a matter of fact, the eigenvalue placement problem is solved for the reformer dynamics for both slow and fast modes. The design is so flexible that combined hybrid controllers (optimal, robust, set-point, eigenvalue assignment controllers or any other linear controller) can be designed independently for particular subsystems of the hydrogen gas reformer. The hybrid linear feedback controller design for the hydrogen gas reformer that optimizes its slow subsystem and assigns the desired eigenvalues to its fast subsystem is also presented in the paper. [ABSTRACT FROM AUTHOR]

Abstract: Once all available measurements are determined, the highest testability index of a complex system is determined. To achieve such highest index with the lowest test cost, AND/OR graph search algorithms were developed for years to determine an optimal or near-optimal test sequence. However, in most cases, achieving the highest testability index induces extremely high test cost. The purpose of this paper is to optimize test set and test sequence so as to cut down the test cost while keeping the required, not necessarily the highest, FIR (Fault Isolation Rate) satisfied. Traditionally, this is an NP-Complete problem, which makes the computation of optimal test set impractical for even the moderate-sized model. In this paper, a greedy method is proposed to get the optimized test set. Then, we combine the greedy method with discrete binary particle swarm optimization (DPSO) to construct a test sequential tree. With the specified FIR requirement satisfied, the lowest test cost is achieved. The proposed algorithms are illustrated and tested in a range of real-world systems. The effectiveness and accurateness of the proposed algorithm is verified by computational results. [Copyright &y& Elsevier]

Abstract: Using the semi-tensor product method, this paper investigates the algebraic formulation and strategy optimization for a class of evolutionary networked games with “myopic best response adjustment” rule, and presents a number of new results. First, the dynamics of the evolutionary networked game is converted to an algebraic form via the semi-tensor product, and an algorithm is established to construct the algebraic formulation for the game. Second, based on the algebraic form, the dynamical behavior of evolutionary networked games is discussed, and some interesting results are presented. Finally, the strategy optimization problem is considered by adding a pseudo-player to the game, and a free-type control sequence is designed to maximize the average payoff of the pseudo-player. The study of an illustrative example shows that the new results obtained in this paper work very well. [Copyright &y& Elsevier]

The Chimp Optimization Algorithm (ChOA) is a hunting-based model and can be utilized as a set of optimization rules to tackle optimization problems. Due to agents' insufficient diversity in some complex problems, this algorithm is sometimes exposed to local optima stagnation. This paper introduces a Dynamic Lévy Flight (DLF) technique to smoothly and gradually transit the search agents from the exploration phase to the exploitation phase. To investigate the efficiency of the DLFChOA, this paper evaluates the performance of DLFChOA on twenty-three standard benchmark functions, twenty challenging functions of CEC-2005, ten suit tests of IEEE CEC06-2019, and twelve real-world optimization problems. The results are compared to benchmark optimization algorithms, including CMA-ES, SHADE, ChOA, HGSO, LGWO and ALEP (as the best benchmark Lévy-based algorithms), and eighteen state-of-the-art algorithms (as the winners of the CEC2019, the GECCO2019, and the SEMCCO2019). Among forty-three numerical test functions, DLFChOA and CMA-ES gain the first and second rank with thirty and eleven best results. In the 100-digit challenge, jDE100 with a score of 100 provides the best results, followed by DISHchain1e+12, and DLFChOA with a score of 85.68 is ranked fifth among eighteen state-of-the-art algorithms achieved the best score in seven out of ten problems. Finally, DLFChOA and CMA-ES respectively gain the best results in five and four real-world engineering problems. [ABSTRACT FROM AUTHOR]

• A hierarchical coordination reinforcement learning method is developed. • A framework is proposed to optimise the maintenance of complex systems via the HCRL. • The HCRL outperforms benchmark maintenance optimisation methods. • An efficient method is developed to simulate interdependent degradation processes. The Markov decision process (MDP) is a widely used method to optimise the maintenance of multicomponent systems, which can provide a system-level maintenance action at each decision point to address various dependences among components. However, MDP suffers from the "curse of dimensionality" and can only process small-scale systems. This paper develops a hierarchical coordinated reinforcement learning (HCRL) algorithm to optimise the maintenance of large-scale multicomponent systems. Both parameters of agents and the coordination relationship among agents are designed based on system characteristics. Furthermore, the hierarchical structure of agents is established according to the structural importance measures of components. The effectiveness of the proposed HCRL algorithm is validated using two maintenance optimisation problems, one on a natural gas plant system and the other using a 12-component series system under dependant competing risks. Results show that the proposed HCRL outperforms methods in two recently published papers and other benchmark approaches including the new emerging deep reinforcement learning. [ABSTRACT FROM AUTHOR]

With the availability of multi-material additive manufacturing, topology optimization of a multi-material structure and the selection of candidate materials become increasingly important. This paper aims to develop a new multi-material topology optimization algorithm and propose the guidelines for the selection of candidate materials from the database. The multi-material design variables and their inequality relationship are built on volume fractions of multiple materials within each element. The multi-material design variables are relaxed and multiple floating projection constraints simulate their discrete constraints by pushing design variables towards 0 or 1. Meanwhile, their inequality relationship is enforced by their variation limits. The proposed multi-material topology optimization algorithm can be applied to the compliance minimization problem constrained by a single mass or multiple volumes, as demonstrated in numerical examples. This paper mainly focuses on a single mass constraint, and 2D and 3D numerical examples systematically demonstrate that an optimized design under a mass constraint achieves a lower compliance when more materials appear in the final design simultaneously. Furthermore, we establish an approach to predict the inclusion or exclusion of a material from the final design, and propose the conditions for the co-existence of candidate materials, which guide users to select candidate materials from the database. • A new multi-material topology optimization algorithm is proposed. • The algorithm can be directly applied to a single mass constraint or multiple volume constraints. • The results systematically demonstrate the advantage of the multi-material design. • The approach to predict the occurrence of candidate materials in the final design is developed. • The approach can guide the users to select materials for multi-material additive manufacturing. [ABSTRACT FROM AUTHOR]

• This paper proposes a decentralized ADAM-type algorithm for solving minmax optimization problem under the condition that the objective function satisfies Minty VI which is a generalization to convex-concave case. The proposed method is unlike the recent non-adaptive gradient-based decentralized min-maxstudies that fail drastically in practice and requires large efforts for tuning. In this paper, we first obtain non-asymptotic rates of convergence of our method for finding (stochastic) first-order Nash equilibrium point and then evaluate its performance by using it for training GANs in numerous dataset. In the revised version of the paper, we have pointed out relaxation of the Minty condition in the discussion of the paper. Min-max saddle point games have recently been intensely studied, due to their wide range of applications, including training Generative Adversarial Networks (GANs). However, most of the recent efforts for solving them are limited to special regimes such as convex-concave games. Further, it is customarily assumed that the underlying optimization problem is solved either by a single machine or in the case of multiple machines connected in centralized fashion, wherein each one communicates with a central node. The latter approach becomes challenging, when the underlying communications network has low bandwidth. In addition, privacy considerations may dictate that certain nodes can communicate with a subset of other nodes. Hence, it is of interest to develop methods that solve min-max games in a decentralized manner. To that end, we develop a decentralized adaptive momentum (ADAM)-type algorithm for solving min-max optimization problem under the condition that the objective function satisfies a Minty Variational Inequality condition, which is a generalization to convex-concave case. The proposed method overcomes shortcomings of recent non-adaptive gradient-based decentralized algorithms for min-max optimization problems, that do not perform well in practice and require careful tuning. In this paper, we obtain non-asymptotic rates of convergence of the proposed algorithm (coined Dadam 3) for finding a (stochastic) first-order Nash equilibrium point and subsequently evaluate its performance on training GANs. The extensive empirical evaluation shows that Dadam 3 outperforms recently developed methods, including decentralized optimistic stochastic gradient for solving such min-max problems. [ABSTRACT FROM AUTHOR]

• The proposed method improves the production efficiency of the machine. • The reliability and superiority of the proposed method are verified by simulation. • The proposed method in this paper is able to achieve the optimal solution. In an automated stereoscopic warehouse, the efficiency of the Rail Guided Vehicle (RGV) is the bottleneck. This paper proposes a foresight stepping model to optimize the intelligent RGV scheduling scheme. We incorporate the chaotic particle swarm optimization algorithm into the model and design the mechanism of multi-step processing. The machine optimization is used to compare the optimal alignment effect of the Back Propagation (BP) network algorithm and GradientBoostingDecisionTree (GBDT) algorithm. The real-life system test is performed by simulation. The simulation results show that the GBDT-foresight stepping model is superior to the traditional models in terms of complexity, reliability and accuracy. Image, graphical abstract The problem addressed in this paper is the work optimization problem in a workshop. [ABSTRACT FROM AUTHOR]

Abstract: The control algorithm hierarchy of motion control for over-actuated mechanical systems with a redundant set of effectors and actuators commonly includes three levels. First, a high-level motion control algorithm commands a vector of virtual control efforts (i.e. forces and moments) in order to meet the overall motion control objectives. Second, a control allocation algorithm coordinates the different effectors such that they together produce the desired virtual control efforts, if possible. Third, low-level control algorithms may be used to control each individual effector via its actuators. Control allocation offers the advantage of a modular design where the high-level motion control algorithm can be designed without detailed knowledge about the effectors and actuators. Important issues such as input saturation and rate constraints, actuator and effector fault tolerance, and meeting secondary objectives such as power efficiency and tear-and-wear minimization are handled within the control allocation algorithm. The objective of the present paper is to survey control allocation algorithms, motivated by the rapidly growing range of applications that have expanded from the aerospace and maritime industries, where control allocation has its roots, to automotive, mechatronics, and other industries. The survey classifies the different algorithms according to two main classes based on the use of linear or nonlinear models, respectively. The presence of physical constraints (e.g input saturation and rate constraints), operational constraints and secondary objectives makes optimization-based design a powerful approach. The simplest formulations allow explicit solutions to be computed using numerical linear algebra in combination with some logic and engineering solutions, while the more challenging formulations with nonlinear models or complex constraints and objectives call for iterative numerical optimization procedures. Experiences using the different methods in aerospace, maritime, automotive and other application areas are discussed. The paper ends with some perspectives on new applications and theoretical challenges. [Copyright &y& Elsevier]

Abstract: Network management is increasingly being customised for green objectives due to roll out of mission-critical applications across the internet of things and execution, in a number of cases, on battery-constrained devices. In addition, the volume of operations across the internet of things is attracting climate change concerns. While operational efficiency of wireless devices and in data centres (which support operation of the internet of things) should not be achieved at the expense of Quality of Service, optimisation opportunities should be exploited and inefficient resource use minimised. Green networking approaches however, are not yet standardised, and there is scope for novel middleware architectures. In this paper, we explore operational efficiency from the perspective of activities in data centres which support the internet of things. This includes evaluation of the effectiveness of mechanisms integrated into the e-CAB framework, an algorithm proposed by the authors to manage next generation data centres with green objectives. A selection of its policy mechanisms have been implemented in the NS-2 Network Simulator to evaluate performance; configuration decisions are described in this paper and presented alongside experimental results which demonstrate optimisations achieved. Focus lies, in particular, on rate adaptation of its context discovery protocol which is responsible for capturing real-time network state. Performance results reveal a small overhead when applying network management and validate improved efficiency through adaption in response to environment dynamics. [Copyright &y& Elsevier]

Abstract: Modelling of microburst is significant for flight simulations. The most widely used model of microburst is the one based on the principle of multiple vortex ring. The characteristics of the microburst are directly determined by the parameters of the model. The conventional method to fix the parameters of multiple vortex ring microburst model is using experiential formula, which is not flexible enough. In this paper, we treat the parameters estimation of multiple vortex ring model as an optimization problem, and introduce the differential evolution algorithm to solve it. In the process of estimating the parameters by the maximum wind velocity, there are two interrelated optimization processes. Therefore, we improved the standard differential evolution algorithm in this paper. A nested differential evolution algorithm is proposed to accomplish the two optimization process, intermediate optimization and objective optimization. The numerical experiments show that this method can flexibly generate microburst with any maximum wind velocity, and the error meet the requirement set by the user. [Copyright &y& Elsevier]

Abstract: This paper draws on diverse areas of computer science to develop a unified view of computation: [•] Optimization in operations research, where a numerical objective function is maximized under constraints, is generalized from the numerical total order to a non-numerical partial order that can be interpreted in terms of information. [•] Relations are generalized so that there are relations of which the constituent tuples have numerical indexes, whereas in other relations these indexes are variables. The distinction is essential in our definition of constraint-satisfaction problems. [•] Constraint-satisfaction problems are formulated in terms of semantics of conjunctions of atomic formulas of predicate logic. [•] Approximation structures, which are available for several important domains, are applied to solutions of constraint-satisfaction problems. As application we treat constraint-satisfaction problems over reals. These cover a large part of numerical analysis, most significantly nonlinear equations and inequalities. The chaotic algorithm analyzed in the paper combines the efficiency of floating-point computation with the correctness guarantees of arising from our logico-mathematical model of constraint-satisfaction problems. [Copyright &y& Elsevier]

Abstract: This paper presents a maintenance optimisation method for a multi-state series–parallel system considering economic dependence and state-dependent inspection intervals. The objective function considered in the paper is the average revenue per unit time calculated based on the semi-regenerative theory and the universal generating function (UGF). A new algorithm using the stochastic ordering is also developed in this paper to reduce the search space of maintenance strategies and to enhance the efficiency of optimisation algorithms. A numerical simulation is presented in the study to evaluate the efficiency of the proposed maintenance strategy and optimisation algorithms. The simulation result reveals that maintenance strategies with opportunistic maintenance and state-dependent inspection intervals are more cost-effective when the influence of economic dependence and inspection cost is significant. The study further demonstrates that the optimisation algorithm proposed in this paper has higher computational efficiency than the commonly employed heuristic algorithms. [Copyright &y& Elsevier]

Abstract: This paper presents a new method to solve the dynamic sub-transmission substation expansion planning (SSEP) in electric power distribution networks. The method employs a new algorithm based on learning automata for optimization process. The developed algorithm includes different electrical constraints such as voltage drops, thermal limits, power flow and radial flow constraints. Moreover, prevalent cost indices are taken into consideration. The proposed method is used to solve the dynamic SSEP for Birjand city, center of South-Khorasan province of Iran, up to the year 2024. In addition, effects of the dynamic and pseudo-dynamic planning models are investigated at the study region and the obtained results are compared to those of genetic algorithm. Detailed numerical results and comparisons presented in the paper show that the proposed approach leads to proper solutions and can be used as an effective tool for the dynamic sub-transmission substation expansion planning in an actual large scale distribution network. [Copyright &y& Elsevier]

Abstract: Wind farm layout design is a complex optimization problem consisting of number of design objectives and constraints. Different variations of this problem have been solved using several optimization techniques. Iterative heuristics are well-known optimization techniques that have been applied to a variety of complex optimization problems. This paper briefly outlines the design issues and constraints involved in the wind farm layout design, computational complexity of the problem, and single-objective and multi-objective aspects of the problem. The main focus of the paper is a brief survey of all iterative non-deterministic algorithms that have been applied to solve the wind farm layout design problem. [Copyright &y& Elsevier]

Abstract: This paper presents an innovative Tribe-Modified Differential Evolution (Tribe-MDE) for solving multi-objective Environmental/Economic Dispatch (EED) problems. By using this method the multi-objective problem will be changed into a mini-max problem on this first stage and then will be solved using the Tirbe-MDE algorithm. The operator, a person who is competent with respect to the problem, can initiate all the necessary actions to determine an optimal solution. Because of the fact that all of Pareto obtained solutions are of the same level of preference an operator will be able to affect his/her opinions with respect to different conflicting objectives. Using this methodology the operator can achieve the optimal solution. The DE algorithm is an advanced stochastic method that can prepare the necessary preliminaries for an operator to solve the EED problem. DE method has several advantages including its few control variables, local searching capability, fast results, easy using process and simple structure and using the control variables logically has a significant effect on the results. The subject of this paper is studying the modified DE algorithm which is founded on self-adaptive control. In this method a diversity-preserving method is used to solve the premature convergence problem and for proving the effectiveness of this method in solving EED problems the presented algorithms is applied on different systems. [Copyright &y& Elsevier]

Abstract: The paper presents advances in the processing technique based on transform domain methods for performance enhancement of extrinsic Fabry–Perot interferometric (EFPI) sensors. The paper introduces a new technique to estimate the cavity length from the instantaneous phase. Pre-processing of the EFPI spectrum signal is proposed using wavelet transform to filter out the low frequency background and broadband measurement noise. The filtering technique efficiently extracts the required interference spectrum in a low cavity length EFPI. The computation of the analytic representation of the spectrum signal has been carried out using Hilbert transform. The variation computed using the proposed algorithm in a continuous test with an EFPI of cavity length 66μm is found to be as low as ±1.13nm. The range of wavelength used for processing the EFPI signal for accurate estimation of cavity length also needs to be optimized. In this regard, a definite relation between the wavelength range and cavity length measurement stability have been found. Optimization improves sensor response time and multiplexing ability. [Copyright &y& Elsevier]

Abstract: A simulation model of a two mode power-split hybrid electric vehicle (HEV) is proposed in this paper for the purpose of HEV dynamics analysis and control system design. The bond graph methodology is used to model dominant dynamic effects of the mechanical part of the HEV transmission. Simple quasi-static battery model, the environment model, the tire and the power losses model of a vehicle are included, as well. A low-level electric generator speed control loop is designed, which includes a PI controller tuned according to the symmetrical optimum tuning procedure. Finally, off-line optimization by conjugate gradient-based BPTT-like optimal control algorithm, which is based on the presented mathematical model, is also given in the paper. [Copyright &y& Elsevier]

Abstract: This paper proposes a new algorithm to determine the best connection of multiple distributed generations (DGs) on distribution networks according to type of customers. The considered DG technology is combined heat and power (CHP) in various sizes which can be extended to other technologies. Different types of demand, such as industrial, residential and commercial are defined by the load model that is voltage dependent and the load patterns from real life hourly demand data. This method which is based on multi-year multi-period power flow is useful for Distribution Network Operators (DNOs) when they are faced with voltage dependent load models in their networks. However it can be used in constant load models with multi-period or single demand. The proposed optimization is from the DNO’s perspective and aims to maximize their present value of profit against the supplying costs of the network. It is shown that by definition of an appreciated economic based objective function, the customer type can significantly affect the optimal DG options in distribution network planning. Finally, this method is tested using the 69-bus radial system. Furthermore, a comparative study for different customer demand types is shown for various cases and results are discussed in this paper. [Copyright &y& Elsevier]

In this paper, Coplanar Waveguide (CPW) elements are used to load UHF RFID meander-line antennas. Although, the insertion of these elements allow to design small antennas their gain decrease depending on the CPW structures are connected. In this sense, a new location to print CPW elements in the meander-line antenna is proposed aiming not to deteriorate the gain. A multi-objective optimization algorithm is applied in the meander-line antennas loaded with CPW elements in different positions and a comparison between their performances is done. To validate the computational model of the proposed antenna its input impedance is measured. [ABSTRACT FROM AUTHOR]

When mining in high dimensional data, the curse of dimensionality is one of the major difficulty to overcome. In this paper, a weighted feature selection strategy is developed and embedded in bacterial based algorithms to reduce the feature dimension in classification. The proposed weighted feature selection strategy distinguishes the features by their classification performances as well as the occurrence frequency in population according to the two matrices. The objectives of minimizing the number of features, maximizing the performance, and minimizing the computational cost are all considered. Regarding the drawback of bacterial based algorithms, Bacterial Colony Optimization based feature selection algorithm is proposed to decrease the computational complexity as well as improve the search ability even in discrete optimization problems. To test the effectiveness of the proposed feature selection method, four bacterial based methods with the weighted strategy embedded have been compared with four classical feature selection methods and three well-known population based algorithms using 15 cancer micro-array datasets with different numbers of features and classes. The results show that the weighted feature selection strategies embedded have improved the feature selection capability of bacterial algorithms. The new proposed mechanisms embedded in Bacterial Colony Optimization method can overcome the limitation of the traditional bacterial based algorithms using premature termination to decrease the computational time, and provide comparable or in most cases better solutions than other feature selection methods considered in the comparison. [ABSTRACT FROM AUTHOR]

Floorplanning is a crucial step in very large scale integration design flow. It provides valuable insights into the hardware decisions and estimates a floorplan with different cost metrics. In this paper, to handle a multi-objective thermal-aware non-slicing floorplanning optimization problem efficiently, an adaptive hybrid memetic algorithm is presented to optimize the area, the total wirelength, the maximum temperature and the average temperature of a chip. In the proposed algorithm, a genetic search algorithm is used as a global search method to explore the search space as much as possible, and a modified simulated annealing search algorithm is used as a local search method to exploit information in the search region. The global exploration and local exploitation are balanced by a death probability strategy. In this strategy, according to the natural mechanisms, each individual in the population is endowed with an actual age and a dynamic survival age. Experimental results on the standard tested benchmarks show that the proposed algorithm is efficient to obtain floorplans, with decreasing the average and the peak temperature. [ABSTRACT FROM AUTHOR]