Size optimization of space trusses using Big Bang–Big Crunch algorithm

A Hybrid Big Bang–Big Crunch (HBB–BC) optimization algorithm is employed for optimal design of truss structures. HBB–BC is compared to Big Bang–Big Crunch (BB–BC) method and other optimization methods including Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization and Harmony Search. Numerical results demonstrate the efficiency and robustness of the HBB–BC method compared to other heuristic algorithms.     

On-line optimization of the Tennessee Eastman challenge problem

On-line, optimal steady state operating conditions were determined for the six modes of operation (with and without disturbances) for the industrial recycle reactor problem proposed by Downs and Vogel (J.J. Downs, E.F. Vogel, A plant-wide industrial process control problem, Computers and Chemical Engineering 17 (1993) 245–255.). The optimization and modeling problem was formulated as a sequential solution of steady state material balances through the process. Optimal operating setpoints were obtained using NPSOL (P.E. Gill et al., User's Guide for NPSOL: A FORTRAN Package for Nonlinear Programming, Technical Report SOL86-2, Stanford University, CA, 1986.) and reactor feed material balances as equality, non-linear constraints. On-line optimization results, without disturbances, compared favorably with the results obtained by Ricker (N.L. Ricker, Optimal steady-state operation of the Tennessee Eastman challenge process, Computers and Chemical Engineering 19 (1995) 949–959.). The optimization objective function tended to be broad and flat around the optimal operating conditions for all six modes of operation. The on-line, steady state, optimization algorithm compared favorably with the more complicated optimization structure designed by Yan (M. Yan, N. L. Ricker, On-line optimization of the Tennessee Eastman challenge process, in: Proceedings of the 1997 American Control Conference.). However, the algorithm presented here required less computational effort and exhibited greater convergence reliability than the work of Yan. On-line optimization was performed every 8 h and required less than 5 min calculation time. Updated model parameters were calculated every minute and filtered using a first order filter with 15 min time constant. Net profit was introduced as a tool to compare economic performance of the plant operating under a knowledgeable operator and operating under an off-line/on-line optimization algorithm for all six modes of operation. For modes 1–3, operating at the setpoints generated by the optimization algorithm provided significant increases in production rate and net profit that amounted to a 16–45% decrease in product operating costs when compared to operation of the plant at the setpoints specified by an operator. By decreasing operating costs and increasing production rate while maintaining a specified G/H ratio, the optimization algorithm increased net profit by 9.3–0.5% for modes 4–6, respectively, when compared to knowledgeable operator optimization of the plant. Also for sustained disturbances, the optimization algorithm decreased the error in the desired G/H ratio and increased process stability when compared to knowledgeable operator optimization of the plant. On-line optimization provided a maximum 1% relative increase in production rate and 1.5% relative increase in net profit compared to off-line optimization for modes 4–6 only when certain sustained, disturbances occurred. The economic justification of on-line optimization over off-line optimization depends upon the type, magnitude, and frequency of the disturbances.     

A Nonreducible System of Constraints of a Combinatorial Polyhedron in a Linear-Fractional Optimization Problem on Arrangements

A system of linear constraints is investigated. The system describes the domain of feasible solutions of a linear optimization problem to which a linear-fractional optimization problem on arrangements is reduced. A system of nonreducible constraints of a polyhedrom is established for the linear-fractional optimization problem on arrangements.__________Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 107–116, March–April 2005.     

Stochastic quasigradient method for optimizing the parameters of dynamic systems

Two-stage stochastic optimization of dynamic system parameters is considered. It is shown that the original optimization problem can be approximated with any degree of accuracy by a finite-dimensional minimax stochastic problem. An Arrow—Hurwicz type stochastic quasigradient algorithm is proposed for solving such problems.Translated from Kibernetika, No. 4, pp. 104–107, 130, July–August, 1990.     

Extremal problems with rare events. I

The paper considers optimization of systems with rare events causing large damage. A number of propositions are stated concerning uniform convergence of stochastic iterative optimization algorithms and some numerical results are considered.Translated from Kibernetika, No. 5, pp. 55–58, September–October, 1990.     

Two-point mid-range approximation enhanced recursive quadratic programming method

This research represents an attempt to combine good convergence properties of recursive quadratic programming methods with the benefits of mid-range approximations, initially developed in the field of structural optimization. In this paper, an optimization method based on Arora and coworkers PLBA (Pshenichny–Lim–Belegundu–Arora) algorithm is proposed in which, during the line search phase, cost and constraint functions are substituted by their two-point approximations using the Generalized Convex Approximation formulae of Chickermane and Gea. The results showed that the proposed optimization method preserves the reliability and accuracy of the recursive quadratic programming method while it might simultaneously reduce the computational effort for some problems. Therefore, the proposed optimization method may be taken as potentially suitable for general design optimization purposes.     

Conceptual design of aeroelastic structures by topology optimization

A topology optimization methodology is presented for the conceptual design of aeroelastic structures accounting for the fluid–structure interaction. The geometrical layout of the internal structure, such as the layout of stiffeners in a wing, is optimized by material topology optimization. The topology of the wet surface, that is, the fluid–structure interface, is not varied. The key components of the proposed methodology are a Sequential Augmented Lagrangian method for solving the resulting large-scale parameter optimization problem, a staggered procedure for computing the steady-state solution of the underlying nonlinear aeroelastic analysis problem, and an analytical adjoint method for evaluating the coupled aeroelastic sensitivities. The fluid–structure interaction problem is modeled by a three-field formulation that couples the structural displacements, the flow field, and the motion of the fluid mesh. The structural response is simulated by a three-dimensional finite element method, and the aerodynamic loads are predicted by a three-dimensional finite volume discretization of a nonlinear Euler flow. The proposed methodology is illustrated by the conceptual design of wing structures. The optimization results show the significant influence of the design dependency of the loads on the optimal layout of flexible structures when compared with results that assume a constant aerodynamic load.     

Optimization of mealy automaton logic using programmable logic arrays

Optimization of a two-level microprogrammed automaton is considered. The optimization method is based on equivalent transformation of the automaton, grouping the automaton states into equivalence classes with special encoding.Translated from Kibernetika i Sistemnyi Analiz, No. 5, pp. 180–184, September–October, 1991.     

Solution of Euclidean combinatorial optimization problems by the method of construction of a lexicographic equivalence

A method of space partition based on an equivalence relation is considered. Based on a lexicographic exhaustive search for equivalence classes, algorithms are developed for solution of a new class of optimization problems, namely, linear conditional Euclidean problems of lexicographic combinatorial optimization.Translated from Kibernetika i Sistemnyi Analiz, No. 5, pp. 155–125, September–October 2004.     

Large clustering problems in a fuzzy setting

The clustering of a large discrete set of objects is considered as a combinatorial optimization problem in a fuzzy setting.Translated from Kibernetika, No. 1, pp. 116–121, January–February, 1991.     

Necessary and sufficient condition of extremum for the Lebesgue-Stieltjes integral on a class of distributions

We consider the optimization problem for the Lebesgue-Stieltjes integral defined on the set of distribution functions with two fixed power moments. The Krein-Nudel'man theorem on the necessary and sufficient condition of extremum of this functional is generalized to the case of a piecewise-continuous integrand.Translated from Kibernetika, No. 1, pp. 72–75, January–February, 1990.     

Optimization in periodic Markovian decision processes

We consider a Markovian decision process with a nonhomogeneous transition function satisfying a periodicity condition. An optimization method is proposed which computes the optimal periodic strategy for an unbounded time interval.Translated from Kibernetika, No. 2, pp. 91–94, 99, March–April, 1991.     

Renewal processes with Markovian increments

The paper considers the computation of nonstationary characteristics of renewal processes with Markovian increments by analytical-statistical methods and determines the optimal characteristics of these processes by stochastic optimization methods.Translated from Kibernetika, No. 2, pp. 86–90, 100, March–April, 1989.     

Disnel: An application package for solving discrete and nonlinear optimization problems

The paper describes the features of the DISNEL package for interactive solution of a wide range of discrete and nonlinear optimization problems of ES computers.Translated from Kibernetika, No. 3, pp. 36–45, May–June, 1991.     

Discrete approximations and optimal solutions of differential inclusions

The paper considers optimization of differential inclusions and convergence of discrete approximations. Necessary conditions of optimality are derived in terms of normal cones and Mordukhovich derivatives.Translated from Kibernetika, No. 1, pp. 76–79, 98, January–February, 1991.     

An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis

Clustering is a popular data analysis and data mining technique. A popular technique for clustering is based on k-means such that the data is partitioned into K clusters. However, the k-means algorithm highly depends on the initial state and converges to local optimum solution. This paper presents a new hybrid evolutionary algorithm to solve nonlinear partitional clustering problem. The proposed hybrid evolutionary algorithm is the combination of FAPSO (fuzzy adaptive particle swarm optimization), ACO (ant colony optimization) and k-means algorithms, called FAPSO-ACO–K, which can find better cluster partition. The performance of the proposed algorithm is evaluated through several benchmark data sets. The simulation results show that the performance of the proposed algorithm is better than other algorithms such as PSO, ACO, simulated annealing (SA), combination of PSO and SA (PSO–SA), combination of ACO and SA (ACO–SA), combination of PSO and ACO (PSO–ACO), genetic algorithm (GA), Tabu search (TS), honey bee mating optimization (HBMO) and k-means for partitional clustering problem.     

A distributed PSO–SVM hybrid system with feature selection and parameter optimization

This study proposed a novel PSO–SVM model that hybridized the particle swarm optimization (PSO) and support vector machines (SVM) to improve the classification accuracy with a small and appropriate feature subset. This optimization mechanism combined the discrete PSO with the continuous-valued PSO to simultaneously optimize the input feature subset selection and the SVM kernel parameter setting. The hybrid PSO–SVM data mining system was implemented via a distributed architecture using the web service technology to reduce the computational time. In a heterogeneous computing environment, the PSO optimization was performed on the application server and the SVM model was trained on the client (agent) computer. The experimental results showed the proposed approach can correctly select the discriminating input features and also achieve high classification accuracy.     

Program reduction allowing for additional specifications

An optimization method for programs with procedures is proposed. The method is based on invariant assertions for program variables, which are viewed as the context of procedure call operators.Translated from Kibernetika i Sistemnyi Analiz, No. 5, pp. 57–67, September–October, 1991.     

Program generation in a computer-aided research system for optimization of geometrical-design problems

The paper considers some features of optimization of geometrical-design problems, including solution algorithms, organization and functional principles of a computer-aided research system for these problems, and approaches to computation planning and program generation in this system.Translated from Kibernetika, No. 6, pp. 43–44, November–December, 1989.     

Quadratic optimization on combinatorial sets in Rn

We consider the optimization of quadratic functions on the set of permutations in Rⁿ. Extremal properties of these functions are studied. Branch-and-bound methods are proposed for the solution of these optimization problems.Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 97–104, July–August, 1991.