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This paper introduces a hybrid multilevel inverter (MLI) with reduced switch count, which can generate higher output voltage level with minimum number of DC input sources. The operation of this proposed MLI is carried out with unequal DC sources to achieve the desired output voltage level. The reduced MLI output voltage is set to minimal total harmonic distortion (THD) with the help of modified JAYA (MJAYA) algorithm. A JAYA algorithm with improved steps by adopting an accelerating parameter has been proposed in this research work to obtain a faster convergence of the objective function. The MJAYA algorithm has provided the suitable switching angles for the proposed three-phase 15-level MLI and reduced the output voltage THD to 2.23%, which satisfies the standard set by IEEE-519. To prove the efficiency of this proposed modified algorithm, the comparative analysis is carried out through MATLAB program and Simulink tool using common JAYA and modified particle swarm optimisation algorithms. The performance and productivity of the proposed MLI have been investigated through simulation and experimental setups. [ABSTRACT FROM AUTHOR]

Systems, generally being nonlinear behaves unexpectedly due to external disturbances. Therefore, operation controllers are needed in tracking, chemical engineering, electrical engineering, and other areas. For continuing quality and productivity of industrial firms, the deployment of industrial robots is increasing. Different structures of fuzzy-Proportional Integral Derivative based trajectories for robot manipulators are designed, for various regular and irregular trajectories. Industrial robot's performance is compared by Proportional Integral Derivative and Fuzzy- Proportional Integral Derivative controller. For tracking the peak power in a Photo Voltaic system, a nonlinear proportional–integral–derivative controller is applied for rapidly changing environmental conditions, particle swarm optimization based Nonlinear-PID, Maximum Power Point Tracking method offers a clean response for Maximum Power Point Tracking. To maintain constant frequency and power variations in power systems the freedom of a three-degree Proportional Integral Derivative controller was designed. For excitation control in Automatic Voltage Regulators, Proportional Integral Derivative and Proportional Integral Derivative -Acceleration controllers are used, Proportional Integral Derivative -Acceleration has more response speed and more stability and it has a good response time and response of frequency by implementing the Particle Swarms Optimization and Gravitational Search algorithm-based Proportional Integral Derivative controller Automatic Voltage Regulator's voltage response is effective. This paper deals with studying the behavior of various controllers and algorithms in practical applications, comparative analysis of magnetic levitation system response with different controllers and algorithms, and finding the performance of magnetic levitation systems by using a Particle Swarm Optimization algorithm-based Proportional Integral Derivative controller. [ABSTRACT FROM AUTHOR]

The gas centrifuge process is widely used in the world to separate binary and multi component mixtures of isotopes. The majority of a plant cost is related to the number of centrifuges in a cascade. The cascade should be built so that it uses the fewest possible centrifuges for a given product and waste concentrations. Minimizing the number of centrifuges and the total flow rates, is a key point in designing and optimizing isotope separation cascades. The main purpose of this paper is to present a novel swarm intelligence based algorithm to solve these kinds of problems. This novel algorithm, called the Horse Herd Optimization Algorithm (HOA), is inspired by the behavioral patterns of horses in their habitats. In this work, it is demonstrated that the suggested algorithm can solve complicated multidimensional problems. HOA is tested by some test functions of high-dimensions and the results are compared with the strongest available optimization algorithms. In next, the proposed algorithm is used to optimize gas centrifuge cascades for the separation of binary and multicomponent mixtures of isotopes in several cases. Also, ideal and optimum cascades are compared in different separating regimes of the stages, using this method. Considering the performance of HOA in solving multidimensional problems, this algorithm is proposed for the optimization of long cascades. [ABSTRACT FROM AUTHOR]

In this paper, the k-median of a graph is used to decompose the domain (mesh) of the continuous two- and three-dimensional finite element models. The problem of k-median is stated as an optimization problem and is solved by utilizing eight robust meta-heuristic algorithms. The Artificial Bee Colony algorithm (ABC), Cyclical Parthenogenesis algorithm (CPA), Cuckoo Search algorithm (CS), Teaching-Learning Based Optimization algorithm (TLBO), Tug of War Optimization algorithm (TWO), Water Evaporation Optimization algorithm (WEO), Ray Optimization algorithm (RO), and Vibrating Particles System algorithm (VPS) constitute the set of algorithms that are employed in the present study. In order to tune the parameters of the meta-heuristics, the Taguchi method is used. The efficiency and robustness of the algorithms are investigated through two- and three- dimensional finite element models. [ABSTRACT FROM AUTHOR]

The optimization problems are becoming more complicated, requiring new and efficient optimization techniques to solve them. Many bio-inspired meta-heuristic algorithms have emerged in the last decade to solve these complex problems as most of these algorithms may be trapped into local optima and could not effectively solve all types of optimization problems. Hence, researchers are still trying to develop new and better optimization algorithms. This paper introduces a novel biologically-based optimization algorithm called circulatory system-based optimization (CSBO). CSBO is modeled based on the function of the body's blood vessels with two distinctive circuits, i.e. pulmonary and systemic circuits. The proposed CSBO algorithm is tested on a wide variety of complex functions of the real world and validated with the standard meta-heuristic algorithms. The results indicate that the CSBO algorithm successfully achieves the optimal solutions and avoids local optima. Note that the source code of the CSBO algorithm is publicly available at . [ABSTRACT FROM AUTHOR]

One of the capable technologies in networking systems is the Multiple-Input Multiple-Output (MIMO) technology as it provides better system capacity and a high Bit Error Rate (BER). Still, the transmit antenna selection remains as the greatest issue. More transmit antennas are necessary to achieve maximum capacity, which leads to increased power consumption. Therefore, for solving these issues in the M-MIMO system, this paper introduces a novel Wolf Adapted Herding algorithm (WA-HA) for selecting the optimal transmit antennas using multi-objective constraints that raise the capacity and Energy Efficiency (EE). The proposed algorithm combines the concept of Elephant Herding Optimization (EHO) and Grey Wolf Optimization (GWO) algorithm and it is referred to as the Wolf Adapted Herding Algorithm (WA-HA). This WA-HA algorithm optimises the number of transmit antennas and hence decides which antenna has to be chosen. Finally, the performance of the adopted method is evaluated over other traditional methods like MV-GSA, SLnO, I-SLnO, EHO and GWO in terms of various metrics like EE and capacity. In particular, the capacity of the proposed WA-HA method for ZF under set-up 1 is 51.02%, 30.61%, 22.44%, 8.163% and 55.10% better than the existing models. Furthermore, the computational time of our model is 10.96%, 26.13%, 14.08%, 13.48% and 10.26% superior to that of the existing models, respectively. [ABSTRACT FROM AUTHOR]

If f is a function of interest, typically either a likelihood or posterior density function on a parameter space Θ, and θ ̂ is the MLE (maximum likelihood estimate) or MAP (maximum a posteriori estimate), it can be of interest to find the region of Θ where f (θ) or log f (θ) is nearly as large as f (θ ̂) or log f (θ ̂). Typical tools for working with f, such as optimizers and MCMC, can fail when f is multimodal or has a plateau. This paper describes an algorithm called WHIM – for function approximation WHere It Matters – that finds the region of Θ where f is large and that is guaranteed not to fail for f's arising from the large class of models described here, even when those f's are multimodal or have plateaus. WHIM was introduced in Lavine, Bray, and Hodges, where the focus was on linear mixed models with exactly two variances. Here, the focus is on the algorithm and what can be learned by finding the region where f (θ) is large, rather than finding just θ ̂. [ABSTRACT FROM AUTHOR]

The utilization of space for human activities has increased steadily over the past several decades. In parallel, space debris has also grown at an alarming rate, posing risks to space missions and establishing debris removal as an aspiration in the current space industry. These objects can orbit at arbitrary rotational speeds in different planes. In this paper, a model predictive control-based algorithm is developed for a target-chaser rendezvous situation to optimize fuel consumption while considering thruster constraints and memory usage. A performance comparison is conducted with several conventional controllers to validate the effectiveness of the algorithm using computer simulations. [ABSTRACT FROM AUTHOR]

Neighborhood delineation is increasingly relied upon in urban social science research to identify the most appropriate spatial unit. In problems of this type, the true number of neighborhoods (typically called the k parameter) is unknown and analysts often require algorithmic approaches to determine k endogenously. Existing approaches for neighborhood delineation that do not require pre-specification of a k-parameter, however, are either nonspatial or lead to noncontiguous or overlapping regions. In this paper, we propose the use of max-p-regions for neighborhood delineation so that the geographic space can be partitioned into a set of homogeneous and geographically contiguous neighborhoods. In addition, we developed a new efficient algorithm to address the computational challenges associated with solving the max-p-regions so that it can be applied for large-scale neighborhood delineation. This new algorithm is implemented in the open-source Python Spatial Analysis Library (PySAL). Computational experiments based on both simulated and realistic data sets are performed and the results demonstrate its effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, a soft computing based scheme is proposed for the model order reduction of single input-single output commensurate fractional-order (FO) systems. The fractional-order system is first converted into an integer order (IO) system. The reduced order model of the corresponding integer order system is then determined via big bang – big crunch (BBBC) optimization algorithm. Finally, the reduced IO transfer function is transferred back to its FO form through an inverse substitution. The proposed approach is substantiated by two numerical examples of different orders. The effectiveness of the technique is demonstrated by the comparison of two performance indices, i.e. integral square error (ISE) and H ∞ error and the response characteristics in time and frequency domain with the existing techniques from the literature. It is revealed that the response of the proposed BBBC based reduced order model is much closer to that of the original higher-order system. [ABSTRACT FROM AUTHOR]

In this paper, the Hager–Zhang (HZ) conjugate gradient (CG) algorithm is studied for large-scale smooth optimization problems. (i) Some results of the HZ CG method for smooth unconstrained optimization problems are given, and a modified HZ (MHZ) CG method is proposed; (ii) the HZ and MHZ CG methods for nonlinear equations are analysed, the global convergence is established and numerical results for large-scale nonlinear equation problems are reported (1,00,000 variables). [ABSTRACT FROM AUTHOR]

The K-means clustering method is a widely adopted clustering algorithm in data mining and pattern recognition, where the partitions are made by minimizing the total within group sum of squares based on a given set of variables. Weighted K-means clustering is an extension of the K-means method by assigning nonnegative weights to the set of variables. In this paper, we aim to obtain more meaningful and interpretable clusters by deriving the optimal variable weights for weighted K-means clustering. Specifically, we improve the weighted k-means clustering method by introducing a new algorithm to obtain the globally optimal variable weights based on the Karush-Kuhn-Tucker conditions. We present the mathematical formulation for the clustering problem, derive the structural properties of the optimal weights, and implement an recursive algorithm to calculate the optimal weights. Numerical examples on simulated and real data indicate that our method is superior in both clustering accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

Generation of circular holes with varying dimensions in diverse parts/products is one of the most common operations in any manufacturing industry. Determination of the optimal drill path sequence is an important problem in hole-drilling operation using CNC machines. This problem's structure is quite analogous to travelling salesman problem and hence, is NP-complete belonging to both NP and NP-hard complexity classes. Due to exponential increase in number of possible solutions when the number of holes to be drilled increases, various evolutionary algorithms are seemed to be the viable choices in solving this type of optimization problem. In this paper, an almost unexplored swarm-based algorithm, in the form of bat algorithm, is applied to determine the optimal path sequences for different layouts consisting of 5 × 5, 7 × 7, 9 × 9 and 11 × 11 matrices of holes, and a 14-hole benchmark model, taking into account machining time, machining cost and non-productive cost of the related drilling operation. [ABSTRACT FROM AUTHOR]

In this paper, the inverse scattering of an imperfectly conducting cylinder with corners is given by the Firefly Algorithm. The inverse problem is first transformed into a nonlinear optimization problem, and then solved by the Firefly Algorithm. To treat a target with corners, the target shape is modeled by the cubic spline interpolation. The Firefly Algorithm utilized in this study is a biologically inspired algorithm. It has been proved to be efficient with high success rate. Since the inverse scattering involves complicated computation, the Firefly Algorithm is a good candidate for reconstructing the target information. [ABSTRACT FROM AUTHOR]

In this paper we present an efficient optimization approach for the dynamic inflow control of electric transmission lines. For the description of transmission lines several mathematical models are available. Here, we focus on the telegraph network equations which consist of a coupled system of hyperbolic differential equations. The optimization is then to find the best possible energy input in terms of boundary control to satisfy given demands. To solve this problem we introduce space mapping techniques based on a first discretize – then optimize approach. The idea of space mapping is the consideration of two model hierarchies (fine and coarse scale), where the coarse scale model is less accurate but easy to solve. We explain how this method works in the framework of inflow control, design the corresponding optimization algorithm and present various computational results to show the numerical efficiency. [ABSTRACT FROM AUTHOR]

This paper provides a developed mathematical model to derive the optimal integrated lot sizing in a multi-level supply chain with imperfect quality products. The developed mathematical model has a bi-objective function, with conflicting goals, minimizing the chain inventory costs and maximizing the chain total profit aided to find optimum policy for integrated lot sizing. We further actualize the problem by assuming multiple stochastic constraints. The mathematical formulation of the problem is stochastic, nonlinear, and large. In this regard, the interior point algorithm that is developed as more effective algorithm with less iteration is used for solving the recent convex nonlinear model. Numerical example shows that the developed interior point method is an efficient method with excellent performance in terms of the quality of the solution. Also, Sensitivity analysis shows that the developed interior point method is an efficient method with excellent performance in terms of the quality of the solution. [ABSTRACT FROM PUBLISHER]

Deeper deposits become the future of mining as an increasing amount of shallow deposits are exploited. Consequently, the mining industry is being forced to extract minerals from deeper deposits using underground mining methods. This increased the demand for underground mine planning and optimization techniques. Stope boundary optimization is an important step of underground mine design and planning due to its direct impact on the economics of the project. It focuses on maximising the economic profit by selecting the best possible layout; by considering operational, geotechnical and physical constraints. There are a number of algorithms that have been developed for stope boundary optimization for the last two decades. In this paper, an existing underground mine is examined where sublevel stoping mining method is applied. Four algorithms are applied to this underground mine to investigate their applicability in underground mine planning and design process. The selected algorithms are Floating Stope, Maximum Value Neighbourhood, and two special applications which are developed by Sens and Topal and Sandanayake and Topal. The capabilities and limitations of these algorithms are evaluated and compared to each other. Then, they are compared with the stope optimization results from an existing underground mine. [ABSTRACT FROM PUBLISHER]

In this paper, we study the knapsack sharing problem (KSP), a variant of the well-known NP-hard single knapsack problem. We propose an exact constructive tree search that combines two complementary procedures: a reduction interval search and a branch and bound. The reduction search has three phases. The first phase applies a polynomial reduction strategy that decomposes the problem into a series of knapsack problems. The second phase is a size reduction strategy that makes the resolution more efficient. The third phase is an interval reduction search that identifies a set of optimal capacities characterizing the knapsack problems. Experimental results provide computational evidence of the better performance of the proposed exact algorithm in comparison to KSPs best exact algorithm, to Cplex and to KSPs latest heuristic approach. Furthermore, they emphasize the importance of the reduction strategies. [ABSTRACT FROM PUBLISHER]

Distribution centers, which are essential to our society, are becoming increasingly important. Although the efficiency of an AVS/RS (Autonomous vehicle storage and retrieval system) makes it a promising system, there is still much to be studied regarding its effective use and design. This paper proposes a method of designing AVS/RSs through a quantitative consideration of group constraint by utilizing probability theory. The proposed algorithm also deals with the problem of designing the sizes of several buffers, and considers the effect of load fluctuations, which are important for real-world operation, by use of queuing network theory. Effectiveness of the proposed algorithm is shown with simulations of various number of groups, those of various safety factors for fluctuations, and those of various evasion rates for buffer overflow. The design results are shown to be much different from those which do not consider those factors. The results prove that the proposed method provides an accurate quantitative assessment of the AVS/RS’s constraints. [ABSTRACT FROM AUTHOR]

This paper deals with the application of the global optimization methods (evolutionary algorithms) to the optimization and identification problems in laminates. The simple and hybrid laminates are considered. There are two aims of the paper: i) to find the optimum stacking sequence of the laminates and ii) to identify the material constants of the laminates. The modal analysis methods are used in both cases. The distributed version of the evolutionary algorithm is used to reduce the computation time. The finite element method software is employed to solve the direct boundary-value problem. [ABSTRACT FROM AUTHOR]

The current study is related to the issue of computer-aided reverse engineering. Although the proposed technique has been suggested and adopted for the image-based planar objects, it is extendable to the objects in three dimensions (3D) with some modifications. The objective of the present study is to provide a robust and automated solution and methodology for the applications of reverse engineering technology in manufacturing and other industries. By extracting nearly exact geometric data of the models, the key points (one may call them ‘corner points’) from the data are detected using an efficient technique. Then curve or surface models are fitted by using non-uniform rational B-splines (NURBS). In curve or surface fitting problems, the selection of appropriate parameters, in order to obtain an optimized shape design, is a well-known problem. For large data, this problem needs to be dealt with by optimization algorithms avoiding possible local optima and at the same time reaching the desired solution in an iterative fashion. Many evolutionary optimization techniques including genetic algorithm and simulated annealing have been applied to the problem. This paper presents an application of an evolutionary heuristic technique known as ‘simulated evolution’ (SimE) to the curve and surface fitting problems using NURBS. NURBS have become the standard for representing curves and surfaces. This paper presents a method for approximating NURBS using digitized data. Instead of using the classical optimization approach to solve the problem, a new method based on SimE is developed. The shape parameters, in the description of NURBS, have been targeted to be optimized in a best possible way. The paper describes the mapping scheme of the problem to SimE followed by the proposed algorithm outlines with the interesting results obtained. [ABSTRACT FROM AUTHOR]

This paper presents an adjoint method for the calculation of remote sensitivities in supersonic flow. The goal is to develop a set of discrete adjoint equations and their corresponding boundary conditions in order to quantify the influence of geometry modifications on the pressure distribution at an arbitrary location within the domain of interest. First, this paper presents the complete formulation and discretization of the discrete adjoint equations. The special treatment of the adjoint boundary condition to obtain remote sensitivities or sensitivities of pressure distributions at points remotely located from the wing surface are discussed. Secondly, we present results that demonstrate the application of the theory to a three-dimensional remote inverse design problem using a low sweep biconvex wing and a highly swept blunt leading edge wing. Lastly, we present results that establish the added benefit of using an objective function that contains the sum of the remote inverse and drag minimization cost functions. [ABSTRACT FROM AUTHOR]

A new algorithm for static damage detection of three- and two-dimensional frames is presented in this paper. This approach is based on the minimization of difference between the measured and analytical static displacements of frames. The damage detection problem is solved as a nonlinear constrained structural optimization. In this strategy, the global structural stiffness matrix is parameterized. To achieve the goal, a new technique based on the eigen decomposition of the local elemental stiffness matrix is suggested. Structural damage is modeled as a reduction in cross-sectional properties of the elements. It is assumed that the stiffness matrix of the structure is perturbed due to damage. Hence, the damaged structural stiffness matrix is presumed to be the sum of the stiffness matrix of the undamaged structure and the perturbation matrix. Consequently, the sum of these matrices should be inverted in each iteration. Instead of the common ways of inversion, Sherman–Morrison–Woodbury formula is employed. Prior to the outset of the optimization procedure, the number of design variables, the amount of reductions in cross-sectional properties, is decreased by introducing a new suggested technique. This scheme is based on the nodal equilibrium equations. To illustrate the robustness and efficiency of the authors’ algorithm, several numerical problems are solved. [ABSTRACT FROM PUBLISHER]

Variational segmentation models provide effective tools for image processing applications. Although existing models are continually refined to increase their capabilities, solution of such models is often a slow process, since fast methods are not immediately applicable to nonlinear problems. This paper presents an efficient multi-grid algorithm for solving the Chan–Vese model in three dimensions, generalizing our previous work on the topic in two dimensions, but this direct generalized method is low performance or unfeasible. So here, we first present two general smoothers for a nonlinear multi-grid method and then give our three new adaptive smoothers which can choose optimal a parameter of the smoothers automatically, also we analyse them using a local Fourier analysis and our theorem to inform how to obtain an optimal parameter and the best smoother selection. Finally, various advantages of our recommended algorithm are illustrated, using both synthetic and real images. [ABSTRACT FROM PUBLISHER]

Vector Quantization (VQ) is lossy data compression technique and has various applications. Key to VQ is a good codebook. Once the codebook size is fixed, then the mean square error (MSE) reaches a value, beyond which it cannot be reduced by using codebook generation algorithms. In this paper, we are proposing modified genetic algorithm giving the optimal value, but it depends on the initial selection of the codevectors. Hence, it takes extremely huge time to give optimal value. For demonstration, we have used codebooks obtained from Linde Buzo and Gray (LBG) and Kekre's Fast Codebook Generation (KFCG) algorithms. It is observed that the optimal error obtained from both LBG and KFCG is almost the same, indicating that they have converged to an optimal value. From the results, it is obvious that KFCG codebook gives slightly less minimized error as compared to LBG, indicating that KFCG codebook is closer to the optimum. The proposed method is general and can be applied to any clustering algorithm. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms, such as shuffled frog-leaping, are stochastic search methods that mimic natural biological evolution and/or the social behavior of species. Such algorithms have been developed to arrive at near-optimum solutions to complex and large-scale optimization problems which cannot be solved by gradient-based mathematical programming techniques. The shuffled frog-leaping algorithm draws its formulation from two other search techniques: the local search of the ‘particle swarm optimization’ technique; and the competitiveness mixing of information of the ‘shuffled complex evolution’ technique. A brief description of the original algorithm is presented along with a pseudocode and flowchart to facilitate its implementation. This paper then introduces a new search-acceleration parameter into the formulation of the original shuffled frog-leaping algorithm to create a modified form of the algorithm. A number of simulations are carried out for two continuous optimization problems (known as benchmark test problems) and two discrete optimization problems (large scale problems in the project management domain) to illustrate the positive impact of this new parameter on the performance of the shuffled frog-leaping algorithm. A range of ‘best’ usable values for the search-acceleration parameter is identified and discussed. [ABSTRACT FROM AUTHOR]

Simultaneous perturbation stochastic approximation (SPSA) is a gradient-based optimization method which has become popular since the 1990s. In contrast with standard numerical procedures, this method requires only a few cost function evaluations to obtain gradient information, and can therefore be advantageously applied when identifying a large number of unknown model parameters, as for instance in neural network models or first-principles models. In this paper, a first-order SPSA algorithm is introduced, which makes use of adaptive gain sequences, gradient smoothing and a step rejection procedure to enhance convergence and stability. The algorithm performance is illustrated with the estimation of the most-likely kinetic parameters and initial conditions of a bioprocess model describing the evolution of batch animal cell cultures. [ABSTRACT FROM AUTHOR]

For many years researchers and decision makers (DMs) faced with multicriteria shortest path problems (MSPPs) have resorted to reductions to the classical shortest path problem (SPP) by means of weighted linear combinations of the criteria. Algorithmic and approximation schemes are available to solve MSPPs but these approaches often display complexities prohibitive to their implementation on real‐world applications. This paper describes the development of an Evolutionary Algorithm (EA) approach to MSPPs on networks with multiple independent criteria. The EA approach is shown to sufficiently explore the underlying network space, generate large candidate path sets, and evolve high quality approximations to the optimal MSPP solution(s). Opportunities for early termination of the EA in time‐critical applications are also offered. Among the issues for further work is the integration of the EA as a tool within a GIS for path optimization. [ABSTRACT FROM AUTHOR]

The problem of finding a local or a global minimum of a real function on a set S⊆ n occurs in many real world problems. When f is a general nonlinear function, deterministic as well as stochastic algorithms have been proposed. The numerical cost of these algorithms, that can be the number of function evaluations or the number of elementary operations required when executed, has been investigated in the last few years. In this paper, we survey the main results by classifying them according to the field they refer to: local minimization, global minimization and checking the optimality conditions. Further, some results concerning the information that an algorithm may use, are surveyed. [ABSTRACT FROM AUTHOR]

Multistate searching methods are a subfield of distributed artificial intelligence that aims to provide both principles for construction of complex systems involving multiple states and mechanisms for coordination of independent agents' actions. This paper proposes a multistate searching algorithm with reinforcement learning for the automatic core design of a boiling water reactor. The characteristics of this algorithm are that the coupling structure and the coupling operation suitable for the assigned problem are assumed and an optimal solution is obtained by mutual interference in multistate transitions using multiagents. Calculations in an actual plant confirmed that the proposed algorithm increased the convergence ability of the optimization process. [ABSTRACT FROM AUTHOR]

This paper discusses the features of a new tool for engineering optimization problems. The new tool, Linearization Error Sequential Linear Programming (LESLP), includes an improved formulation of the well-established Sequential Linear Programming method. The LESLP algorithm is tested in seven structural optimization problems and two mathematical programming problems. The numerical efficiency, merits and limitations of LESLP are investigated in detail and compared with other general purpose optimization algorithms. Results indicate that LESLP is competitive with respect to other optimization algorithms published in the literature. [ABSTRACT FROM AUTHOR]

One of the most popular algorithms for solving minimax approximation problems is the Barrodale and Phillips simplex-based method [I. Barrodale and C. Phillips (1974). An improved algorithm for discrete Chebyshev linear approximation. In: Proceedings of the Fourth Manitoba Conference on Numerical Mathematics , pp. 177-190, University of Manitoba, Winnipeg, Canada.]. Our new research has found that the modified pivoting strategy in the first stage of the Barrodale and Phillips algorithm could result in an invalid starting point and system failure in later stages [D. Lei (2002). Robust and efficient algorithms for ℓ 1 and ℓ ∞ approximations. PhD Thesis, School of Computing and Engineering, University of Huddersfield, Huddersfield, UK.]. In contrast, a primal method that we propose in this paper is numerically more stable, and the approximating function does not suffer the effects of wild oscillation. However, on average, the primal method requires more searching steps to find an optimal solution. To improve the efficiency of the primal method, we have considered using the least squares solution to provide a better starting point. Experimental results show that if the least squares estimator is available, then it can speed up the convergence rate significantly in most cases. The number of steps required by using the least squares solution for the primal method also compares favourably with that of the dual method. [ABSTRACT FROM AUTHOR]

This paper describes the methodology and application of a genetic algorithm scheme tailor-made to EPANET, for optimizing the operation of a water distribution system under unsteady water quality conditions. The water distribution system consists of sources of different qualities, treatment facilities, tanks, pipes, control valves, and pumping stations. The objective is to minimize the total cost of pumping and treating the water for a selected operational time horizon, while delivering the consumers the required quantities at acceptable qualities and pressures. The decision variables for each of the time steps that encompass the total operational time horizon include: the scheduling of the pumping units, settings of the control valves, and treatment removal ratios at the treatment facilities. The constraints are: head and concentrations at the consumer nodes, maximum removal ratios at the treatment facilities, maximum allowable amounts of water withdrawals at the sources, and returning at the end of the operational time horizon to a prescribed total volume in the tanks. The model is explored through two example applications. [ABSTRACT FROM AUTHOR]

A common problem in a packet radio network (PRN) environment is to construct a multicasting network from a single source to a set of remote destinations which minimizes the number of transmissions. This problem is known to be NP-complete, thus computing an optimal solution may be infeasible for sizable networks. This paper provides two alternative solutions to this problem. The first is a heuristic algorithm which iteratively builds a spanning tree from the destinations to the source. A second solution, included for comparative purposes, is based on the Hopfield neural network whose dynamics are governed by a motion equation and a set of constraints. Both solutions are tested on a variety of instances against an optimal algorithm. Results show the approaches form good solutions (the number of transmissions is within about 3% of the optimum) and run in a fraction of the time required to form the optimal solution. [ABSTRACT FROM AUTHOR]

This paper explores the behaviour of the primary and secondary hot spots in homogeneous and two-dimensional inhomogeneous medium. Circular arrays are considered with a radius of up to five wavelengths. The number of sources and their positions in the array are varied, and the influence of these variations on the primary and secondary hot spots is observed. It is found that the primary hot spot reaches its final shape with the addition of a very small number of sources to the array. An increase in the number of sources results in a reduction of the normalized magnitude of the secondary hot spots, but the size of the normalized primary hot spot remains the same. An upper limit of sources in the array exists after which no further reduction of the secondary hot spots is observed. The finite-difference time-domain method (FDTD) is used to obtain the electric-field distribution in the inhomogeneous medium. A genetic algorithm is then applied to find the optimal positions of the antennae in the array. [ABSTRACT FROM AUTHOR]

SDP (SemiDefinite Programming) is one of the most attractive optimization models. It has many applications from various fields such as control theory, combinatorial and robust optimization, and quantum chemistry. The SDPA (SemiDefinite Programming Algorithm) is a software package for solving general SDPs based on primal--dual interior-point methods with the HRVW/KSH/M search direction. It is written in C with the help of LAPACK for numerical linear algebra for dense matrix computation. The purpose of this paper is to present a brief description of the latest version of the SDPA and its high performance for large scale problems through numerical experiments and comparisons with some other major software packages for general SDPs. [ABSTRACT FROM AUTHOR]