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Min–max problems have broad applications in machine learning, including learning with non-decomposable loss and learning with robustness to data distribution. Convex–concave min–max problem is an active topic of research with efficient algorithms and sound theoretical foundations developed. However, it remains a challenge to design provably efficient algorithms for non-convex min–max problems with or without smoothness. In this paper, we study a family of non-convex min–max problems, whose objective function is weakly convex in the variables of minimization and is concave in the variables of maximization. We propose a proximally guided stochastic subgradient method and a proximally guided stochastic variance-reduced method for the non-smooth and smooth instances, respectively, in this family of problems. We analyse the time complexities of the proposed methods for finding a nearly stationary point of the outer minimization problem corresponding to the min–max problem. [ABSTRACT FROM AUTHOR]

Over the last two decades, the PageRank problem has received increased interest from the academic community as an efficient tool to estimate web-page importance in information retrieval. Despite numerous developments, the design of efficient optimization algorithms for the PageRank problem is still a challenge. This paper proposes three new algorithms with a linear time complexity for solving the problem over a bounded-degree graph. The idea behind them is to set up the PageRank as a convex minimization problem over a unit simplex, and then solve it using iterative methods with small iteration complexity. Our theoretical results are supported by an extensive empirical justification using real-world and simulated data. [ABSTRACT FROM AUTHOR]

In this article, a class of lower-order smoothed objective penalty functions is introduced to find locally optimal points for constrained optimization problems. The exactness of the new penalty functions is studied. Based on the current locally optimal points, a new class of penalty functions based on filling properties is proposed. This new penalty function can be used to find a better locally optimal point. The exactness and filling properties of this penalty function are proved in this paper. To do this, two algorithms are presented to find the locally and globally optimal points. Additionally, their convergence is proved under some mild conditions. Finally, numerical results are included to illustrate the applicability of the local and global optimization algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a class of linear multiplicative problems (LMP) are considered, which cover many applications and are known to be NP-hard. For finding the globally optimal solution to problem (LMP) with a pre-specified ε-tolerance, problem (LMP) is first transformed into an equivalent problem (EP) via introducing the variable transformation. And, a novel linear relaxation technique is presented by exploiting the special structure of problem (EP), for deriving the linear relaxation programming which can be used to acquire the upper bound of the optimal value to problem (EP). A branch and bound algorithm is then located for globally solving problem (LMP). The convergence of the algorithm is established and its computational complexity is estimated. Finally, numerical results are reported to illustrate the feasibility and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes the use of a nature-inspired algorithm known as Grasshopper Optimisation Algorithm for optimising switching angles applied to a three-phase 7-level cascaded H-bridge multilevel inverter. Switching angles obtained using the algorithm can be used to synthesise an optimised output voltage waveform with desired fundamental component and minimised 5th and 7th order harmonics. Simulation results show that the algorithm is able to provide switching angles over a wider modulation index range than Newton Raphson technique and is capable of minimising the objective function better than Genetic Algorithm. MATLAB/Simulink-PSIM dynamic co-simulation performed on a three-phase 7-level cascaded H-bridge multilevel inverter operated using GOA derived switching angles is able to respond to varying modulation index demand and produce the output voltage waveform with the desired fundamental component and minimised selected 5th and 7th order harmonics. Both simulation and experimental results obtained from the three-phase 7-level cascaded H-bridge multilevel inverter show good agreement, confirming the validity of the switching angles obtained using the Grasshopper Optimisation Algorithm. [ABSTRACT FROM AUTHOR]

Due to the high construction costs of a water distribution network, the least cost design with the most reliability is of great importance. In this paper, a survey was conducted on the performance of different reliability metrics including nodal pressure (PIP), pipe velocity (PIV), pressure difference (ΔH), combined index of pipe velocity and nodal pressure (PI) as well as the network resilience index (NRI), minimum surplus head (MSH) and entropy reliability index (ERI) using NSGA-II optimization algorithm and EPANET hydraulic solver. Reliability metrics were tested on three benchmark water distribution networks: Hanoi, Pescara and Modena and the results were analyzed in three scenarios of 5%, 10%, and 20% pipe failure. Experimental results showed that NRI generally outperforms other metrics and the PIP index is in the second score. These metrics increase the average of network pressure and thus provide higher reliability in the situation of unexpected events like pipe breaks. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid (MFO-SCA) method based on Moth Flame Optimization (MFO) algorithm and Sine Cosine Algorithm (SCA). MFO is utilized for the exploitation phase while SCA is used for the exploration phase. In addition, the hybrid MFO-SCA method is used with combined power loss sensitivity (CPLS) to determine the optimal allocation of single and multiple of distributed generations (DGs) and capacitors in distribution system. In this paper, the total active power loss is used as a single objective function for incorporating DGs and capacitors in RDS. Also, this paper studies the system performance when integrating DGs or capacitors alone and DGs with capacitors simultaneously in RDS. The proposed method is validated using the standard IEEE 69-bus and IEEE 33-bus radial distribution system (RDS). From comparative study, the novel hybrid MFO-SCA proved its effectiveness in obtaining the best global optimum results of the objective function compared with other recent optimization techniques, especially SCA and MFO algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a bi-objective model for integrated production and distribution planning in a multi-period multi-product green supply chain. This paper considers many near-real-world factors, such as: normal and overtime production shifts, limited storage capacity of the finished product and raw materials, different vehicle types, direct and indirect shipping along with considering different production methods that influence the production costs and sale prices, simultaneously. Mixed integer programming model is proposed in which the profit maximization and the CO2 emission minimization are formulated. A particle swarm optimization algorithm based on Pareto archive that utilizes the genetic algorithm operators is proposed to solve the model. [ABSTRACT FROM AUTHOR]

In this paper, an automated leukocyte identification and classification methodology using microscopic blood smear images is proposed. After performing a colour based clustering for the identification of leukocytes, a large set of features are derived from the identified leukocytes. Thereafter a weighted aggregation-based transposition particle swarm optimisation technique (WATPSO) is proposed to select an optimised feature subset which is sufficient enough for classifying carcinogenic blast cells and normal cells efficiently. The proposed method is applied to a benchmark acute lymphoblastic leukaemia (ALL) data set and 97.30% test accuracy is achieved using only 6 optimal features. Experimental results demonstrate the usefulness of the proposed methodology in terms of high test accuracy and less number of informative selected features. The proposed method can be used as a decision support structure for haematologists for early diagnostic prediction of ALL. [ABSTRACT FROM AUTHOR]

Substrate Integrated Waveguide (SIW) technology has recently drawn attention to its microwave benefits, such as integration into planar microwave circuits, low manufacturing cost and high-quality factor compared to other voluminous technologies. In this paper, a cavity-backed slot antenna structure has been designed and optimised by the Particle Swarm Optimization (PSO) algorithm and manufactured for X-band applications. Indeed, the starting cavity-backed slot antenna has three pairs of resonators placed on the lower wall of the cavity, and the PSO algorithm seeks the optimal antenna by automating HFSS using MATLAB script. Two antennas have been chosen from the last PSO generation. The selected antennas operate respectively on the bands [9.17 GHz. 10.63 GHz] and [9.09 GHz, 10.6 GHz] with a bandwidth of 15%, and can achieve a maximum gain of 8.8 dBi. The total size of the proposed design is 0.46 λ 0 × 1.95 λ 0 . The gain, reflection coefficient and radiation partners are measured and compared to the EM simulation results and a very good agreement is obtained. [ABSTRACT FROM AUTHOR]

This paper aims to develop an integrated adaptive control strategy to control vehicle lateral dynamics. A hierarchical control scheme consisted of two control levels is designed to keep the vehicle handling and improve yaw stability control using the coordination of active front steering (AFS) and direct yaw moment control (DYC). At the upper control level, the desired yaw moment and additive steering angle are obtained through proper adaptive laws in the first level adaptation (FLA) control structure. A constraint optimisation algorithm is utilised in the lower control level to adjust the impact of the steering and braking actuators. The desired yaw moment is converted into the brake torques and effectively distributed to the wheels. Improvement in the yaw stability control is demonstrated through simulation studies for a nonlinear 8 degree-of-freedom (DOF) vehicle model. Furthermore, the performance of the proposed control strategy is compared with that of the adaptive controller without integration. Simulation results of two different maneuvers point to the superiority of the proposed approach, highlighting some of its appealing features, like better tracking performance with lower control efforts in terms of brake torque and steering angle, as compared to the conventional adaptive method. [ABSTRACT FROM AUTHOR]

Copy move forgery (CMF) of digital images is the frequently adapted tampering by simply copying a region of the image and pasting it on to the same image using user-friendly image processing tools. Most of the existing key-point (KP) based CMF detection algorithms involve a hefty number of KPs and huge sets of feature descriptors, which increase their computational burden. In addition, the identified KPs may not spread over all regions of the image and the classical clustering techniques may not optimally classify the feature space into cluster space, thereby affecting the accuracy of the results. This paper suggests a new CMF detection method that considers a small number of the strongest KPs, chosen from both Difference of Gaussian (DoG)-based KPs and FAST-corner KPs, evaluates Scale Invariant Features Transform (SIFT) descriptors, applies discrete wavelet transform for dimensionality reduction, and employs football game based optimization (FGBO). The FGBO belongs to the family of meta-heuristic optimization algorithms, and is used for effectively classifying the feature space into cluster space, with a view to overcoming the drawbacks of classical methods. This article exhibits the superior performances of the developed method over existing methods by presenting results on 500 digital images. [ABSTRACT FROM AUTHOR]

This paper presents the CFD numerical investigation of novel drag-driven wind turbine blade inspired by spiral optimisation algorithm (SPO) and cycloid curve. In this study, six hybrid spiral geometries were analysed via CFD simulation using FLUENT based on URANS and SST numerical model. The simulated results of six shapes were compared against simulated conventional Savonius wind turbine. In terms of moment coefficient, all the turbines were simulated under similar computational configuration. The turbines were studied at two moderate tip speed ratios, which are λ = 0.59 and λ = 0.94, under constant freestream velocity of 8 m/s. The result shows that design shape S-4 displayed higher moment coefficient than Savonius wind turbine with an improvement of 7.2% in moment coefficient at λ = 0.59. However, at λ = 0.94, the percentage of improvement in moment coefficient is only 4%. It is observed that reduction in blade height and modification of blade curve configuration improve the moment of the rotor. The sharp edge presented by the hybrid spiral shape induces higher pressure gradient than Savonius wind turbine on the convex side of the blade. [ABSTRACT FROM AUTHOR]

Bio-inspired optimisation algorithms have recently attracted much attention in the control community. Most of these algorithms mimic particular behaviours of some animal species in such a way that allows solving optimisation problems. The present paper aims at applying three metaheuristic methods for optimising fuzzy logic controllers used for quadrotor attitude stabilisation. The investigated methods are particle swarm optimisation (PSO), BAT algorithm and cuckoo search (CS). These methods are applied to find the best output distribution of singleton membership functions of the fuzzy controllers. The quadrotor control requires measured responses, therefore, three objective functions are considered: integral squared error, integral time-weighted absolute error and integral time-squared error. These metrics allow performance comparison of the controllers in terms of tracking errors and speed of convergence. The simulation results indicate that BAT algorithm demonstrated higher performance than both PSO and CS. Furthermore, BAT algorithm is capable of offering 50% less computation time than CS and 10% less time than PSO. In terms of fitness, BAT algorithm achieved an average of 5% better fitness than PSO and 15% better than CS. According to these results, the BAT-based fuzzy controller exhibits superior performance compared with other algorithms to stabilise the quadrotor. [ABSTRACT FROM AUTHOR]

Active chilled beams (ACBs), as indoor air terminal units in heating, ventilation, and air conditioning (HVAC) systems, have been of great concern in recent years due to the requirements of energy savings and indoor thermal comfort. This paper reviews the structure and application of ACB, which is helpful in identifying current research scenarios and prospects. The results indicate that the structure of ACBs will be improved based on the entrainment ratio (ER), cooling performance and acoustic behavior as the manufacturing level evolves. The mathematical model of ACBs is constantly being optimized, although there is still room for simplification to fit practical applications. Research on airflow patterns and thermal comfort can be extended to larger spaces (including buildings with large sensible heat ratios) and various climates. Based on the optimization of the ACB mathematical model, modern control theory can be focused on the research on ACB control system. ACB system has a coupling trend with other systems to achieve the comprehensive utilization of energy due to the characteristics of high-temperature cooling. In addition, numerous technical issues remain to be resolved to reduce unnecessary energy consumption. These conclusions also provide a simple reference for research and design in the area where this technology is still in its infancy. [ABSTRACT FROM AUTHOR]

Structural equation modeling (SEM) is being applied to ever more complex data types and questions, often requiring extensions such as regularization or novel fitting functions. To extend SEM, researchers currently need to completely reformulate SEM and its optimization algorithm – a challenging and time–consuming task. In this paper, we introduce the computation graph for SEM, and show that this approach can extend SEM without the need for bespoke software development. We show that both existing and novel SEM improvements follow naturally. To demonstrate, we introduce three SEM extensions: least absolute deviation estimation, Bayesian LASSO optimization, and sparse high–dimensional mediation analysis. We provide an implementation of SEM in PyTorch – popular software in the machine learning community – to accelerate development of structural equation models adequate for modern–day data and research questions. [ABSTRACT FROM AUTHOR]

In multi-objective bilevel optimisation problems, the upper-level performance of different lower-level optimal solutions may be very different, even though they belong to the same lower-level problem. It may lead to poor optimisation results. Therefore, the lower-level search should search lower-level non-dominated solutions that are also non-dominated in the upper-level objective space. In this paper, we use two populations in the lower-level search. The first population maintains non-dominance and diversity in the lower-level objective space and provides the second population with convergence pressure from the lower level. The second population selects the upper-level non-dominated solutions that are not dominated by the first population in the lower-level objective space, which make the second population maintain the non-dominance at both upper and lower levels. Besides, to improve the search efficiency, we set up the upper-level mating pool to generate the upper-level vectors of offsprings near the upper-level vectors of the better individuals in the current population. To balance convergence and diversity, the selection operator of a decomposition based multi-objective evolutionary algorithm is adopted. The proposed algorithm has been evaluated on a set of benchmark problems and a real-world optimisation problem. Experimental results demonstrate that the proposed algorithm is efficient and effective. [ABSTRACT FROM AUTHOR]

Image event recognition is different from object recognition, behaviour recognition and scene recognition. Event is a more advanced concept than object, behaviour and scene. Regarding semantics loss in image event recognition, this paper first proposes a WordNet-based optimization algorithm for concept semantics similarity and describes the semantics relations between different concepts by taking account of such following four impact factors in the WordNet tree as concept semantics distance, concept node depth, concept node density and concept semantics overlap ratio. On that basis, an image event recognition algorithm (CS-IER) based on concept score is proposed, while multi-view learning is applied to fuse concept score and inter-conceptual semantics relations. However, if a higher erroneous concept score is given using CNN, multi-view learning will also augment the concept score approximate to its erroneous concept semantics, thereby leading to the distortion of image representation information. To address this problem, CNN is used to extract channel information to obtain the local features of the image, and it is further fused with the optimized concept score features, so as to form the final image representation information and complete the image event recognition. In experiments, the effectiveness of the proposed algorithm on three datasets is verified. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are capable of achieving data dissemination between them such that exploration of their potential could be performed based on their frequency range. It is considered to be highly difficult for recharging sensor devices under adverse situations. The main drawbacks of WSNs concern to the issue of network lifetime, coverage area, scheduling and data aggregation. In particular, prolonging network lifetime confirms the success together with the energy conservation of sensor nodes, data transmission reliability and scalability of their operation in data aggregation. Clustering schemes are considered to be highly suitable for effectively utilising the resources with lower overhead, such that energy consumption is enhanced for upgrading the network lifespan. In this paper, a Hybrid Modified Artificial Bee Colony and Firefly Algorithm (HMABCFA) -Based Cluster Head Selection is proposed for ensuring energy stabilisation, delay minimisation and inter-node distance reduction for improving the network lifetime. This proposed HMABCFA integrates the benefit of the Firefly optimisation algorithm for generating a new position that which has the capability of replacing the position, which is not updated in the scout bee phase of ABC. This incorporation of Firefly optimisation algorithm into the ABC algorithm prevents the limitations of premature convergence, slow convergence and the possibility of being trapped into the local point of optimality in the clustering process. The modified ABC-based clustering process is phenomenal in improving the feasible dimensions for enhancing the process of exploitation and exploration. The results of the HMABCFA, on an average are confirmed to enhance the network lifetime by 23.21%, energy stability by 19.84% and reduce network latency by 22.88%, compared to the benchmarked approaches. [ABSTRACT FROM AUTHOR]

Newton's method is usually preferred when solving optimization problems due to its superior convergence properties compared to gradient-based or derivative-free optimization algorithms. However, deriving and computing second-order derivatives needed by Newton's method often is not trivial and, in some cases, not possible. In such cases quasi-Newton algorithms are a great alternative. In this paper, we provide a new derivation of well-known quasi-Newton formulas in an infinite-dimensional Hilbert space setting. It is known that quasi-Newton update formulas are solutions to certain variational problems over the space of symmetric matrices. In this paper, we formulate similar variational problems over the space of bounded symmetric operators in Hilbert spaces. By changing the constraints of the variational problem we obtain updates (for the Hessian and Hessian inverse) not only for the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method but also for Davidon–Fletcher–Powell (DFP), Symmetric Rank One (SR1), and Powell-Symmetric-Broyden (PSB). In addition, for an inverse problem governed by a partial differential equation (PDE), we derive DFP and BFGS "structured" secant formulas that explicitly use the derivative of the regularization and only approximates the second derivative of the misfit term. We show numerical results that demonstrate the desired mesh-independence property and superior performance of the resulting quasi-Newton methods. [ABSTRACT FROM AUTHOR]

Critical infrastructure networks, such as transport and power networks, are essential for the functioning of a society and economy. The rising transport demand increases the congestion in railway networks and thus they become more interdependent and more complex to operate. Also, an increasing number of disruptions due to system failures as well as climate changes can be expected in the future. As a consequence, many trains are cancelled and excessively delayed, and thus, many passengers are not reaching their destinations which compromises customers need for mobility. Currently, there is a rising need to quantify impacts of disruptions and the evolution of system performance. This review paper aims to set-up a field-specific definition of resilience in railway transport and gives a comprehensive, up-to-date review of railway resilience papers. The focus is on quantitative approaches. The review analyses peer-reviewed papers in Web of Science and Scopus from January 2008 to August 2019. The results show a steady increase of the number of published papers in recent years. The review classifies resilience metrics and approaches. It has been recognised that system-based metrics tend to better capture effects on transport services and transport demand. Also, mathematical optimization shows a great potential to assess and improve resilience of railway systems. Alternatively, data-driven approaches could be potentially used for detailed ex-post analysis of past disruptions. Finally, several rising future scientific topics are identified, spanning from learning from historical data, to considering interdependent critical systems and community resilience. Practitioners can also benefit from the review to understand a common terminology, recognise possible applications for assessing and designing resilient railway transport systems. [ABSTRACT FROM AUTHOR]

The paper presents the development of the mathematical model of joint parameters impact, during solid wood lengthening with a mini tooth, on the joint strength. Impact parameters are wood density (ρ), ratio of wood element width and thickness (b/h) and geometrical size of mini tooth expressed as ratio of length and distance between width of the tooth (l/p). This paper presents an example of how the process of joining solid wood can be optimized prior to the production process to avoid exploitation testing and increased costs of the process. Based on the obtained model (F), force optimization was performed as a function of the maximum tensile force. The results of the conducted research indicate that mathematical modelling and optimization can be successfully used to define the tensile break force and technological parameters of solid wood elements lengthened by finger joint. [ABSTRACT FROM AUTHOR]

In this paper, 1-Degree of Freedom (1-DOF) and 2-Degree of Freedom (2-DOF) Integer Order (IO) and Fractional Order (FO) Proportional–Integral–Derivative (PID) Controller has been designed for the Magnetic Levitation (Maglev) system. Maglev is one of the most versatile research-oriented laboratory instruments in the field of control systems engineering. The controller for the Maglev system is designed in such a way that a ferromagnetic ball can precisely levitate in a controlled electromagnetic field with particular design specifications. The controller parameters can be appropriately identified by optimizing the objective function with various popular artificial intelligence based evolutionary optimization algorithms. The 2-DOF controller structure takes care of both reference signal tracking and disturbance rejection through the feedback path. The 1 and 2-DOF controller structures with both Integer as well as Fractional Order approaches for different nonlinear systems were already discussed in various papers. However, the application of the Fractional Order control technique on a highly nonlinear Maglev system using the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Hybrid Particle Swarm Optimization (Hybrid PSO), and recently developed Jaya Algorithm is of our interest. Observation of the final analysis based on different simulation results states that as the controller structure moves from 1-DOF to 2-DOF, the performance of the system and the control signal gets improved. The robustness analysis has also been incorporated in this study which shows that the 1 and 2-DOF IOPID and FOPID controllers designed using different optimization algorithms for the Maglev plant are robust in nature. [ABSTRACT FROM AUTHOR]

Recently, hybrid distributed generation system has become a popular energy supply mode. It is obvious that the integrated system could improve energy efficiency and reduce costs. However, the system scheduling is a problem that would determine the operation cost. In this paper, a hybrid energy system including wind power, photovoltaics, gas turbines, and energy storage was introduced. In order to obtain the minimum operation cost, an operation optimization model was built. The schedule plan of each unit was optimized by moth flame optimization algorithm. Finally, through empirical research on a microgrid project, the optimization results in three configuration case of wind power, photovoltaics, and storage indicated that the operation optimization model in this paper could effectively reduce system operation cost, and the optimal output plan of each unit was obtained. And it is proved that the model proposed in this paper has a certain guiding role on economically dispatch of hybrid energy system. [ABSTRACT FROM AUTHOR]

This paper proposes a new block encryption algorithm for cryptographic information protection. It describes a new transformation method EM (Exponentiation Module), which is part of the algorithm, and a method of S-box obtaining. The paper also considers an optimization technique to advance the efficiency of key selection and calculation. We discuss the possibility to obtain good results by applying the peculiar properties of cryptographic primitives in the Galois field. To increase the strength and speed of the encryption algorithm, we used a nonpositional polynomial notation and an indexed view for the Galois field. The paper provides for statistical properties of the ciphertext obtained with the developed algorithm. We also present the results of differential and linear cryptanalysis of the S-box used. [ABSTRACT FROM AUTHOR]

This paper proposes a novel particle swarm optimization (PSO) algorithm with population reduction, which is called modified new self-organizing hierarchical PSO with jumping time-varying acceleration coefficients (MNHPSO-JTVAC). The proposed method is used for solving well-known benchmark functions, as well as non-convex and non-smooth dynamic economic dispatch (DED) problems for a 24 h time interval in two different test systems. Operational constraints including the prohibited operating zones (POZs), the transmission losses, the ramp-rate limits and the valve-point effects are considered in solving the DED problem. The obtained numerical results show that the MNHPSO-JTVAC algorithm is very suitable and competitive compared to other algorithms and have the capacity to obtain better optimal solutions in solving the non-convex and non-smooth DED problems compared to the other variants of PSO and the state of the art optimization algorithms proposed in recent literature. The source codes of the HPSO-TVAC algorithms and supplementary data for this paper are publicly available at . [ABSTRACT FROM AUTHOR]

This paper presents a non-intrusive framework for integrating existing unsteady partial differential equation (PDE) solvers into a parallel-in-time simultaneous optimization algorithm. The time-parallelization is provided by the non-intrusive software library XBraid [Parallel multigrid in time, software available at ], which applies an iterative multigrid reduction technique to the time domain of existing time-marching schemes for solving unsteady PDEs. Its general user-interface has been extended in [S. Günther, N.R. Gauger, and J.B. Schroder, A non-intrusive parallel-in-time adjoint solver with the XBraid library, Comput. Vis. Sci. 19 (2018), pp. 85–95. Available at arXiv, math.OC/1705.00663] for computing adjoint sensitivities such that gradients of output quantities with respect to design changes can be computed parallel-in-time alongside with the primal PDE solution. In this paper, the primal and adjoint XBraid iterations are embedded into a simultaneous optimization framework, namely the One-shot method. In this method, design updates towards optimality are employed after each state and adjoint update such that optimality and feasibility of the design and the PDE solution are reached simultaneously. The time-parallel optimization method is validated on an advection-dominated flow control problem which shows significant speedup over a classical time-serial optimization algorithm. [ABSTRACT FROM AUTHOR]

In this paper we show that for some problem classes it is possible to design Global Optimization algorithms which mimic existing procedures obtaining the same quality at a fraction of their computational cost. We achieved this applying clustering methods to identify regions of attraction of local minima. If we could identify the shape of regions of attraction, a local search starting from each of them would lead to the global minimum. This idea had been a winning one in the 1980s, and later abandoned when large dimensional global optimization problems were used to test global optimization algorithms. In this paper we show that by using the idea of clustering in a feature space of much smaller dimension than the original one, very significant speed ups can be obtained. We apply this idea to two of the most widely studied families of hard, large scale, global optimization problems: the optimization of the potential energy of atomic clusters, and the problem of packing identical spheres of largest radius in the unit hypercube. We could even improve some existing putative optima, thus proving that the proposed method is not only very efficient but also effective in exploring the feasible space. [ABSTRACT FROM AUTHOR]

The photovoltaic power system is highly dependent on the quantity of solar irradiance incident on it and its temperature. These parameters are dynamically changing with time due to climatic changes, shadows formed by clouds, trees, buildings, and so on, resulting in multiple maximum power points (MPP). Out of which one is global MPP (GMPP) and the rest are local MPP (LMPP). The GMPP varies dynamically along with the weather conditions and connected load. Many GMPP tracking (GMPPT) algorithms were developed which are inefficient and ineffective under dynamic irradiance conditions. This paper proposes a new enhanced arithmetic optimization algorithm based on the levy flight (AOA-LF) as a GMPPT method, which improves the tracking efficiency and tracking speed because of its good exploration and exploitation due to the long jump with a variable step size. The suggested AOA-LF GMPPT method is implemented in MATLAB/SIMULINK and tested with eight different dynamic irradiance patterns whose tracking curves are compared with the existing GMPPT algorithms like JAYA-LF, DFO, AOA, PSO, and P&O, which reveals that it is excellent in the tracking of GMPP with increment in efficiency as high as 19% when compared with P&O method and the least time to reach the GMPP with percentage decrement as high as 69% as compared to the PSO method. Also, the proposed AOA-LF GMPPT method is validated with the help of OPAL-RT OP4510 Real-Time Digital Simulator. Hence, the proposed algorithm shows superior performance with zero steady-state oscillations and also enhanced exploration and exploitation. [ABSTRACT FROM AUTHOR]

The problem addressed in this paper is the optimal allocation of a CPU to a number of software control tasks. Each task is used to implement a feedback controller for a linear and time invariant system and is activated with a fixed period. On every periodic activation, the task executes a job, which collects the output of the system, and produces the control values after executing for a random computation time. If a job's duration exceeds a deadline, then the job is cancelled and the control values are not updated. The systems to be controlled are affected by process noise. Therefore the performance of each control loop can be evaluated through the steady-state covariance of the system's state, which depends on the probability with which the task implementing the controller drops its jobs. We show that by making a proper choice for the scheduling algorithm, this probability can be straightforwardly computed as a function of the scheduling parameters. This observation enables the construction of a very efficient procedure for finding the scheduling parameters that attain the optimal tradeoff between the performance of the different control loops. [ABSTRACT FROM AUTHOR]

This paper focuses on the development of a robust accurate streamflow prediction model by balancing the abilities of exploitation and exploration to find the best parameters of a machine learning model. To do so, the simulated annealing (SA) algorithm is integrated with the mayfly optimization algorithm (MOA) as SAMOA to determine the optimal hyper-parameters of support vector regression (SVR) to overcome the exploration weakness of the MOA method. The proposed method is compared with the classical SVR and hybrid SVR-MOA. To examine the accuracy of the selected methods, monthly hydroclimatic data from Jhelum River Basin is used to predict the monthly streamflow on the basis of RMSE, MAE, NSE, and R2 indices. Test results show that the SVR-SAMOA outperformed the SVR-MOA and SVR models. SVR-SAMOA reduced the prediction errors of the SVR-MOA and SVR models by decreasing the RMSE and the MSE from 21.4% to 14.7% and from 21.7% to 15.1%, respectively, in the test stage. [ABSTRACT FROM AUTHOR]

The demand for long casting sequences together with a wide range of different steel grades inherently leads to the generation of incompatible steel intermixing during a grade change. This paper describes the development of an optimisation tool for casting sequences based on a mixing model. The mixing model accounts for mixing in the tundish and in the liquid core of the strand and was calibrated based on samples of the chemical composition taken from slabs containing intermixed steel. The optimisation of the casting sequence order by minimising incompatible intermixing was accomplished by applying heuristic optimisation algorithms developed for the solution of the 'Traveling Salesman Problem'. [ABSTRACT FROM AUTHOR]

Energy management strategy (EMS) is the core of hybrid electric vehicle (HEV) and directly determines the road performance of HEV. The transient characteristics of traffic information greatly affect the driving condition of the vehicle, making the demand torque or power of the vehicle change regularly with the traffic information. Therefore, based on traffic information fusion and power management to realize multi-objective optimization of EMS for HEV is of practical significance for reducing energy consumption and improving driving performance. The development status of EMS integrated with traffic information has been reviewed in this paper. First, EMS based on rules and optimizations and intelligent algorithms are discussed and compared. Second, the influential mechanism of traffic information on EMS is analyzed from driving condition prediction, multi-objective solution and energy management algorithm optimization. Finally, the main development directions of EMS integrated with traffic information are prospected from various aspects including traffic information acquisition, driving condition determination, energy management architecture, and online update mechanism optimization. [ABSTRACT FROM AUTHOR]

Power fluctuation at the PV panel output is caused by variation of input irradiance. Several optimization techniques have been used for selecting the Global Maximum Power Point (GMPP) from different Local Maximum Power Point (LMPP) that are caused due to variable irradiance. Reconfiguring the PV array by various combination based on the available irradiance is used to overcome the effects of partial shading. In these methods, system complexity increases and efficiency reduces as the size of PV array increases. Hence, in this paper Artificial Neural Network (ANN) based switching network of optimum number of PV panel with battery and supercapacitor is formed. Genetic Algorithm (GA) is used to optimize the number of PV panels, battery and supercapacitor. Battery and supercapacitors are used as energy back up to support the load during low power generation from PV panels. Size of supercapacitors is calculated to support the system during instant power fluctuation caused either due to sudden increase in load or unexpected variation in irradiance. Power enhancement of optimized PV array is performed by ANN based switching and numerically analyzed, and compared with other reconfiguration methods. Optimization is carried out for a system having 245 kW load which led to 1281 number of PV panels, 161 number of batteries and 8 supercapacitors for minimum cost of energy sources. Results of optimization are used for switching of 1281 PV array to form 16 × 81 strings. Out of which 16 × 9 strings are selected for Month Ahead Scheduling (MAS) and string of 16 × 1 PV panels for Real Time Scheduling (RTS). Considering variations at the input irradiance while load is constant. For the selected value of irradiance 16 × 45 strings remain ON which is selected as the base value. [ABSTRACT FROM AUTHOR]

An effort is given in this paper, for the first time, to discuss the load-frequency control (LFC) problem of an interconnected power system by implementing a novel heuristic optimization method called quasi-oppositional-based JAYA (QOJAYA). The proposed algorithm houses the knowledge of quasi-oppositional-based learning to enhance convergence speed and find the best solutions to the LFC problem. To show the effectiveness, the proposed QOJAYA scheme is, individually, applied to a two-area multi-unit multi-source power plant and a three-area hydrothermal power plant considering the system nonlinearities. The structural simplicity and performance adequacy of the well-known proportional-integral (PI) controller enforces to optimally design it as a secondary controller at the first instant. Afterward, the dynamic stability of the concerned power systems is increased with a 2DOF-PID controller. The proposed algorithm is employed to search the optimal settings of the said controllers by minimizing the time-based single-valued fitness functions. Simulation results validate the success of the proposed QOJAYA scheme and demonstrate its dominance over original JAYA, teaching-learning-based optimization (TLBO), and other recently introduced optimization techniques in the state-of-the-art for the same power system. Finally, the robustness of the designed controller is established by performing sensitivity analysis considering parameters and loading uncertainties. [ABSTRACT FROM AUTHOR]

In this paper, exergy analysis of a steam power plant located in southern Iran named Zarand power plant has been studied. In order to optimize the performance of the Rankine cycle and achieve higher exergy efficiency, several parameters have been considered as decision variables. Knowing that there is the ability to change some of the parameters in the specific range in the process of electricity production in power plant, temperature and output pressure of the boiler and output pressure of four steps of extraction turbine have been selected as six decision variables. Also, exergy efficiency has been considered as the objective function. For this purpose, the exergy efficiency of the system is optimized using intelligent algorithms including bees, fireflies, and algorithm based on teaching and learning and they are compared with each other. The results show that in the case of suitable changes of decision variables and applying appropriate algorithms, exergy efficiency of the studied thermal power plant can be increased from 30.1% to 30.68047%, 30.70368%, and 30.70369%, respectively. It means using optimization algorithms of bees, fireflies, training-learning, exergy efficiency of the Rankine cycle of the studied power plant can be increased by 0.58047%, 0.60368%, and 0.60369%, respectively. [ABSTRACT FROM AUTHOR]

Aged road pavements and insufficient maintenance budgets, along with increasing concerns over the environmental issues related to transportation have introduced additional challenges to highway agencies. Multiobjective optimisation techniques can be used to account for those multiple aspects in the design of maintenance and rehabilitation (M&R) strategies. Contrary to the single-objective optimisation problems where a single solution is optimal, the solution of multiobjective optimisation problems is a set of non-dominated solutions, often referred to as Pareto-optimal set. This set of optimal solutions represents the trade-off between the different and often conflicting objectives, and in many cases is comprised by a vast number of elements. This paper presents the development and application of a fuzzy logic expert system for selecting a single solution from the Pareto set obtained from the multiobjective optimisation of sustainable pavement M&R strategies. It provides decision-makers with an easy and intuitive methodology for the selection of the most preferred solution according to sustainability criteria. The proposed system is applied to a case study from France. Posteriorly, different strategies reflecting the decision-maker's preferences towards economic and environmental objectives are analysed. Conclusions and recommendations for future improvements are derived from this application. [ABSTRACT FROM AUTHOR]

In recent decades, traditional energy generation and consumption have undergone a transformation to cleaner energy sources. The transition to a sustainable energy system based on efficiency and renewable energies will thus require the replacement of a complex and heavily implemented energy system with one based on concepts such as microgrids. A microgrid is a controlled small-scale power system and the elements that make up it are: distributed generation systems; energy storage systems; load management techniques; monitoring systems, etc. Consequently, since ac devices are used, synchronization criteria must be satisfied to switch operation between them. The synchronization criteria consist of making the values of the phase-angle difference, slip frequency, and voltage difference as small as possible. This is very important to ensure the correct operation of the grid. In the other hand, the use of Particle Swarm Optimization (PSO) algorithm has been increasing, because of their simplicity and efficiency in engineering optimization problems. Therefore, this paper proposes an active synchronizing control scheme that allows to synchronize a generator set to the grid and a particle swarm optimization algorithm, which makes the synchronizing control as efficient as possible. With the implemented control and with the help of the synchronization algorithm, improvements in the reduction of settling time (from 9 seconds to 3 seconds) are obtained in comparison to other controls in the literature. [ABSTRACT FROM AUTHOR]

Striking developments have taken place in feedback control theory after the evolution of the fractional-order (FO) control concept. However, for large-scale (high-order) systems, these well-established FO techniques become rigorous and lead to an infeasible solution. To overcome this issue, this paper proposes a three-fold control policy. The first step finds the optimal reduced-order (low-order) model using Big-Bang, Big-Crunch (BB–BC) optimisation algorithm. Based on the obtained reduced model, the control structure is formulated in the internal model control (IMC) framework. The controller acquires a PID form followed by an additional term and FO integrator. Unlike the FO-PID controller which demands five tuning parameters, the proposed controller requires only two tuning parameters whose evaluation is done on the basis of user specified gain crossover frequency and phase margin. To substantiate the design approach, a detailed simulation study has been carried out, in which the problems of tracking control, disturbance rejection, and time-delay compensation are illustrated. [ABSTRACT FROM AUTHOR]

The design of a trajectory is significant to the efficiency and safety of a drilling process. In this paper, wellbore stability, indicated by the safe mud weight, is introduced as constraints to formulate a drilling trajectory optimisation problem. The upper and lower bounds of the safe mud weight are analysed according to the real data of a well. To solve the optimisation problem with strict constraints, we devise a new multi-objective optimisation algorithm. The devised algorithm takes advantage of inverted generational distance and knee points, and improves the convergence and diversity of the obtained solutions by modifying the selection process. Simulation results show that the devised algorithm is effective on the drilling trajectory optimisation problem constrained to wellbore stability. [ABSTRACT FROM AUTHOR]

Their work is an extension of algorithms devised for equilibrium problems, proposed by Moudafi, and by Iusem and Sosa, to the more general context of quasi-equilibrium problems. We are very proud to present the special issue of Optimization related to the XIII Brazilian Workshop on Continuous Optimization, held in September 2019, celebrating Alfredo Iusem's 70th anniversary. Pedro Jorge S. Santos, Paulo Sérgio M. Santos, and Susana Scheimberg contributed their paper I A Newton-type method for Quasi-Equilibrium Problems and applications i , where they extend a Newton-type method for particular Equilibrium Problems to the setting of Quasi-Equilibrium Problems. [Extracted from the article]

This paper presents the results of an experimental study of the detection of a defect in steel plates based on an optimisation algorithm using small amplitude homogenisation. In a first approximation using numerical simulation, this method detected inclusions using boundaries measurements obtained from static tests in the context of linear elasticity. We study the performance of such method by introducing direct measurements of real experiments. The defects in the specimens generate very small perturbations in displacements and therefore, it was necessary to perform several experimental realisations of the test procedure for dealing with random and systematic influences. We included in the analysis practical engineering considerations such as bolted and welded connections, a typical experimental setup and limited instrumentation. Detection results using experimental measurements shows that it is possible to use this method in real applications and that the results are improved when using an iterative scheme, although with limitations. For instance, the need to use larger measurement zones than those used in numerical simulations in order to avoid potential bias in the detection of defects due to errors in measurement. In this sense, it is also necessary the validation of support and loading conditions used in the simulations. [ABSTRACT FROM AUTHOR]

The radius of robust feasibility is the maximal size of uncertain set in which the robust feasible set for an uncertain program is nonempty. In this paper, we employ robust optimization technique to study a class of uncertain convex conic program, and give its formulas for radius of robust feasibility under several data uncertain sets. First, with aid of the distance from the origin to the so-called epigraphcal set, we provide computable upper and lower bounds of the radius of robust feasibility for convex conic program in face of ball uncertainty. Second, a formula is presented for the radius of robust feasibility for robust convex optimization problem with SOS-convex polynomial constraints under ball uncertain sets. Finally, some exact formulas of radius of robust feasibility are given for convex conic program with piecewise linear function constraints under boxes or polytopes uncertain sets. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new family of continuous-time optimisation algorithms based on discontinuous second-order gradient optimisation flows, with finite-time convergence guarantees to local optima, for locally strongly convex (time-varying) cost functions. To analyse our flows, we first extend a well-know Lyapunov inequality condition for finite-time stability, to the case of (time-varying) differential inclusions. We then prove the convergence of these second-order flows in finite-time. In some particular cases, we can show that the finite-time convergence can be pre-defined by the user. We propose a robustification of the flows to bounded additive uncertainties and extend some of the results to the case of constrained optimisation. We show the performance of these flows on well-known optimisation benchmarks, namely, the Rosenbrock function, and the Rastringin function. [ABSTRACT FROM AUTHOR]

A sparse array synthesis method based on improved wind-driven optimization algorithm (IWDO) and fast Fourier transform (FFT) for the purpose of sidelobe suppression was introduced. First, the uniform linear array was sparse by the FFT algorithm. Then the amplitude excitation weighting of array elements was optimized by IWDO algorithm based on probability factor chaotic map. During the optimization process, the minimum peak sidelobe level was used as the fitness function. By comparing with the simulation results of classic Chebyshev weighting method, the method proposed in this paper has better performance. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of the optimal approximation for a given matrix pencil (Ma, Da, Ga, Ka, Na) under the spectral constraint and the symmetric constraint. Such a problem arises in finite element model updating for damped gyroscopic systems. By using constrained optimization theory and matrix derivatives, an explicit formulation for the solution of the problem is established. The efficiency and accuracy of the proposed method is numerically verified by a simple five-degree-of-freedom system. [ABSTRACT FROM AUTHOR]

Accurate prediction of the scour hole depth and dimensions downstream of ski-jump spillways has been an important issue among hydraulic researchers for decades. In recent years, computing methods such as Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference Systems (ANFISs) and Support Vector Regression (SVR) have shown a powerful performance in the prediction of scour characteristics owing to their flexibility and learning nature. In the present paper, a new hybrid approach has been proposed for the first time in order to improve the estimation power of the SVR tool for scour hole geometry prediction below ski-jump spillways. The principal characteristics of the scour hole pattern in the equilibrium phase have been predicted using SVR optimized with Fruitfly Optimization Algorithms (FOAs). The hybrid model is compared with the corresponding simple SVR model. To evaluate the proposed hybrid model further, it is also compared with other machine learning and empirical methods, such as ANNs, ANFISs and regression equations. The results show that the proposed SVR-FOA method performs well, improves remarkably on Support Vector Machines (SVMs) results, estimates scour hole geometrical parameters more accurately than the simple SVR model, and can be applied as an alternative reliable scheme for estimations on which simple SVR and other methods demonstrate shortcomings. The proposed hybrid method improves the precision level for scour depth prediction by about 8% compared with simple SVM in terms of the correlation coefficient. [ABSTRACT FROM AUTHOR]

The aim of the paper is to propose a groundbreaking method for the aerodynamic optimization design of the bioinspired wing with leading-edge tubercles. An emphasis on the optimization design of the spanwise waviness in the leading edge for delaying stall and increasing lift from the aerodynamic performance perspective has been laid in this study. For the conversion of the wavy configuration, the form parameterized approach using F-spline curves has been used to produce more variants of the leading-edge tubercles. Numerical investigations of flow characteristics which are performed using CFD computations have been used to validate the numerical scheme with experimental data. The combination of Non-dominated Sorting Genetic Algorithm II and Response Surface Method based Kriging Model has been adopted as the aerodynamic optimization strategy. As consequence, the three main components of the optimization process are incorporated into the establishment of the aerodynamic optimization design system for the bio-inspired airfoil with leading-edge tubercles. The four optimal airfoils respectively which increases the stall angle as well as the lift have been obtained in contrast to the smooth wing. The optimized bio-inspired design of this kind can be applied to flow-controlled devices for improving the efficiency of a particular operating mechanism. [ABSTRACT FROM AUTHOR]

In this paper, pixelated Frequency Selective Surface (FSS) layer embedded in two carbonyl iron-filled silicon rubber (CISR) sheets are used for design of broadband microwave absorber in frequency range of 3.95 to 8.2 GHz. Top and bottom layers of CISR sheets are prepared with 24% and 33% of Carbonyl Iron (CI) powder by volume, respectively. Reflectivity of microwave absorber, complex permittivity and complex permeability of CISR sheets are evaluated using two Rectangular Dielectric Waveguide (RDWG) measurement systems in 3.95 to 5.85 GHz frequency band (WR-187) and 5.85 to 8.2 GHz frequency band (WR-137). An algorithm was developed using Geometry-Refinement Method (GRM) and Genetic Algorithm (GA) optimization technique to design pixelated FSS layer. Measured as well as simulated reflectivity of composite microwave absorber is better than −10 dB at normal incidence in the frequency range of 3.95–8.2 GHz. For oblique incidence from 0 ∘ to 40 ∘ , simulated reflectivity of TE and TM polarizations carried out for all angles of incidence. [ABSTRACT FROM AUTHOR]

Finding the best structure of ANN to minimize errors, the processing and the search time is one of the main objectives in the AI field. In order to achieve prediction with a high degree of accuracy in a short time, an enhanced PSO-based selection technique to determine the optimal configuration for the artificial neural network has been proposed in this paper. To design the neural network to minimize processing time, search time and maximize the accuracy of prediction, it is necessary to identify hyperparameter values with precision. PSO with 2-D search space has been employed to select the best hyperparameters in order to construct the best neural network where PSO is used as a decision-making model and ANN is used as a learning model. The suggested technique was used to select the optimal number of the hidden layer and the number of units per hidden layer. The proposed technique was evaluated using a chemical dataset. The result of testing the proposed technique displayed high prediction accuracy with MSE equal to 3.9% and the relative error between the expected output and actual target is less than 1.6%. The results of the comparison of the proposed technique with the ANN showed thatthe proposed approach could predict output with an infinitesimal error, outperforming the existing ANN model in terms of error ratio. [ABSTRACT FROM AUTHOR]