Automatic seismic velocity analysis based on nonlinear optimization of the semblance function

Computing the maximal pose error given an upper bound on perturbations is challenging for parallel robots, mainly because the direct kinematic problem has several solutions, which become unstable near or at paral- lel singularities. In this paper, we propose a local unique- ness hypothesis that will allow safely computing pose error upper bounds using nonlinear optimization. This hypoth- esis, together with a corresponding maximal allowed per- turbation domain and a certified pose error upper bound valid over the complete workspace, will be proved numer- ically using a parametric version of Kantorovich theorem and certified nonlinear global optimization. We will then show how to synthesize tolerances reaching a prescribed maximal pose error over a workspace using approximate linearizations. This approximate tolerance synthesis will fi- nally be checked using the certified pose error upper bound we propose. Preliminary experiments on a RPRPR and a 3RPR with fixed orientation parallel manipulators are pre- sented.

We present a review of several professional software products that serve to analyze and solve nonlinear (global and local) optimization problems across a variety of hardware and software environments. The product versions discussed have been implemented for compiler platforms, spreadsheets, algebraic (optimization) modeling languages, and for integrated scientific-technical computing systems. The discussion highlights some of the key advantages of these implementations. Test examples, well-known numerical challenges and client applications illustrate the usage of the current software versions.

Recently, in structural engineering, many main elements in structural systems such as columns or girders are designed with variable cross-section for certain purposes. In addition, in order to determine the appropriate sizes or suitable shapes of structural elements for optimal volume of material under the effect of certain loading or with the given sizes of elements the structures can be subjected maximum loading, it is required to studied the optimization problem. This paper presents the non-linear weight optimization problem of frames with variable section stiffness of columns according to the strength, stiffness and stability criteria with the help of finite element method. The non-linear optimization problem is solved by the Genetic Algorithm. The calculation procedure of non-linear optimization of plane frames with variable section stiffness of columns is established and implemented using Matlab calculation programming software. From the results of numerical examples, the conclusions and recommendations will be proposed.

The paper reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including Firefly algorithm, PSO algorithms and ABC algorithm. By implementing these algorithms in Matlab, we will use worked examples to show how each algorithm works. Firefly algorithm is one of the evolutionary optimization algorithms, and is inspired by the flashing behaviour of fireflies in nature. There are many noisy non-linear mathematical optimization problems that can be effectively solved by Metaheuristic Algorithms. Mathematical optimization or programming is the study of such planning and design problems using mathematical tools. Nowadays, computer simulations become an indispensable tool for solving such optimization problems with various efficient search algorithms. Nature-inspired algorithms are among the most powerful algorithms for optimization. Firefly algorithm is one of the new metaheuristic algorithms for optimization problems. The algorithm is inspired by the flashing behaviour of fireflies. A Firefly Algorithm (FA) is a recent nature inspired optimization algorithm, which simulates the flash pattern and characteristics of fireflies. It is a powerful swarm intelligence algorithm inspired by the flash phenomenon of the fireflies. In this context, three types of meta-heuristics called Artificial bee Colony algorithm, Particle Swarm Optimization (PSO) and Firefly algorithms were devised to find optimal solutions of noisy non-linear continuous mathematical models. A series of computational experiments using each algorithm were conducted. The stimulation result of this experiment were analyzed and compared to the best solutions found so The Firefly algorithm in each noisy non linear optimization function seems to perform better and efficient.

This paper presents a novel optimized algorithm-interval evolution algorithm, which sufficiently exerts the advantage of the reliability of interval algorithm as well as the global convergence of evolution strategies such as group search. By providing us all global optimizers and the inclusion interval of optimizers, we conclude that the algorithm is equipped well in solving the global nonlinear constrained optimization problem. The experiment demonstrates that the algorithm is effective in its steadiness, reliability, fast convergence rate and high accuracy.

In the current scenario Economic Dispatch problem has taken a suitable twist as the public has become increasingly concerned with environmental matters. The dispatch of systems to minimise pollutants, as well as to achieve minimum cost are the two main constraints to be dealt with. In this paper, Global optimization techniques such as Honey Bee’s and Simulated Annealing techniques are employed together (HB-SA) to solve the Combined static Economic and Emission dispatch (CEED) problems in power system. This paper clearly is justified with the results, separately obtained for the above two techniques and is also provided with the results on applying both the algorithms separately. With practical consideration, Economic Dispatch will have non-smooth cost functions with equality and inequality constraints that make the problem, a large-scale highly constrained non linear optimization problem. As to demonstrate the effectiveness of the proposed method, it is subjected to the CEED problems considering (or) handling valve point loading effects. Comparison with other optimization techniques showed the superiority of the proposed HB-SA approach and confirmed its potential for solving nonlinear CEED problems.

This article, written by Technology Editor Dennis Denney, contains highlights of paper SPE 101933, "Improved and More- Rapid History Matching With a Nonlinear Proxy and Global Optimization," by A.S. Cullick, SPE, W.D. Johnson, SPE, and G. Shi, Landmark Graphics, prepared for the 2006 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 24–27 September. Generating reservoir-simulation models that match field production data has been a long-time industry challenge. Two workflows are presented for assisting history match. One workflow minimizes the misfit between simulated and historical data with a global optimizer. Another workflow trains a comprehensive nonlinear-proxy model with a small set of numerical simulations from experimental design to reduce the number of numerical simulations. Introduction History matching is, by its nature, an ill-posed optimization problem with many unknown reservoir parameters that could be adjusted to achieve a match against a relatively small amount of measured data at wells. The most common method of history match is to execute many simulations one at a time, changing a few parameters in a trial-and-error fashion. Often, a reservoir-simulation history match might require months of effort and many simulations to achieve a single model that neglects model nonuniqueness and often may not be a good predictor of future field performance. Many automated history-match algorithms have been investigated. Early assisted history matching emphasized deterministic gradient-optimization algorithms that require complete derivatives of the production response with respect to reservoir parameters. Subsequently, the emphasis evolved to faster methods to compute the sensitivity coefficients, such as streamline simulation, with emphasis on preserving uncertainty through the integration of geostatistics. Recent developments with adjoint models have renewed interest in sensitivity-based algorithms. However, none of these methods have been taken up widely by practitioners. With black-box optimization, the number of required simulations to achieve a match can be very large, numbered in the hundreds of simulations. If a single simulation model takes many hours to execute, there is good incentive to reduce the number of simulations for the optimization, even with the use of a distributed-computing system. This study used a global optimizer, while seeking to reduce the number of simulations with a fast proxy model. The use of proxy-regression models has been suggested to reduce the number of required simulations, but analytical-response models may not be sufficiently robust to represent the nonlinear reservoir production and pressure vs. time profile data. The use of neural networks as nonlinear-proxy models also has been suggested. In this paper, a history-match workflow is presented that has a robust, nonlinear neural-network proxy model to improve a history match and reduce the number of finite-difference simulations required.

The modeling and control of photovoltaic and other engineering systems highly depend on the accuracy of parameter identification. However, parameter extraction for photovoltaic equivalent models typically presents a high-dimensional, strongly nonlinear, and multimodal global optimization problem. Traditional analytical or gradient-based methods are sensitive to initial values and easily fall into local optima. To address this issue, this paper proposes a multi-strategy improvement teaching–learning-based optimization algorithm (SNTLBO). A social learning network structure with symmetric interaction topology is introduced into the classical TLBO framework to characterize the knowledge propagation relationships among individuals. Through this symmetric and balanced information exchange mechanism, learners can be guided not only by the teacher but also by multiple neighbors within the network, enabling more diverse and symmetric exploration of the search space and enhancing population diversity and global search capability. Furthermore, a teacher reputation mechanism is constructed, where historical performance is used to weight teacher influence, strengthening the guidance of high-quality solutions and accelerating convergence. Meanwhile, an adaptive teaching factor is designed to dynamically adjust the teaching intensity based on the distance between the teacher and students in the solution space, maintaining a dynamic balance (symmetry) between exploration and exploitation. To evaluate the performance of the proposed algorithm, SNTLBO is systematically compared with 11 advanced optimization algorithms on two benchmark test suites, CEC2017 (30D, 50D) and CEC2022 (10D, 20D). Non-parametric statistical tests are conducted to assess significance. The results demonstrate that SNTLBO shows competitive advantages in terms of convergence speed, solution accuracy, and stability. Finally, SNTLBO is applied to the parameter estimation of single-diode, double-diode, triple-diode, quadruple-diode, and photovoltaic module models. Experimental results show that the proposed algorithm achieves higher identification accuracy and robustness in terms of RMSE, IAE, and I–V/P–V curve fitting, verifying its effectiveness and practical value for complex global optimization and practical engineering applications.

STATISTICAL CONTROL OF OPTIMIZATION

An environment for general research into and prototyping of algorithms for reliable constrained and unconstrained global nonlinear optimization and reliable enclosure of all roots of nonlinear systems of equations, with or without inequality constraints, is being developed. This environment should be portable, easy to learn, use, and maintain, and sufficiently fast for some production work. The motivation, design principles, uses, and capabilities for this environment are outlined. The environment includes an interval data type, a symbolic form of automatic differentiation to obtain an internal representation for functions, a special technique to allow conditional branches with operator overloading and interval computations, and generic routines to give interval and noninterval function and derivative information. Some of these generic routines use a special version of the backward mode of automatic differentiation. The package also includes dynamic data structures for exhaustive search algorithms.

Voltage profile improvement and loss reduction is an important issue in power system especially under loaded condition. In this paper, two FACTS controllers like Static Var Compensator (SVC), and Thyristor Controlled Series Capacitor (TCSC) are considered for voltage profile improvement and loss reduction. The voltage profile improvement and loss reduction using FACTS devices is a problem of combinatorial non linear optimization. Small population based particle swarm optimization (SPPSO) has been used to solve this combinatorial non linear optimization problem. To enhance the diversity of particle in small population, mutation operator and concept of regeneration is used. The optimizations are performed on two parameters: the location and the values of SVC and TCSC. The proposed method is tested on modified IEEE 30 bus system for two operating conditions. To validate the accuracy of results a comparison with conventional particle swarm optimization (CPSO) has been made. The results show the effectiveness of SPPSO than CPSO in loss reduction and voltage profile improvement.

In this paper we consider a particular class of nonlinear optimization problems involving both continuous and discrete variables. The distinguishing feature of this class of nonlinear mixed variable optimization problems is that the structure and the number of variables of the problem depend on the values of some discrete variables. In particular, we define a general algorithm model for the solution of this class of problems, that draws inspiration from the approach recently proposed by Audet and Dennis [SIAM J. Optim., 11 (2001), pp. 573--594], and is based on the strategy of combining in a suitable way a local search with respect to the continuous variables and a local search with respect to the discrete variables. We prove global convergence of the algorithm model without specifying the local continuous search, but only identifying some reasonable requirements. Moreover, we define a particular derivative-free algorithm for solving mixed variable programming problems where the continuous variables are linearly constrained and derivative information is not available. Finally, we report numerical results obtained by the proposed algorithm in solving a real optimal design problem. These results show the effectiveness of the approach.

Since there were few chaotic neural networks applicable to the global optimization, in this paper, we propose a new neural network model — chaotic parameters disturbance annealing (CPDA) network, which is superior to other existing neural networks, genetic algorithms, and simulated annealing algorithms in global optimization. In the present CPDA network, we add some chaotic parameters in the energy function, which make the Hopfield neural network escape from the attraction of a local minimal solution and with the parameter annealing, our model will converge to the global optimal solutions quickly and steadily. The converge ability and other characters are also analyzed in this paper. The benchmark examples show the present CPDA neural network's merits in nonlinear global optimization.

Simultaneous measurement of flame temperature and species concentration distribution from nonlinear tomographic absorption spectroscopy

The paper deals with multiobjective nonlinear optimization problems with equality and inequality con- straints. In a corresponding dialogue procedure the decision maker has to determine in each step the aspira- tion and reservation level expressing his goals. This leads to a nonlinear optimization problem which can be solved by using a combination of pathfollowing methods and feasible descent direction methods (so- called jumps). The authors propose the application of a recently published pathfollowing method with jumps to multiobjective optimization problems, discuss its topological properties and present several computational results.