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This paper presents the comparison ofselective harmonic elimination in a cascaded h-bridge multilevel (CHBMLI) inverter using PSO and TLBO optimization algorithms. The inverters are employed in a variety of applications, including high-voltage dc transmission, variable drive systems, and electrical vehicles. The inverter's output includes harmonics. PSO and TLBO algorithm is used in this paper To find out the best switching angles for a CHBMLI selective harmonic elimination. In order to get the appropriate fundamental component and get rid of low-order harmonics, this project solves nonlinear equations. These algorithms can produce the most effective triggering pulses to minimize Overall Harmonic Distortion(THD) and Selected Harmonics. If the harmonics are removed, the inverter's output will be improved. MATLAB is used to implement and compared both optimization techniques usingthe seven-level inverter. [ABSTRACT FROM AUTHOR]

In order to solve the ill-posed problem in super-resolution image reconstruction, this paper proposes an adaptive regularization way use sparse representation. We build a new L1/2 non-convex optimization model and apply reweighted L2 Norm for the adaptive algorithm in this paper. Experimental results show the significant effect in denoising and preserving edge details. It outperforms some traditional methods in the value of peak signal to noise ratio and structural similarity [ABSTRACT FROM AUTHOR]

This paper attempts to compare the two socio-inspired based metaheuristics algorithms which were introduced recently. These metaheuristics optimizations and search techniques "Election algorithm, and Election Campaign Optimization Algorithm" were inspired by socio-political processes of human ideologies. This paper gives an overview of these algorithms with respect to their control parameters, components, intensification and diversification as well as their applications. It also makes a simple comparison between the two metaheuristics algorithms in terms of quality of solution, ease of finding correct control parameters and further explored their strengths and limitations as well as their superiority over other metaheuristics algorithms. [ABSTRACT FROM AUTHOR]

Optimizing modes of the modern power system on active power is a complex problem of nonlinear mathematical programming. Despite the existence of many methods and algorithms for solving this problem, the issues of their improvement in the direction of correctly taking into account the factors that affect the regime remains an important problem. One of such factors is functional inequality constraints. The paper presents a new algorithm for optimising the modes of power system, taking into account losses in networks and functional constraints in the form of inequalities on power flows of controlled power transmission lines (PTL). A characteristic feature of the algorithm is taking into account the losses in branches by transferring them to the nodes of the electrical network. The algorithm's efficiency is investigated by the example of optimization of the mode of a complex electric power system with four thermal power plants participating in the optimization, ten PTL, in two of which the active power flows are controlled. In this case, the power flows in the controlled lines are determined by linearized formulas using the power distribution coefficients of the nodes. The economic effect on the total fuel consumption due to the use of the proposed algorithm is 0.45%. [ABSTRACT FROM AUTHOR]

OCR post-processing is an important step to improve OCR text accuracy. It includes two main tasks, error detection and error correction. Hill climbing algorithm is a heuristic search method used for solving optimization problems. In this paper, we present a novel OCR error correction approach using an adapted version of the Hill climbing algorithm. Correction candidates of OCR errors are explored by random character edits and evolved with the Hill climbing. The character edit patterns are obtained from the training data. The proposed model is evaluated on the benchmark dataset in the OCR post-correction competition of the International Conference on Document Analysis and Recognition 2017. It is shown that our model outperforms various baseline approaches in the competition. In addition, the randomness of the proposed algorithm is analyzed to verify its stability under parameter configurations. [ABSTRACT FROM AUTHOR]

Electrochemical machining (ECM) process has a wide capability to generate complex shapes on different materials which are occasionally difficult to cut. Its ability to machine a variety of materials makes it an extensively accepted non-traditional machining process in modern day manufacturing sector. Thus, selection of the optimal input parameters for an ECM process is crucial for its efficient utilization. In this paper, a comparative analysis is made among four metaheuristics, i.e. firefly algorithm (FA), differential evolution (DE), ant colony optimization (ACO) algorithm and teaching-learning-based optimization (TLBO) algorithm to discover the optimal values of various control parameters for an ECM process. Dimensional inaccuracy, tool life and material removal rate are the three responses considered which are subjected to temperature, choking and passivity constraints. The TLBO algorithm shows the best performance among the others without violating any of the constraints. The paired t-test is also performed to prove the efficacy of TLBO algorithm over the other optimization techniques. The results derived from these algorithms are finally compared with those obtained by the past researchers using other optimization methods for both single and multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

Topology optimization, although well recognized is still widely developed. It has gained recently more attention since large computational ability become available for designers. This process is stimulated simultaneously by variety of emerging, innovative optimization methods. It is observed that traditional gradient-based mathematical programming algorithms, in many cases, are replaced by novel and e cient heuristic methods inspired by biological, chemical or physical phenomena. These methods become useful tools for structural optimization because of their versatility and easy numerical implementation. In this paper engineering implementation of a novel heuristic algorithm for minimum compliance topology optimization is discussed. The performance of the topology generator is based on implementation of a special function utilizing information of compliance distribution within the design space. With a view to cope with engineering problems the algorithm has been combined with structural analysis system Ansys. [ABSTRACT FROM AUTHOR]

In order to optimize the recognition accuracy of maize kernels breakage detection and improve the detection efficiency of maize kernels breakage, this paper using computer vision technology and detecting of the maize kernels breakage based on K-means clustering algorithm. First, the collected RGB images are converted into Lab images, then the original images clarity evaluation are evaluated by the energy function of Sobel 8 gradient. Finally, the detection of maize kernels breakage using different pixel acquisition equipments and different shooting angles. In this paper, the broken maize kernels are identified by the color difference between integrity kernels and broken kernels. The original images clarity evaluation and different shooting angles are taken to verify that the clarity and shooting angles of the images have a direct influence on the feature extraction. The results show that K-means clustering algorithm can distinguish the broken maize kernels effectively. [ABSTRACT FROM AUTHOR]

The paper presents an application of a novel concept of graph - the Incompatibility and Complement Graph - to state assignment for asynchronous FSMs. A specific feature of the graph is that it contains two kinds of edges: connecting mutually incompatible nodes, and connecting mutually complementing nodes. The graph can be useful in certain class of optimization problems, in which compatibility of bit patterns both in the true, and in the complemented form has to be analyzed. An example of such a problem is covering analysis in state assignment for asynchronous FSMs. The paper presents a simple example explaining the method. Appropriate algorithms for the graph building and coloring are also presented. [ABSTRACT FROM AUTHOR]

In this paper multi-Tasking operation-based power converter is designed with 3 sources that offers stability and better, control Which essential in conventional converters power distribution. The design of converter covers operation of Bi-direction DC-DC, Bi-direction DC-AC and AC-DC which is applied to electrical vehicle charging converter operation. The operation of proposed system is control using pulse given to breaker to achieve the required operation. The pulse signals given to circuit breaker and MOSFET switches is accordingly, and it is controlled by teaching and learning optimization technique. TLBO technique is proposed to solve constrained in design optimization problem with the function parameters designed with variable objective function for the desired operation to obtain optimum value of objective function. The proposed system output made to give to EV charging port and battery further to test stability of system. Buck and boost operation, ac to dc, dc to ac modes is operating mode of MULTI-TASKING converter with TLBO algorithm incorporates using MATLAB designed in Simulink environment. Experimental results suggest the proposed converter model with controller better response for EVs vehicles based on solar PV source charging. [ABSTRACT FROM AUTHOR]

This paper aims at developing a multi-objective nonlinear optimization algorithm applied to coordinated motorway ramp metering. The multi-objective function includes two components: traffic and safety. Off-line simulation studies were performed on A4 France Motorway including 4 on-ramps. [ABSTRACT FROM AUTHOR]

Any large functioning system consists of equipment that needs to be repaired during its lifetime. The methods of mathematical programming are used to formalize the optimization problem of preventive equipment repair planning in this paper. The method is used to find the annual optimal plan of preventive equipment repair. The goal of such plan is the uniform distribution of repair works by the months. It is supposed, that the deviation from the standard plan of repair works can not be more than one month. An algorithm to find the optimal plan of repair works is provided. A numerical example is given. [ABSTRACT FROM AUTHOR]

The conjugate gradient (CG) and Davidon, Fletcher and Powell (DFP) method are both well known solvers for solving unconstrained optimization problems. In this paper, we proposed a new hybrid DFP-CG method and compared with the ordinary DFP method in terms of number of iteration and CPU times. Numerical results show that the new algorithm is more efficient compared to the ordinary DFP method and proven to posses both sufficient descent and global convergence properties. [ABSTRACT FROM AUTHOR]

Customer positioning has always been a problem that enterprises focus on. In this paper, FCM clustering algorithm is used to cluster customer groups. However, due to the traditional FCM clustering algorithm, which is susceptible to the influence of the initial clustering center and easy to fall into the local optimal problem, the short board of FCM is solved by the gray optimization algorithm (GWO) to achieve efficient and accurate handling of a large number of retailer data. [ABSTRACT FROM AUTHOR]

This paper speaks to the changed Tropos objective model for handling the danger related with the product advancement cycle. Here the modification is done by the hybrid optimization algorithm. The tropos model will be improved by selecting the optimal candidate solution by using Hybrid Gravitational Search Algorithm – Particle Swarm Optimization algorithm. The proposed approach characterizes a strategy to dissect the relationship between the hubs of each layer to assess their odds of raising the dangers. Here the risks are prioritized and which are identified by the improved Tropos method. The obtained results show that, the proposed optimization-based goal model has delivered better performance in terms of time and memory requirement when compared with the normal model and also the risk and cost of the proposed model is proved to be more efficient than existing model. [ABSTRACT FROM AUTHOR]

An adaptive cockroach swarm optimization (ACSO) algorithm is proposed in this paper to strengthen the existing cockroach swarm optimization (CSO) algorithm. The ruthless component of CSO algorithm is modified by the employment of blend crossover predator-prey evolution method which helps algorithm prevent any possible population collapse, maintain population diversity and create adaptive search in each iteration. The performance of the proposed algorithm on 16 global optimization benchmark function problems was evaluated and compared with the existing CSO, cuckoo search, differential evolution, particle swarm optimization and artificial bee colony algorithms. [ABSTRACT FROM AUTHOR]

In view of the needs of the vehicle unit, this paper has carried on the research to the sensor distribution question. In the case of fully taking into account the information such as target identity and target priority, the mathematical model of optimal sensor allocation is established based on the maximum information gain criterion, and then the population-based incremental learning algorithm is used to solve. Finally, the validity of the method is verified by solving the problem of sensor assignment problem of the vehicle unit. [ABSTRACT FROM AUTHOR]

The field of nature inspired computing and optimization techniques have evolved to solve difficult optimization problem in diverse fields of engineering, science and technology. The firefly attraction process is mimicked in the algorithm for solving optimizing proble3ms. In Firefly Algorithm (FA) sorting of fireflies is done by using sorting algorithm. The original FA is proposed with bubble sort for ranking the fireflies. In this paper, the quick sort replaces bubble sort to decrease the time complexity of FA. The dataset used is unconstrained benchmark functions from CEC 2005 [22]. The comparison of FA using bubble sort and FA using quick sort is performed with respect to best, worst, mean, standard deviation, number of comparisons and execution time. The experimental result shows that FA using quick sort requires less number of comparisons but requires more execution time. The increased number of fireflies helps to converge into optimal solution whereas by varying dimension for algorithm performed better at a lower dimension than higher dimension. [ABSTRACT FROM AUTHOR]

Consideration was given to the problem of relative positioning with the use of alternating magnetic field. The problem was shown to be solvable using a system of three magnetic dipoles emitting magnetic field at different frequencies. Ambiguity of its solution was discussed, and ways to resolve it were proposed. An algorithm for relative positioning based on the solution of this problem was proposed. Error equations of the positioning algorithm and the results of their analysis were presented. Calibration procedures, improving accuracy of relative positioning, are also considered. One of these procedures, based on nonlinear optimization techniques, was presented in greater details. The paper was supplied with several numerical examples demonstrating effectiveness of proposed algorithms in relative positioning. [ABSTRACT FROM AUTHOR]

Data Deduplication is defined as an elimination process of redundant duplicated data; this process is stratified by using a unique value that been represented by a chunk of data, which is referenced by the original file that contains this chunk. Data Deduplication techniques have been mainly applied in the cloud-based systems in order to decrease the space size of the data storage, and enhance the connection bandwidth. In this paper, we have introduced a data deduplication optimization technique that been applied to the data storage of cloud-based systems. The proposed technique optimizes data deduplication by implementing Source-Based and In-Line based techniques. The Source-Based method is stratified at the source that contains the data, on the other hand, the In-Line method is stratified at the RAM that contains the data momentarily before the writing process of the I/O. Moreover, the proposed technique applies a Content-Based chunking algorithm with Variable Chunking utilization by using Rabin Karp Rolling Hash (RKRH). RKRH is a data chunking algorithm that breaks data files into different segments sizes. Generally, the proposed technique process is based on calculating the hash value of data chunk which considered as a fingerprint. Afterward, the chunk availability process is applied in order to identify the existence of this chunk in the storage; therefore, if this chunk does not exist in the storage a reference to this chunk is added and store the hash value as a key in the storage. The proposed technique includes data chunk compression to reduce the data redundancy in the same chunk. Practically, the proposed technique provides a data deduplication ratio of 33 percent and an average upload latency of five seconds. Finally, the proposed approach utilized with any data file type as a byte stream. [ABSTRACT FROM AUTHOR]

Presented paper performs an analysis of quadrature signal processing algorithms for high demanding laser interferometry applications. Careful signal processing is required to minimize nonlinearities which come from optical path and components' imperfections, and reduce overall instrumental error. Paper focuses on algebraic fits, because implementation for real time systems was a main requirement. The most demanding applications are stationary measurements where the position slightly fluctuates in the range below one fringe period. Therefore, analysis was performed for samples that were spread along a few milliradians of a full circle. [ABSTRACT FROM AUTHOR]

In this paper, we present a comparative study involving several penalty functions that can be used in a penalty approach for globally solving bound mixed-integer nonlinear programming (bMIMLP) problems. The penalty approach relies on a continuous reformulation of the bMINLP problem by adding a particular penalty term to the objective function. A penalty function based on the 'erf' function is proposed. The continuous nonlinear optimization problems are sequentially solved by the population-based firefly algorithm. Preliminary numerical experiments are carried out in order to analyze the quality of the produced solutions, when compared with other penalty functions available in the literature. [ABSTRACT FROM AUTHOR]

Power wiring is the cornerstone of urban expansion and social progress. In recent decades, the traditional route routing method takes the first place, that is, to minimize the construction pressure without considering the cost first. However, this method is often restricted by many unexpected factors, and it often takes a long time to choose an appropriate path, which seriously affects the construction progress. Combined with the intelligent algorithm and the understanding of the real situation, we designed the power wiring system (PW system) and cabling optimization system (CO system). Through computer programming, the actual problems are digitized, and the actual problems are transformed into virtual points. The swarm intelligence algorithm is used to generate the optimal path through the computer. At the same time, the use of the computer can compare and choose the problems and factors that are difficult to be considered at the same time in the traditional wiring. Moreover, the flexibility and convenient manipulation of the computer also create convenient conditions for the later maintenance, which can effectively improve the efficiency of power wiring and reduce the wiring cost. Through the research on intelligent cabling, it aims to provide more effective methods for power cabling and help solve the problem of power demand arising from the rapid development of society. [ABSTRACT FROM AUTHOR]

In this paper, the global minimization problem of a multi-dimensional black-box Lipschitzian function is considered. In order to pass from the original Lipschitz multi-dimensional problem to a univariate one, an approach using space-filling curves to reduce the dimension is applied. The method does not use derivatives and, at each iteration, works with a set of estimates of the Hölder constant of the reduced one-dimensional problem. A two-phase technique is applied to accelerate the search of the global minimum. Numerical experiments carried out on several hundreds of test functions show a promising performance of the discussed algorithm in comparison with its direct competitors. [ABSTRACT FROM AUTHOR]

This paper summarizes the collaborative work done by CENER, Fraunhofer ISE and CRS4 within the scope of the STAGE-STE project related to heliostat field generation algorithms and their application to small heliostats (<10 m²). Radially staggered heliostat field layouts have been commonly applied to real solar fields with known results. However, their use in conjunction with small heliostats or the use of brand new ways for heliostat allocation, such natural pattern based algorithms, is still unknown. Herein, the most promising heliostat field generation algorithms are studied and compared, in annual optical efficiency terms, for different scenarios. These efficiencies correspond to the best heliostat layouts found by the optimization process that each generation algorithm can create, for three scenarios. Results show that the fields from the selected algorithms lead to similar optical efficiencies. The slight differences are not enough evidence to nominate one of the algorithms as the best choice, taking into account the inherent error of the simulation tools, the optimization process and further requirements needed in commercial applications (e.g. access paths) not coped in this study. [ABSTRACT FROM AUTHOR]

Assembly sequence planning (ASP) plays an important role in manufacturing because it determines the investment on the assembly facilities. Recently, various research works have been done to optimize the ASP problem using different optimization algorithms. Besides the algorithm efficiency, another factor that directly contributed to the quality of the solution is how the problem is modelled. According to the existing research, the most accurate approach to represent the ASP is to model base on the part of product. However, this approach resulting in the large number of assembly sequence possibility, and finally make the optimization process become harder. This paper proposed a simpler version of part-based representation using the base part concept. In the proposed model, the base part for the product is defined prior to the establishment of the precedence constraint. This approach has contributed to a smaller search space that resulting in better chance to obtain optimum solution during the optimization. The proposed ASP modelling approach however limited the number of possible solution, since the base part was fixed prior to the optimization. [ABSTRACT FROM AUTHOR]

This paper proposes a novel parallelization of ABC Algorithm to solve Short-term Hydro-Thermal Scheduling (SHTS) problem. Parallelization is a novel way of improving the convergence of the algorithm. In case of normal ABC algorithms accelerating operation or modification can be used for better convergence and diversity. But with large number of variables the non-linearity of the search space increases and these modifications are not sufficient to give enough diversity in the crowds of swarms for finding better solutions. However, parallelization of the algorithm increases the diversity in solutions and the search capability. The performance of the proposed algorithm is experimented on two short term multi reservoir cascaded hydrothermal systems. First test case is moderately large while the second test case is a considerably large one. Experimental results demonstrate the superior performance of the proposed algorithm as compared to other recently reported ones in terms of convergence and better quality of solutions. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new global optimization algorithm for non-smooth unconstrained optimization problems. We construct a new global optimization (GO) method based on the function new smoothing technique. We give some numerical examples in order to demonstrate the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of finding a Bayesian net representation of the probability function that agrees with the distributions of multiple consistent datasets and otherwise has maximum entropy. We give a general algorithm which is significantly more efficient than the standard brute-force approach. Furthermore, we show that in a wide range of cases such a Bayesian net can be obtained without solving any optimisation problem. [ABSTRACT FROM AUTHOR]

In this paper, a modification of spectral conjugate gradient (CG) method is proposed which combines the advantages of the spectral CG method and the RMIL method namely as spectral Khadijah-Rivaie-Mustafa-Ibrahim (SKRMI) to solve unconstrained optimization problems. Based on inexact line searches, the objective function generates a sufficient descent direction and the global convergence property for the proposed method has been proved. Moreover, the method reduces to the standard RMIL method if exact line search is applied. Numerical results are also presented to examine the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Computer simulation of multiscale burning and detonation processes requires an exaflop-scale performance supercomputer. The paper present research from SRISA intended to development high-performance architectures of DSP extensions for burning process simulations. Also a number of solutions for dataflow coprocessor development based on self-timed circuits are proposed. [ABSTRACT FROM AUTHOR]

The contribution is the second step of the optimization process introduced on AMEE'13 where the position of cooling canal was searched. In this paper the outward shape of the regulation current body located in the axis of the system is optimized to obtain required temperature on the outward surface of the tube. The algorithm was designed and debugged for simplified model, in which the plunger is replaced by the tube, which is surrounded by thermal source representing cooled glass moulded piece from outward, and the cooling water of temperature 15°C on input flowing through. The state problem is formulated as a stationary heat conduction process. The cost functional is taken as the second power of L² distance of temperature from the given constant value on the outward boundary of the tube. The results of the numerical optimization to three required target temperatures 700, 750 and 800°C of the outward tube surface together with the distribution of temperatures on the interface between the tube and the heat source before and after the optimization process are presented. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid optimization algorithm to be used in feature selection problem. The proposed algorithm utilizes the power of sine cosine algorithm and Cuckoo search to both exploration and exploitation the search space to reach at best promising solution. The empirical results are evaluated on four datasets, named Breast, Leukemia, Heart and Iris that obtained from UCI repository and compared with the native sine cosine and other feature selection algorithms include Particle swarm optimization and Antlion algorithm. The results of the experiments clearly showed the efficiency of the hybrid algorithm to exploration and exploitation the features space for finest features and improved high classification rate with low run time on all used datasets. [ABSTRACT FROM AUTHOR]

In order to optimize the cloud computing task scheduling scheme, an improved differential evolution algorithm for cloud computing task scheduling is proposed. Firstly, the cloud computing task scheduling model, according to the model of the fitness function, and then used improved optimization calculation of the fitness function of the evolutionary algorithm, according to the evolution of generation of dynamic selection strategy through dynamic mutation strategy to ensure the global and local search ability. The performance test experiment was carried out in the CloudSim simulation platform, the experimental results show that the improved differential evolution algorithm can reduce the cloud computing task execution time and user cost saving, good implementation of the optimal scheduling of cloud computing tasks. [ABSTRACT FROM AUTHOR]

We have reviewed the simulation modeling problems concerning the technological process of transport support. We have proposed the solutions to optimization problems. An optimal path algorithm has been developed in the Anylogic software. Also, the optimization process of the number of vehicles in the specified conditions has been performed. The results of computer experiments are given. [ABSTRACT FROM AUTHOR]

The general mathematical optimization model of multimodal logistics operations carried out by a transport company is discussed. On the basis of application of the modified heuristic principle of the generalization to the specialized version of Wilson formula, the theoretical algorithm for obtaining the fuzzy-multiple estimation of the optimized order size related to functions of multimodal transport company as a logistic mediator and distributor, taking into account the uncertainty of exogenous parameter of company expenses on storage of the supplies is described. [ABSTRACT FROM AUTHOR]

For the BN-800 reactor, for which the refueling mechanism consists of three rotating plugs, a time-optimal algorithm is proposed that works on the assumption that only two plugs can rotate at the same time. Application of such an algorithm will help to reduce the shutdown time of the power unit for refueling and, accordingly, increase the utilization factor of the installed capacity of the NPP unit. [ABSTRACT FROM AUTHOR]

Here we proposed a solution for the flow shop scheduling problems in a lot streaming with the intention of diminishing the Makespan. This particle swarm optimization algorithm is summarized here. The method has been used for a collection of different issues and the efficiency is calculated by comparing the results with other literatures. Finally, we concluded that the proposed developed algorithm works best when a local search is applied. [ABSTRACT FROM AUTHOR]

Solving global optimization problems plays a key role in most branches of natural sciences whilst dealing with everyday problems. With the help of optimization algorithms and the exponentially fast growth of the capabilities of the underlying hardware, the scale of feasible optimization tasks is reaching new levels. We help this goal with revisiting GLOBAL, a stochastic optimization method aiming to solve non-linear constrained optimization problems by the penalty function approach. It is a versatile tool for a broad range of problems, proven to be competitive in multiple comparisons. With the similarly rapid evolution of programming habits and tools, time naturally passed by GLOBAL’s latest implementation so that it became a drawback. Now, we present GlobalJ, a fully modularized Java framework refurbishing and extending the potential of this algorithm. [ABSTRACT FROM AUTHOR]

In the article, an application of evolutionary topology optimization procedure to searching alternative quasi-optimal solutions is presented. In the work, the constant criterion surface algorithm of evolutionary topology optimization is used. The algorithm is equipped with the procedure of restarting the search after reaching the subsequent quasi-optimal solution. This procedure uses a simulated annealing mechanism that is suitable for finding an optimal solution but also provides a set of alternative solutions. As shown in the test example, this method enables effective search within the solution space and finding a high number of alternative design layouts. [ABSTRACT FROM AUTHOR]

The problem of constructing nanophotonic structures of arbitrary geometry with prescribed properties was studied using an adaptive optimization algorithm. Stability estimates for the forward and adjoint problems involved in this algorithm are presented. A numerical example illustrates the construction of nanostructure in two dimensions. [ABSTRACT FROM AUTHOR]

The performance of a new mechanism applied on a differential evolution algorithm with an individual-dependent mechanism (IDE) is studied experimentally. Three new IDE variants are proposed and compared with the original IDE variant. The performance of all studied algorithms was compared on each problem of CEC 2015 test suite. The newly proposed IDE variants mostly outperformed the original IDE variant significantly. The results show that the best results are for the newly proposed IDE variant with an enhanced mutation scheme and IDE with the control of the population diversity. [ABSTRACT FROM AUTHOR]

Simulation-driven optimization problems are often used with the aid of metamodels, as the latter provide predicted objective values at a lower computational cost. To further improve the prediction accuracy, an ensemble of metamodel incorporates several metamodels concurrently and aggregates their prediction into a single one. An open question is which types of metamodels should be incorporated, and this is typically not known a-priori, while using a fixed topology may hamper the optimization process. To address this issue, this study proposes a new metamodel-assisted algorithm with dynamic topology adaptation, namely, it autonomously adapts the ensemble topology during the search, and selects the most suitable topology as the search progresses. An extensive performance analysis shows the effectiveness of the proposed algorithm, and highlights the merit of the proposed topology adaptation. [ABSTRACT FROM AUTHOR]

We address optimal control problems for nonlinear systems with pathwise state-constraints. These are challenging nonlinear problems for which the number of discretization points is a major factor determining the computational time. Also, the location of these points has a major impact in the accuracy of the solutions. We propose an algorithm that iteratively finds an adequate time-grid to satisfy some predefined error estimate on the obtained trajectories, which is guided by information on the adjoint multipliers. The obtained results show a highly favorable comparison against the traditional equidistant-spaced time-grid methods, including the ones using discrete-time models. This way, continuous-time plant models can be directly used. The discretization procedure can be automated and there is no need to select a priori the adequate time step. Even if the optimization procedure is forced to stop in an early stage, as might be the case in real-time problems, we can still obtain a meaningful solution, although it might be a less accurate one. The extension of the procedure to a Model Predictive Control (MPC) context is proposed here. By defining a time-dependent accuracy threshold, we can generate solutions that are more accurate in the initial parts of the receding horizon, which are the most relevant for MPC. [ABSTRACT FROM AUTHOR]

Optimization with stochastic algorithms has become a relevant research field. Due to its stochastic nature, its assessment is not straightforward and involves integrating accuracy and precision. Performance profiles for the mean do not show the trade-off between accuracy and precision, and parametric stochastic profiles require strong distributional assumptions and are limited to the mean performance for a large number of runs. In this work, bootstrap performance profiles are used to compare stochastic algorithms for different statistics. This technique allows the estimation of the sampling distribution of almost any statistic even with small samples. Multiple comparison profiles are presented for more than two algorithms. The advantages and drawbacks of each assessment methodology are discussed. [ABSTRACT FROM AUTHOR]

We propose a new method for solving univariate global optimization problems by combining a lower bound function given in (αBB) method [1] [For more details (Algorithms,Methods) one see [1]], with the improved lower bound function of the method developed in [2]. The new lower bound function is better than the two lower bound functions by its construction. The complementarity of the two lower bound functions allows us to derive the convex/concave test and the pruning step which accelerate the convergence of the proposed method. Illustrative examples are treated efficiently. [ABSTRACT FROM AUTHOR]

Recently, subspace quasi-Newton (SQN) method has been widely used in solving large scale unconstrained optimization. Besides constructing sub-problems in low dimensions so that the storage requirement as well as computational cost can be reduced, it can also be implemented extremely fast when the objective function is a combination of computationally cheap non-linear functions. However, the main deficiency of SQN method is that it can be very slow on certain type of non-linear problem. Hence, a preconditioner which is computationally cheap and is a good approximation to the actual Hessian is constructed to speed up the convergence of the quasi-Newton methods since the evaluation of the actual Hessian is considered as impractical and costly. For this purpose, a diagonal updating matrix has been derived to replace the identity matrix in approximating the initial inverse Hessian. The numerical results show that the preconditioned SQN method performs better than the standard SQN method that without preconditioning [ABSTRACT FROM AUTHOR]