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The article focuses on network virtualization, a technique used to overcome the Internet's perceived ossification. It says that virtualization is used to combine and/or partition into virtual network resources the physical network resources. It says that the virtual networks' actual deployment needs the network operator to map onto physical resources the virtual resources. It mentions that the question on how to map optimally is usually called the Virtual Network Embedding (VNE) problem. It adds that several algorithms proposed for the solution of the problem concentrate on optimizing the application of resources in terms of performance.

Inventory is the core link of enterprise operation and an essential resource for enterprises to occupy the market. Therefore, optimizing the control and management of inventory is a necessary measure for enterprises to maintain their core competitiveness. However, at present, the optimization of the enterprise's inventory is basically still at the single inventory level, which seriously shortens the life cycle of the enterprise's products and reduces the speed of the enterprise's capital flow. Digital supply chain is the digitization of traditional supply chain services, which can change the traditional thinking of inventory control and management, break down the barriers between enterprises and modern logistics, and build a complete management system for enterprises. Smart logistics is a product of the new era, and it is also a product of the development of the digital supply chain. To this end, this paper proposes an inventory optimization algorithm based on digital supply chain and smart logistics. The algorithm is optimized on the basis of the original inventory optimization scheme, so that the improved algorithm can be more suitable for actual inventory management needs. The experimental results of this paper show that the improved new inventory optimization algorithm improves the comprehensive benefit by 39.1% on the original basis, which can effectively reduce the error in the actual use process. [ABSTRACT FROM AUTHOR]

Transportation sector is currently the largest carbon emitter and produces lots of harmful gases in the atmosphere, as a result Electric Vehicle (EV) is an attractive choice for mankind. Increased use of EVs needs the establishment of a large number of EV charging stations. This paper shows comprehensive assessment of solar powered charging station which is used for the charging of electric vehicles. This paper includes the design, cost optimization and management of an EV charging station along with pre-feasibility analysis for location assessment for EV charging stations. A case study for Ahmedabad is presented in this paper. Solar-Pro software is used for design, technical and financial assessment of solar powered based charging stations. Memetic and Cuckoo optimization techniques are used for cost optimization. Further Program, Evaluation and Review Technique (PERT) is used to assess the cradle to grave process of charging stations and at the end strengths, weaknesses, opportunities and threats (SWOT) and Political, Economic, Social, Legal, Environmental (PESTLE) analysis is presented, which shows strength and weakness of project from political, economical, social and ecological points of view. Results shows that Ahmedabad is suitable location for the solar powered charging station due to the mean value and maximum value of solar radiation is 5.7516 and 10.8869 kwh/m2/day respectively. The available energy of the given system is 30,506 kwh/m2/day at the solar fraction of 97.12%. The result of cuckoo algorithm for cost optimization is much better than memtic algorithm. Result also shows the reliability of whole system is 89%. [ABSTRACT FROM AUTHOR]

Currently, electrical distribution networks (EDNs) have used modern technologies to operate and serve many types of consumers such as renewable energy, energy storage systems, electric vehicles, and demand response programs. Due to the variability and unpredictability of these technologies, all these technologies have brought various challenges to the operation and control of EDNs. In this case, in order to operate effectively, it is inevitable that effective power redistribution is required in the entire network. In this paper, a multi-objective based dynamic optimal network reconfiguration (DONR) problem is formulated using power loss and voltage deviation index considering the hourly variation of load, photovoltaic (PV) power, and electric vehicle (EV) fleet load in the network. This paper introduces recently introduced meta-heuristic butterfly optimization algorithm (BOA) and it's improve variant of self-adaptive method (SABOA) for solving the DONR problem. The simulation study of IEEE 33-bus EDN under different conditions has proved the effectiveness of DONR, and its adoptability for real-time applications. In addition, by comparing different performance indicators (such as mean, standard deviation, variance, and average calculation time) of 50 independently run simulations, the efficiency of SABOA can be evaluated compared with other heuristic methods (HMs). Comparative studies show that SABOA is better than PSO, TLBO, CSA and FPA in the frequent occurrence of global optimal values. [ABSTRACT FROM AUTHOR]

Online learning is a classical algorithm for optimization problems. Due to its low computational cost, it has been widely used in many aspects of machine learning and statistical learning. Its convergence performance depends heavily on the step size. In this paper, a two-stage step size is proposed for the unregularized online learning algorithm, based on reproducing Kernels. Theoretically, we prove that, such an algorithm can achieve a nearly min–max convergence rate, up to some logarithmic term, without any capacity condition. [ABSTRACT FROM AUTHOR]

Local reconstruction from localized projections attains importance in Computed Tomography (CT). Several researchers addressed the local recovery (or interior) problem in different frameworks. The recent sparsity based optimization techniques in Compressed Sensing (CS) are shown to be useful for CT reconstruction. The CS based methods provide hardware-friendly algorithms, while using lesser data compared to other methods. The interior reconstruction in CT, being ill-posed, in general admits several solutions. Consequently, a question arises pertaining to the presence of target (or interior-centric) pixels in the recovered solution. In this paper, we address this problem by posing the local CT problem in the prior support constrained CS framework. In particular, we provide certain analytical guarantees for the presence of intended pixels in the recovered solution, while demonstrating the efficacy of our method empirically. [ABSTRACT FROM AUTHOR]

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

Recently, using a non-linear programming method authors have demonstrated that based on certain criteria if some non-significant selectivity constraints are relaxed, all remaining constraints happen to be feasible but the solution of coordination problem is not possible with the conventional objective function as optimization procedure leaves the solution space [20]. In coordination studies solution with the conventional objective function provides minimum relay operating times. The present paper describes an additional criterion so that the solution with the conventional objective function can be achieved. This paper also achieves the acceptable speed of the primary protection while attempting to coordinate the maximum relay pairs. Complete results are presented in this paper in this regard. This paper uses the non-linear Random Search Technique to solve the coordination problem, which has been successfully applied in many problem areas. [ABSTRACT FROM AUTHOR]

In this paper, oxidative desulfurization (ODS) process is studied for the purpose of removing the sulfur components from light gas oil (LGO) via experimentation and process modeling. A recently developed (by the authors) copper and nickel oxide based composite nano-catalyst is used in the process. The ODS experiments are conducted in a batch reactor and air is used as an oxidizer under moderate operation conditions. Determination of the kinetic parameters with high accuracy is necessary of the related chemical reactions to develop a helpful model for the ODS operation giving a perfect design of the reactor and process with high confidence. High conversion of 92% LGO was obtained under a reaction temperature of 413 K and reaction time of 90 min for synthesized Cu Ni /HY nano-catalyst. Here model based optimization technique incorporating experimental data is used to estimate such parameters. Two approaches (linear and non-linear) are utilized to estimate the best kinematic parameters with an absolute error of less than 5% between the predicted and the experimental results. An environmentally friendly fuel is regarded the main goal of this study, therefore the optimization process is then employed utilizing the validated model of the prepared composite nano-catalyst to get the optimal operating conditions achieving maximum conversion of such process. The results show that the process is effective in removing more than 99% of the sulfur from the LGO resulting in a cleaner fuel. [ABSTRACT FROM AUTHOR]

This paper presents a new multi-objective day-ahead market clearing (DAMC) mechanism with demand-side reserves/demand response (DR) offers, considering realistic voltage-dependent load modeling. The paper proposes objectives such as social welfare maximization (SWM) including demand-side reserves, and load served error (LSE) minimization. In this paper, energy and demand-side reserves are cleared simultaneously through co-optimization process. The paper clearly brings out the unsuitability of conventional SWM for DAMC in the presence of voltage-dependent loads, due to reduction of load served (LS). Under such circumstances multi-objective DAMC with DR offers is essential. Multi-objective Strength Pareto Evolutionary Algorithm 2+ (SPEA 2+) has been used to solve the optimization problem. The effectiveness of the proposed scheme is confirmed with results obtained from IEEE 30 bus system. [ABSTRACT FROM AUTHOR]

Supercritical water gasification (SCWG) is a novel and clean technology for lignite translating into hydrogen-rich gas. Previous experimental researches show that the use of external recycle system of liquid residual can improve the energy efficiency, but there is not a theoretical model to figure out the component of which exergy lost most and to provide guidance for further optimization of the existing system. In this paper, the thermodynamic model of liquid residual external recycle system was established, based on which energy and exergy balance of the system was evaluated and the exergy efficiency of the main equipment was calculated. Moreover, the influence of recycle flow ratio (0–37.5 %), gasification temperature (550 °C–650 °C), gasification pressure (23–25 MPa) and slurry concentration (2.73–4.15 %) on the exergy and energy efficiency were analyzed. The results showed that the exergy destruction rate of reactor was the highest, which reached 5.52 kW. Both energy and exergy efficiency increased as recycle flow ratio, gasification temperature and pressure increased. The energy and exergy efficiency of the system reached 70.26 % and 56.86 % respectively at the condition of recycle flow ratio of 30 %, gasification temperature of 650 °C, pressure of 25 MPa and slurry concentration of about 2.93 %. [ABSTRACT FROM AUTHOR]

The simulated annealing (SA) algorithm is a popular intelligent optimization algorithm which has been successfully applied in many fields. In this paper, we propose a software component under the Windows environment called goRDS which implements a refined descriptive sampling (RDS) number generator of high quality in the MATLAB programming language. The aim of this generator is to sample random inputs through the RDS method to be used in the Simple SA algorithm with swap operator. In this way, the new probabilistic meta-heuristic algorithm called RDS-SA algorithm will enhance the simple SA algorithm with swap operator, the SA algorithm and possibly its variants with solutions of better quality and precision. Towards this goal, the goRDS generator was highly tested by adequate statistical tests and compared statistically to the random number generator (RNG) of MATLAB, and it was proved that goRDS has passed all tests better. Simulation experiments were carried out on the benchmark traveling salesman problem (TSP) and the results show that the solutions obtained with the RDS-SA algorithm are of better quality and precision than those of the simple SA algorithm with swap operator, since the software component goRDS represents the probability behavior of the SA input random variables better than the usual RNG. [ABSTRACT FROM AUTHOR]

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

Living cells, type of substrate, enzymatic hydrolysis play an important role in the efficiency of ethanol production; however, the kinetic parameters of biochemical reactions necessary for modelling these processes are often not accessible directly through experiments. In this context, for the implementation of suitable operational strategies, it is necessary to have kineticmodels able to describe the process as realistically as possible. This paper proposes a comparative study of two nonlinear techniques for parametric identification of a kinetic model for ethanol production from recycled paper sludge in order to improve process performance. The parameters of the model are optimized by two methods: using the Levenberg-Marquardt optimization approach and Genetic Algorithms. The performances of both techniques are evaluated using a numerical simulation. The optimal value of these parameters have been obtained based on Genetic Algorithm. Finally, the effect of parametric adjustment and dilution rate on productivity was demonstrated by changing the batch operation to the continuous operating model. The maximum ethanol concentration was about 13.25 g/l in batch process and about 13.9 g/l at Dilution rate: 0.005 1/h corresponding to a productivity of 0.327 in continuous process. [ABSTRACT FROM AUTHOR]

The processes which contain at least one pole at the origin are known as integrating systems. The process output varies continuously with time at certain speed when they are disturbed from the equilibrium operating point by any environment disturbance/change in input conditions and thus they are considered as non-self-regulating. In most occasions this phenomenon is very disadvantageous and dangerous. Therefore it is always a challenging task to efficient control such kind of processes. Depending upon the number of poles present at the origin and also on the location of other poles in transfer function different types of integrating systems exist. Stable first order plus time delay systems with an integrator (FOPTDI), unstable first order plus time delay systems with an integrator (UFOPTDI), pure integrating plus time delay (PIPTD) systems and double integrating plus time delay (DIPTD) systems are the classifications of integrating systems. By using a well-controlled positioning stage the advances in micro and nano metrology are inevitable in order satisfy the need to maintain the product quality of miniaturized components. As proportional-integral-derivative (PID) controllers are very simple to tune, easy to understand and robust in control they are widely implemented in many of the chemical process industries. In industries this PID control is the most common control algorithm used and also this has been universally accepted in industrial control. In a wide range of operating conditions the popularity of PID controllers can be attributed partly to their robust performance and partly to their functional simplicity which allows engineers to operate them in a simple, straight forward manner. One of the accepted control algorithms by the process industries is the PID control. However, in order to accomplish high precision positioning performance and to build a robust controller tuning of the key parameters in a PID controller is most inevitable. Therefore, for PID controllers many tuning methods are proposed. the main factors that lead to lifetime reduction in gain loss of PID parameters are described in This paper and also the main methods used for gain tuning based on optimization approach analysis is reviewed. The advantages and disadvantages of each one are outlined and some future directions for research are analyzed. [ABSTRACT FROM AUTHOR]

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

In order to improve product development cycle, design engineers use multidisciplinary analysis tools which allow for better productivity. This paper covers the development of new tools to improve the preliminary design phase of turbine disc, being a critical part of aircraft engines. First, a new single platform D&A (Design & Analysis) tool integrating commercial CAD (Computer Aided Design) and FEA (Finite Element Analysis) software processing in batch mode is presented. This integrated architecture leads to a real improvement, enabling a cohesive single integrated simulation environment that offers significant time reduction on user manipulation and execution. An optimization of disc geometry is then performed by using different optimization algorithms and configurations for a given disc parameterized model. The results show potential improvement over the current preliminary rotor discs for life and burst limited design. Finally, optimal curves obtained by developing HPT (High Pressure Turbine) disc reference charts, indicate how to get the minimum weight for given mechanical performance without running any structural analysis. These new tools supporting disc design have allowed improvement of disc life and durability leading to a reduction of preliminary design phase duration. [ABSTRACT FROM AUTHOR]

With the latest smart technologies in the electricity sector, the consumers of electricity got the opportunity to reduce their electricity consumption cost by participating in the demand response programs offered by the utility companies. In this paper, a model of energy management system is introduced for the energy scheduling at home. Residential automatic smart appliances of general use are selected for energy scheduling. The energy controlling device in the EMS model receives the real time electricity price signals from the utility company and schedule the appliances according to the user requirements in such a way so that the electricity consumption cost could be minimized. The appliances are scheduled under real time pricing combined with inclined block rate pricing scheme so that the peak to average ratio of power could be maintained in the satisfactory range. This helps the utility companies in maintaining the system reliability. For the solution of the scheduling problem, particle swarm optimization algorithm is used due to its effectiveness and easy implementation. Finally, the results have been compared and verified against the results achieved by genetic algorithm. [ABSTRACT FROM AUTHOR]

This paper presents an elite opposition-based cognitive behavior optimization algorithm (ECOA). The traditional COA is divided into three stages: rough search, information exchange and share, and intelligent adjustment process. In this paper, we introduce the elite opposition-based learning in the third stage of COA, with a view to avoid the latter congestion as well as to enhance the convergence speed. ECOA is validated by 23 benchmark functions and three engineering design problems, and the experimental results have proven the superior performance of ECOA compared to other algorithms in the literature. [ABSTRACT FROM AUTHOR]

In the real-world complex product design (CPD) process, task allocating is an ongoing reactive process where the presence of unexpected design change is usually inevitable. Therefore, reallocating is necessary to respond to design change positively as a procedure to repair the affected task plan. General reallocating literature addressed the reallocating versions with fixed executing time. In this paper, a multi-objective reallocation model is developed with a feasible assumption that the task executing time is controllable. To illustrate this idea, a compressing executing time strategy (CETS) is proposed in CPD process, where the executing time can be controlled with a non-linear compression cost. When design change occurs during the executing, task-resource reallocating is required to absorb the interference effects. Reallocating implies an increase in design cost and system instability; the proposed method CETS can address this issue effectively. CETS considers three objectives: completing time, stability, and change-adaptation cost. An adaptive multi-objective hybrid genetic algorithm and tabu search (AMOGATS) is developed to solve this mathematical method. The computational results of specific simulation examples verify the superiority. It shows that CETS is sensitive to design change, and the proposed algorithm AMOGATS can be effective to achieve the allocating by coordinating the objective consistency. [ABSTRACT FROM AUTHOR]

This paper presents an optimum day-ahead scheduling of thermal and renewable (wind and solar photovoltaic) power generation as a multi-objective optimization (MOO) problem considering the uncertainty. System operating cost (i.e. cost of thermal, wind, solar PV and battery), reliability and emission cost are considered to be optimized simultaneously. The uncertainties due to the generator outages, wind, solar PV power and load forecast errors are incorporated in the proposed optimization problem using the Loss Of Load Probability (LOLP) and Expected Unserved Energy (EUE) reliability indices. In the proposed approach, the amount of spinning reserves (SRs) required are scheduled based on the desired level of system reliability. The proposed multi-objective optimization problem is solved using NSGA-II algorithm. Different case studies are performed considering different objective functions that may be selected by system operator (SO) based on the preference. [ABSTRACT FROM AUTHOR]

We provide a characterization of the radii minimal projections of polytopes onto j-dimensional subspaces in Euclidean space 피n. Applied to simplices this characterization allows to reduce the computation of an outer radius to a computation in the circumscribing case or to the computation of an outer radius of a lower-dimensional simplex. In the second part of the paper, we use this characterization to determine the sequence of outer (n - 1)-radii of regular simplices (which are the radii of smallest enclosing cylinders). This settles a question which arose from an error in a paper by Weißbach (1983). In the proof, we first reduce the problem to a constrained optimization problem of symmetric polynomials and then to an optimization problem in a fixed number of variables with additional integer constraints. [ABSTRACT FROM AUTHOR]

The output power curve of the photovoltaic array presents a multi-peak characteristic under partial shading conditions, which causes the traditional maximum power point tracking technology to fail to guarantee the maximum power output of the photovoltaic cell. In response to this problem, this paper proposes to apply the improved Mayfly Optimization Algorithm (MA) to the maximum power tracking control. By introducing the gravity factor and limiting the search space of male mayfly, the optimization accuracy of the algorithm is enhanced, the vibration of the algorithm near the MPP is reduced, and the occurrence of premature phenomenon is avoided. Three test functions are selected to verify the algorithm, and under the conditions of rapid irradiance changes and complex shadow occlusion, an MPPT model based on the Boost circuit is established to verify the effectiveness of the algorithm. The simulation results show that the improved MA algorithm can effectively converge to MPP under complex shading conditions, and the output efficiency of photovoltaic arrays can be maintained above 99.96%. The average tracking time for different shading patterns is about 0.15 s. [ABSTRACT FROM AUTHOR]

Intelligent threshold value improves the speed and accuracy of any fault detector unit. But in the literature, any such traditional threshold selection mechanism is not available to implant in the fault detector unit of protective relay. This paper introduces a swarm intelligence-based threshold setting mechanism for fault detector. In this context, the Euclidean distance approach (EDA) with a single detection index is used for transmission network protection. The detection index function is formulated with fault parameters on a priority base. Further, the modified particle swarm optimization technique with adaptive inertia strategy is used to identify the optimal threshold value of detection index function. With swarm intelligence based optimal threshold, numerous cases are investigated on a test power system and obtained results show the efficacy of the method. [ABSTRACT FROM AUTHOR]

Capacitors and series voltage regulators are used extensively in distribution systems to reduce power losses and improve the voltage profile along the feeders. This paper deals with the problem of contemporaneously choosing optimal locations and sizes for both capacitors and series voltage regulators in three-phase, unbalanced distribution systems. This is a mixed, non-linear, constrained, multi-objective optimization problem that usually is solved in deterministic scenarios. However, distribution systems are stochastic in nature, which can lead to inaccurate deterministic solutions. To take into account the unavoidable uncertainties that affect the input data related to the problem, in this paper, we have formulated and solved the multi-objective optimization problem in probabilistic scenarios. To address the multi-objective optimization problem, algorithms were used in which all the objective functions were combined to form a single function. These algorithms allow us to transform the original multi-objective optimization problem into an equivalent, single-objective, optimization problem, an approach that appeared to be particularly suitable since computational time was an important issue. To further reduce the computational efforts, a linearized form of the equality constraints of the optimization model was used, and a micro-genetic algorithm-based procedure was applied in the solution method. [ABSTRACT FROM AUTHOR]

By virtue of the excellent aerodynamic performances, conformal phased arrays have been attracting considerable attention. However, for the synthesis of patterns with low/ultra-low sidelobes of the conventional conformal arrays, the obtained dynamic range ratios of amplitude excitations could be quite high, which results in stringent requirements on various error tolerances for practical implementation. Time-modulated array (TMA) has the advantages of low sidelobe and reduced dynamic range ratio requirement of amplitude excitations. This paper takes full advantages of conformal antenna arrays and time-modulated arrays. The active-element-pattern, including element mutual coupling and platform effects, is employed in the whole design process. To optimize the pulse durations and the switch-on instants of the time-modulated elements, multiobjective invasive weed optimization (MOIWO) algorithm based on the nondominated sorting of the solutions is proposed. A S-band 8-element cylindrical conformal array is designed and a S-band 16-element cylindrical-parabolic conformal array is constructed and tested at two different steering angles. [ABSTRACT FROM AUTHOR]

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

The pigeon-inspired optimization algorithm is a category of a newly proposed swarm intelligence-based algorithm that belongs to the population-based solution technique. The MKP is a class of complex optimization problems that have many practical applications in the fields of engineering and sciences. Due to the practical applications of MKP, numerous algorithmic-based methods like local search and population-based search algorithms have been proposed to solve the MKP in the past few decades. This paper proposes a modified binary pigeon-inspired optimization algorithm named (Modified-BPIO) for the 0 - 1 multidimensional knapsack problem (MKP). The utilization of the binary pigeon-inspired optimization (BPIO) for solving the multidimensional knapsack problem came with huge success. However, it can be observed that the BPIO converges prematurely due to lost diversity during the search activities. Given the above, the crossover operator is integrated with the landmark component of the BPIO to improve the diversity of the solution space. The MKP benchmarks from the Operations Research (OR) library are utilized to test the performance of the proposed binary method. Experimentally, it is concluded that the proposed Modified-BPIO has a better performance when compared with the BPIO and existing state-of-the-arts that worked on the same MKP benchmarks. [ABSTRACT FROM AUTHOR]

Clustering is an unsupervised kind of grouping of data points based on the similarity that exists between them. This paper applied a combination of particle swarm optimization and K-means for data clustering. The proposed approach tries to improve the performance of traditional partition clustering techniques such as K-means by avoiding the initial requirement of number of clusters or centroids for clustering. The proposed approach is evaluated using various primary and real-world datasets. Moreover, this paper also presents a comparison of results produced by the proposed approach and by the K-means based on clustering validity measures such as inter- and intra-cluster distances, quantization error, silhouette index, and Dunn index. The comparison of results shows that as the size of the dataset increases, the proposed approach produces significant improvement over the K-means partition clustering technique. [ABSTRACT FROM AUTHOR]

This paper proposes the efficient approaches for solving the Optimal Power Flow (OPF) problem using the meta-heuristic algorithms. Mathematically, OPF is formulated as non-linear equality and inequality constrained optimization problem. The main drawback of meta-heuristic algorithm based OPF is the excessive execution time required due to the large number of power flows needed in the solution process. The proposed efficient approaches uses the lower and upper bounds of objective function values. By using this approach, the number of power flows to be performed are reduced substantially, resulting in the solution speed up. The efficiently generated objective function bounds can result in the faster solutions of meta-heuristic algorithms. The original advantages of meta-heuristic algorithms, such as ability to handle complex non-linearities, discontinuities in the objective function, discrete variables handling, and multi-objective optimization, etc., are still available in the proposed efficient approaches. The proposed OPF formulation includes the active and reactive power generation limits, Valve Point Loading (VPL) and Prohibited Operating Zones (POZs) effects of generating units. The effectiveness of proposed approach is examined on IEEE 30, 118 and 300 bus test systems, and the simulation results confirm the efficiency and superiority of the proposed approaches over the other meta-heuristic algorithms. The proposed efficient approach is generic enough to use with any type of meta-heuristic algorithm based OPF. [ABSTRACT FROM AUTHOR]

Dynamic transmission network expansion planning (DTNEP) decides when (which year) and where (which R.O.W.), the new lines are to be built so that the overall cost of the plan is minimum and the system is secure. Generally, this is done for a fixed time horizon window (3/5 years) at a time. DTNEP has been proved to be better than the incremental approach in which optimal investment plans are found out for each year separately. In DTNEP, 1st year and last/terminal years are given different treatment, and there is a complete disconnect between two DTNEP windows. Also DTNEP assumes that the conditions during optimization like the earlier year generation, transmission expansion plans get\ executed exactly as per plan &/or the load forecast for the entire period remains unchanged. However, it is well known that this is generally not true. In view of above, this paper presents a rolling window based approach which treats every year of investment in exactly the same fashion. It also has an inherent capability to take care of such changes. It involves successive, overlapping DTNEP for 3/5 years planning horizon. A harmony search algorithm is used for the solution of the problem. Results for two test systems have been obtained to demonstrate the potential of the algorithm. [ABSTRACT FROM AUTHOR]

In the paper [Eppler and Harbrecht, Control & Cybernetics 34: 203–225, 2005], the authors investigated the identification of an obstacle or void of perfectly conducting material in a two-dimensional domain by measurements of voltage and currents at the boundary. In particular, the reformulation of the given nonlinear identification problem was considered as a shape optimization problem using the Kohn and Vogelius criterion. The compactness of the complete shape Hessian at the optimal inclusion was proven, verifying strictly the ill-posedness of the identification problem. The aim of the paper is to present a similar analysis for the related least square tracking formulations. It turns out that the two-norm-discrepancy is of the same principal nature as for the Kohn and Vogelius objective. As a byproduct, the necessary first order optimality condition are shown to be satisfied if and only if the data are perfectly matching. Finally, we comment on possible consequences of the two-norm-discrepancy for the regularization issue. [ABSTRACT FROM AUTHOR]

In this paper, fractional order PID controller, as well as integer order PID controller, is designed for non-linear system to enhance the system's performance and gain the stability. The novelty of the work is achieved by the development of a new methodology for integer order PID and fractional order PID control tuning by optimizing the parameters of controllers using the Genetic Algorithms optimization technique. The performance of any system mainly depends upon how efficiently the controller will be working and hence that's how most crucial part of the designing of FOPID controller or any controller is the tuning of its parameters. The uniquely designed and tuned parameters of the FOPID controller which is obtained by optimizing all the five parameters by using an evolutionary algorithm optimization technique i. e. a genetic algorithm which is a very powerful search tool and carrying heuristic characteristics. This method of tuning the FOPID controller which is designed and has been applied over the conical tank (nonlinear) system. The most important step in applying genetic algorithm is the selection of the fitness function and hence Integral of time multiplied by absolute error (ITAE) have been used here as the fitness function. Each chromosome comprised of all the five parameters of FOPID controller, which have been further optimised using above mentioned fitness function. From the simulation results, it can be observed that the solutions which are obtained optimally, presents an excellent performance for the system studied, by improving the behaviour of the system satisfactorily. Simulation results also show that the proposed FOPID controller gives improved performance over classical PID controller in terms of IAE and TV. [ABSTRACT FROM AUTHOR]

The multivariable systems have to control by using multiloop controllers and each closed loop controller has unique characteristics. The successful model structure for design of control system is extremely subject to the accurate choice of the tuning parameters ( K p , K i , K d , λ , μ K_p, K_i, K_d, \lambda, \mu ) of the controller. The choice of optimal tuning parameters of Fractional Order PID (FOPID) controller leads to accurate controlling of desired level in multivariable system. Here, for multivariable system, a FOPID controller design based on the advanced optimization technique called Teaching Learning based optimization (TLBO) algorithm is proposed. The goal of paper is (i) The elimination of interaction between the control loops and (ii) Reference tracking along the disturbance in each loop. These objectives are satisfied by using four cost function, namely, integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE) and integral time square error (ITSE). Out of these cost functions, ITAE based FOPID controller design using TLBO algorithm provides better performance in terms of fast reference tracking and disturbance elimination in the loop. Moreover, the comparative analysis of convergence characteristics of each objective of the controller by using TLBO is presented. The simulation study confirms that the TLBO algorithm based FOPID controller for multivariable systems (2 × 2) are more robust and exhibits superior response with respect to other algorithm. [ABSTRACT FROM AUTHOR]

This paper presents the solution to mitigate the total harmonic distortion (THD) in multilevel inverters (MLIs) using novel improved whale optimization algorithm (IWOA). The IWOA falls under the category of swarm-based nature inspired optimization algorithms. It uses a novel diffusion process using a random walk technique and utilizes an additional ranking system to estimate the optimum solution to minimize THD. Moreover, THD minimization is further accomplished through nine various meta-heuristic algorithms for investigation and comparative analysis. The selected algorithms along with the proposed IWOA are rigorously tested on single phase 5 and 7 level cascaded H-Bridge MLIs for various performance parameters such as consistency, computational efficiency and speed of convergence. It is found that the proposed algorithm outperforms the nine algorithms and is efficient for THD minimization for modulation index (MI) in the range of 0–1. The results are analyzed and reported after thorough verification using MATLAB simulation. It presents a novel switching technique through improved whale optimization algorithm (IWOA) to optimize the switching angles of a single phase 5 and 7 level multilevel inverters (MLIs) in order to minimize the total harmonic distortion (THD). IWOA incorporates a novel diffusion process and two new position updating techniques The proposed IWOA provides better computational efficiency, improved consistency and faster convergence compared to the older whale optimization algorithm (WOA) with minimal tuning of the algorithm's parameters. The proposed IWOA is compared alongside various meta-heuristics like genetic algorithm (GA), particle swarm optimization (PSO), grey wolf optimizer (GWO), krill herd (KH), artificial electric field algorithm (AEFA), sun flower optimizer (SFO), galactic swarm optimization (GSO), fruit fly optimization algorithm (FOA) and whale optimization algorithm (WOA) for optimal THD minimization. [ABSTRACT FROM AUTHOR]

Recently, the voltage stability and control of distribution networks become challenges due to the large line impedance, load variations and particularly the presence of distributed generation. This paper presents a coordinated voltage control scheme of distribution systems with distributed generation based on on-load tap changer and shunt capacitors. The problem is to determine the optimal operation of voltage regulation devices to minimize a multi-objective function including power losses, voltage deviations and operation stresses while subject to the allowable voltage ranges, line capacity and switching stresses, etc. The problem is formulated and solved by a modified particle swarm optimization algorithm to treat the large-scale and high nonlinearity property. The proposed scheme is applied to a typical 48-bus distribution network in Vietnam. The result of simulation shows that the voltage profile can be improved while the power loss of distribution systems can be reduced significantly. [ABSTRACT FROM AUTHOR]

Based on deeply analysis for optimization process of basic fruit fly optimization algorithm (FOA), a new improved FOA (IFOA) method is proposed, which modifies random search direction, increases the adjustment coefficient of search radius, and establishes a multi-sub-population solution mechanism. The proposed method can process the nonlinear objective function that has non-zero and non-negative extreme points. In the paper, IFOA method is applied to ill-conditioned problem solution in the field of surveying data processing. Application of the proposed method on two practical examples show that solution accuracy of IFOA is superior to that of three well-known intelligent optimization algorithms and two existing improved FOA methods, and it is also better than truncated singular value decomposition method and ridge estimation method. In addition, compared with intelligent search method represented by particle swarm optimization algorithm, The IFOA method has the advantages of less parameter settings, simple optimization process and easy program implementation. So, IFOA method is feasible, effective and practical in solving ill-conditioned problems. [ABSTRACT FROM AUTHOR]

Mixed-variable optimization problems consist of the continuous, integer, and discrete variables generally used in various engineering optimization problems. These variables increase the computational cost and complexity of optimization problems due to the handling of variables. Moreover, there are few optimization algorithms that give a globally optimal solution for non-differential and non-convex objective functions. Initially, the Jaya algorithm has been developed for continuous variable optimization problems. In this paper, the Jaya algorithm is further extended for solving mixed-variable optimization problems. In the proposed algorithm, continuous variables remain in the continuous domain while continuous domains of discrete and integer variables are converted into discrete and integer domains applying bound constraint of the middle point of corresponding two consecutive values of discrete and integer variables. The effectiveness of the proposed algorithm is evaluated through examples of mixed-variable optimization problems taken from previous research works, and optimum solutions are validated with other mixed-variable optimization algorithms. The proposed algorithm is also applied to two-plane balancing of the unbalanced rigid threshing rotor, using the number of balance masses on plane 1 and plane 2. It is found that the proposed algorithm is computationally more efficient and easier to use than other mixed optimization techniques. [ABSTRACT FROM AUTHOR]

Poor design choices at the early stages of software development and unprincipled maintenance practices usually deteriorate software modularity and subsequently increase system complexity. In object-oriented software, improper distribution of classes among packages is a key factor, responsible for modularity degradation. Many optimization techniques to improve the software modularity have been proposed in the literature. The focus of these optimization techniques is to produce modularization solutions by optimizing different design quality criteria. Such modularization solutions are good from the different aspect of quality; however, they require huge modifications in the existing modular structure to realize the suggested solution. Thus these techniques are costly and time consuming if applied at early stages of software maintenance. This paper proposes a search-based optimization technique to improve the modularity of the software system with minimum possible variation between the existing and produced modularization solution. To this contribution, a penalized fitness function, namely, penalized modularization quality, is designed in terms of modularization quality and the Move or Join Effectiveness Measure metric. Furthermore, this fitness function is used in both single-objective genetic algorithm (SGA) and multi-objective genetic algorithm (MGA) to generate the modularization. The effectiveness of the proposed remodularization approach is evaluated over five open-source and three random generated software systems. The experimentation results show that the proposed approach is able to generate modularization solutions with improved quality along with lesser perturbation compared to their non-penalty counterpart and at the same time it performs better with the MGA compared to the SGA. The proposed approach can be very useful, especially when total remodularization is not feasible/desirable due to lack of time or high cost. [ABSTRACT FROM AUTHOR]

This paper introduces a new variant of the particle swarm optimization (PSO) algorithm, designed for global optimization of multidimensional functions. The goal of this variant, called ImPSO, is to improve the exploration and exploitation abilities of the algorithm by introducing a new operation in the iterative search process. The use of this operation is governed by a stochastic rule that ensures either the exploration of new regions of the search space or the exploitation of good intermediate solutions. The proposed method is inspired by collaborative human learning and uses as a starting point a basic PSO variant with constriction factor and velocity clamping. Simulation results that show the ability of ImPSO to locate the global optima of multidimensional functions are presented for 10 well-know benchmark functions from CEC-2013 and CEC-2005. These results are compared with the PSO variant used as starting point, three other PSO variants, one of which is based on human learning strategies, and three alternative evolutionary computing methods. [ABSTRACT FROM AUTHOR]

Transmission network expansion planning is a very critical problem due to not only the huge investment cost involved, but also the associated security issues. Any long range planning problem is confronted with the challenge of non-statistical uncertainty in the data. Although large number of papers have been published in this area, the efforts to tackle the above mentioned security and uncertainty issues have been relatively very few, due to the formidable complexity involved. This paper tries to bridge this gap by proposing a technique to tackle these problems. Boundary DC power flow is used to ascertain the worst power flows on the lines. A simple basic binary Genetic algorithm is used to solve the optimization problem as an illustration. Results for two sample test systems have been obtained to demonstrate the potential of the proposed method. [ABSTRACT FROM AUTHOR]

Nature has the ability of sustainability and improvisation for better survival. This unique characteristic reflects a pattern of optimization that inspires the computational intelligence toward different scopes of optimization: a nondeterministic optimization approach or a nature-inspired metaheuristic algorithm. To date, there are many metaheuristic algorithms introduced with good promising results and also becoming a powerful method for solving numerous optimization problems. In this paper, a new metaheuristic algorithm inspired from a plant growth system is proposed, which is defined as tree physiology optimization (TPO). A plant growth consists of two main counterparts: plant shoots and roots. Shoots extend to find better sunlight for the photosynthesis process that converts light and water supplied from the roots into energy for plant growth; at the same time, roots elongate in the opposite way in search for water and nutrients for shoot survival. The collaboration from both systems ensures plant sustainability. This idea is transformed into an optimization algorithm: shoots with defined branches find the potential solution with the help of roots variable. The shoots-branches extension enhances the search diversity and the root system amplifying the search via evaluated fitness. To demonstrate its effectiveness, two different classes of problem are evaluated: (1) a continuous benchmark test function compared to particle swarm optimization (PSO) and genetic algorithm (GA) and (2) an NP-hard problem with the traveling salesman problem (TSP) compared to GA and nearest-neighbor (NN) algorithm. The simulation results show that TPO outperforms PSO and GA in all problem characteristics (flat surface and steep-drop with a combination of many local minima and plateau). In the TSP, TPO has a comparable result to GA. [ABSTRACT FROM AUTHOR]

This paper presents optimal power flow management in real time for grid connected photovoltaic (PV)–battery system. The objective is the real time, dynamic and optimal scheduling of battery storage which reduces grid power consumption and achieves the peak load shaving. For this purpose, two strategies are developed; one is rule-based and other is optimization algorithm based. Two strategies are compared, pros and cons of those strategies are established. Though rule-based technique is simple & easy to implement, the optimization technique provides more optimal regulation of battery which provides the lesser electricity cost. Forecasted PV/load powers are used to get optimal value of battery power. In order to handle uncertainties due to forecasted data, fuzzy-based corrective strategy is developed to modify the reference battery power as per the present values of PV/load powers. The proposed analysis is carried out for over one exemplary day. The simulations are presented using typical load/PV data of India and Finland as a part of research collaboration. Experimental results are also carried out using proposed scheme. [ABSTRACT FROM AUTHOR]

The multi-reservoir systems optimization problem requires defining a set of rules to recognize the water amount stored and released in accordance with the system constraints. Traditional methods are not suitable for complex multi-reservoir systems with high dimensionality. Recently, metaheuristic-based algorithms such as evolutionary algorithms and local search-based algorithms are successfully used to solve the multi-reservoir systems. β-hill climbing is a recent metaheuristic local search-based algorithm. In this paper, the multi-reservoir systems optimization problem is tackled using β-hill climbing. In order to validate the proposed method, four-reservoir systems used in the literature to evaluate the algorithm are utilized. A comparative evaluation is conducted to evaluate the proposed method against other methods found in the literature. The obtained results show the competitiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The objective of this paper is to optimize maintenance scheduling of generating units of a captive thermal plant using intelligent optimization techniques such as genetic algorithm (GA) and simulated annealing (SA). A multi-class maintenance scheduling has been considered to incorporate statutory safety regulations for maintenance of boilers, turbines and generators. Such multi-class maintenance scheduling in terms of mathematical formulations necessitates a planning horizon of five years compared to the conventional one to two year horizon. As the utilities catered by captive power plants are very sensitive to power failure, the reliability of power supply has been evaluated by loss of load probability (LOLP) index. The significant contribution of this paper is to evaluate confidence intervals of LOLP instead of conventional point estimate of LOLP. A point-estimate of LOLP uses expected values and provides no information about the range of values over which LOLP may vary. Some variations from optimum schedules are anticipated while executing maintenance schedules due to different real-life unforeseen exigencies. Such exigencies are considered in terms of near-optimum schedules obtained from SA during the final stages of convergence. LOLP evaluated from such sub-optimum schedules forms the basis to treat LOLP as a random variable for subsequent evaluation of confidence intervals. Case studies pertaining to a captive power plant for an aluminium smelter corroborate that the schedules obtained from two optimization techniques yield the same optimum schedule and thus the efficacy of the proposed algorithm is validated. Also the interval of confidence for LOLP denotes the possible risk in a quantified manner and it is of immense use from perspective of captive power plants intended for quality power. [ABSTRACT FROM AUTHOR]

The paper by Bond, Harrison, and O'Brien illustrates the role that convergence criteria, search algorithms, and starting values can play in influencing the success of numerical optimization. One aspect contributing to the importance of these choices in their results appears to be a multivariate generalization of the "pile-up" phenomenon for maximum likelihood estimation of moving average processes. Their results suggest advantages in some settings of using either Bayesian methods or an alternative specification of the random field. [ABSTRACT FROM AUTHOR]

Rational expectations are not required to follow from beliefs that explain well history, but just to correctly foresee the future. As a consequence, at a rational expectations equilibrium, the agents' expectations may follow from beliefs that explain poorly the observed history, even among those rationalizing their choices. This paper shows, firstly, that if agents hold rationally formed expectations instead - in the sense of following from beliefs that explain history better than any other beliefs justifying their choices - then allocations unsupported by rational expectations can be shown to be equilibrium outcomes. By means of this result, it is established, secondly, that adding the common knowledge of the rationality of the formation of expectations to that of the rationality of choices and of market clearing, still does not suffice to guarantee rational expectations. Finally, the rationally formed expectations equilibria produced in this paper exhibit a sunspot-like volatility that, interestingly enough, do not rely on an explicit sunspot mechanism. [ABSTRACT FROM AUTHOR]

Recent advancements in inverse design approaches, exemplified by their large-scale optimization of all geometrical degrees of freedom, have provided a significant paradigm shift in photonic design. However, these innovative strategies still require full-wave Maxwell solutions to compute the gradients concerning the desired figure of merit, imposing, prohibitive computational demands on conventional computing platforms. This review analyzes the computational challenges associated with the design of large-scale photonic structures. It delves into the adequacy of various electromagnetic solvers for large-scale designs, from conventional to neural network-based solvers, and discusses their suitability and limitations. Furthermore, this review evaluates the research on optimization techniques, analyzes their advantages and disadvantages in large-scale applications, and sheds light on cutting-edge studies that combine neural networks with inverse design for large-scale applications. Through this comprehensive examination, this review aims to provide insights into navigating the landscape of large-scale design and advocate for strategic advancements in optimization methods, solver selection, and the integration of neural networks to overcome computational barriers, thereby guiding future advancements in large-scale photonic design. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) energy has become a promising energy source because the demand for electrical energy from renewable energy sources is increasing worldwide in recent decades. Due to efficiency issues, the Maximum Power Point Tracking (MPPT) has been developed to optimize the solar panel's performance. This paper presents an MPPT model, made up of the analog component, which overcomes traditional MPPT methods' weakness via the Perturb and observes (P&O) technique. In this case, the PV system includes a PV array, a DC/DC boost converter, a battery, and a load. The proposed method was precisely built and simulated using the Powersim, MATLAB Simulink, and SimCoupler Module. The components of the analog MPPT system were designed practically in detail. The experiment was carried out by using European Efficiency Test 50530, and the results showed the proposed model has higher efficiency over the digital MPPT technique, about 99.99% as maximum. Moreover, MPPT methods were tested under steady-state, irradiation variation, and space conditions to verify the system's potential capability with PV module Solbian 52L. [ABSTRACT FROM AUTHOR]

The paper implements and optimizes the performance of a currently proposed chaos-deoxyribonucleic acid (DNA)-based hybrid approach to encrypt images using a bi-objective genetic algorithm (GA) optimization. Image encryption is a multi-objective problem. Optimizing the same using one fitness function may not be a good choice, as it can result in different outcomes concerning other fitness functions. The proposed work initially encrypts the given image using chaotic function and DNA masks. Further, GA uses two fitness functions – entropy with correlation coefficient (CC), entropy with unified average changing intensity (UACI), and entropy with number of pixel change rate (NPCR) – simultaneously to optimize the encrypted data in the second stage. The bi-objective optimization using entropy with CC shows significant performance gain over the single-objective GA optimization for image encryption. [ABSTRACT FROM AUTHOR]