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A minimum weight design is developed for a composite laminated tube considering the number of plies as one of the design variables. The objective function is found to be complex, and more than one optimal design point may exist with different numbers of plies. Existing methods based on evolutionary algorithms tend to become trapped around a local optimum and can find no more than one optimal result per calculation. Aiming at the characteristics of the objective function, an improved evolutionary algorithm (INDE for short) is established based on niching technology. The formula for calculating the distance between individuals in the niching technology is improved to satisfy the minimum weight design for the composite laminated tube. As a result, the improved niching evolutionary algorithm offers better global search ability and can find more than one optimal result per calculation for different numbers of plies. [ABSTRACT FROM AUTHOR]

This paper defines a new procedure for optimising wind farm turbine placement by means of Monte Carlo simulation method. To verify the algorithm's accuracy, an experimental wind farmwas tested in a wind tunnel. On the basis of experimentalmeasurements, the error on wind farmpower output was less than 4%.Theoptimizationmaximises the energy production criterion; wind turbines' ground positionswere used as independent variables. Moreover, the mathematical model takes into account annual wind intensities and directions and wind turbine interaction. The optimization of a wind farm on a real site was carried out using measured wind data, dominant wind direction, and intensity data as inputs to run the Monte Carlo simulations. There were 30 turbines in the wind park, each rated at 20kW. This choice was based on wind farmeconomics. The site was proportionally divided into 100 square cells, taking into account a minimum windward and crosswind distance between the turbines. The results highlight that the dominant wind intensity factor tends to overestimate the annual energy production by about 8%.Thus, the proposed method leads to a more precise annual energy evaluation and to a more optimal placement of the wind turbines. [ABSTRACT FROM AUTHOR]

In order to improve network throughput and reduce energy consumption, we propose in this paper a cross-layer optimization design that is able to achieve multicast utility maximization and energy consumption minimization. The joint optimization of congestion control and power allocation is formulated to be a nonlinear nonconvex problem. Using dual decomposition, a distributed optimization algorithm is proposed to avoid the congestion by control flow rate at the source node and eliminate the bottleneck by allocating the power at the intermediate node. Simulation results show that the cross-layer algorithm can increase network performance, reduce the energy consumption of wireless nodes and prolong the network lifetime, while keeping network throughput basically unchanged. [ABSTRACT FROM AUTHOR]

This study proposes a traffic congestion minimization model in which the traffic signal setting optimization is performed through a combined simulation-optimizationmodel. In thismodel, the TRANSYT traffic simulation software is combined with Differential Evolution (DE) optimization algorithm, which is based on the natural selection paradigm. In this context, the EQuilibrium Network Design (EQND) problem is formulated as a bilevel programming problem in which the upper level is the minimization of the total network performance index. In the lower level, the traffic assignment problem, which represents the route choice behavior of the road users, is solved using the Path Flow Estimator (PFE) as a stochastic user equilibrium assessment. The solution of the bilevel EQNDproblemis carried out by the proposed Differential Evolution and TRANSYT with PFE, the so-called DETRANSPFE model, on a well-known signal controlled test network. Performance of the proposed model is compared to that of two previous works where the EQND problem has been solved by Genetic-Algorithms- (GAs-) and Harmony-Search- (HS-) based models. Results show that the DETRANSPFE model outperforms the GA- and HS-based models in terms of the network performance index and the computational time required. [ABSTRACT FROM AUTHOR]

Previous studies have shown that composite fibre orientations can be optimised for specific load cases such as longitudinal or inplane loading. However, the methodologies utilised in these studies cannot be used for general analysis of such problems. In this research, an extra termis added to the optimisation penalty function in order to consider the transverse shear effect. This modified penalty function leads to a new methodology whereby the thickness of laminated composite plate is minimized by optimising the fibre orientations for different load cases. Therefore, the effect of transverse shear forces is considered in this study. Simulated annealing (SA) is used to search for the optimal design. This optimisation algorithm has been shown to be reliable as it is not based on the starting point, and it can escape from the local optimum points. In this research, the Tsai-Wu failure and maximum stress criteria for composite laminate are chosen. By applying two failure criteria at the same time the results are more reliable. Experimentally generated results show a very good agreement with the numerical results, validating the simulated model used. Finally, to validate the methodology the numerical results are compared to the results of previous research with specific loading. [ABSTRACT FROM AUTHOR]

An optimization study was conducted on a centrifugal compressor. Eight design variables were chosen from the control points for the Bezier curves which widely influenced the geometric variation; four design variables were selected to optimize the flow passage between the hub and the shroud, and other four design variables were used to improve the performance of the impeller blade. As an optimization algorithm, an artificial neural network (ANN) was adopted. Initially, the design of experiments was applied to set up the initial data space of the ANN, which was improved during the optimization process using a genetic algorithm. If a result of the ANN reached a higher level, that result was re-calculated by computational fluid dynamics (CFD) and was applied to develop a new ANN. The prediction difference between the ANN and CFD was consequently less than 1% after the 6th generation. Using this optimization technique, the computational time for the optimization was greatly reduced and the accuracy of the optimization algorithm was increased. The efficiency was improved by 1.4% without losing the pressure ratio, and Pareto-optimal solutions of the efficiency versus the pressure ratio were obtained through the 21st generation. [ABSTRACT FROM AUTHOR]

A Canonic-Signed-Digit-(CSD-) coded genetic algorithm (GA) is proposed to find the optimal design of robustly stable infinite impulse response digital filter (IIR). Under the characteristics of the CSD structure, the circuit of the filter can be simplified and also the calculation speed can be raised to increase the hardware's efficiency. However, the design of CSD has a big challenge: the CSD structure of the system parameters will be destroyed by an optimal design procedure. To solve this problem, in this research a CSD-coded GA is proposed so that the CSD structure can be maintained. Moreover, the robustly stable IIR filters design problem is included in this paper. The robustness of the IIR filters is achieved by ensuring that all poles of the filters are located inside a disk D (α, r) contained in the unit circle, in which α is the center, r is the radius of the disk, and |α| r < 1. Consequently, in this paper, a new and more efficient D (α, r)-stability criterion will be derived and then embedded in GA for the design of robust IIR filters. It is worthwhile to note that to design an IIR filter simultaneously with CSD-structured parameters and robust stability is difficult and is not well explored so far. An example will be presented to show the efficiency of the proposed strategy for design of IIR filters. [ABSTRACT FROM AUTHOR]

We study the policy iteration algorithm (PIA) for continuous-time jump Markov decision processes in general state and action spaces. The corresponding transition rates are allowed to be unbounded, and the reward rates may have neither upper nor lower bounds. The criterion that we are concerned with is expected average reward. We propose a set of conditions under which we first establish the average reward optimality equation and present the PIA. Then under two slightly different sets of conditions we show that the PIA yields the optimal (maximum) reward, an average optimal stationary policy, and a solution to the average reward optimality equation. [ABSTRACT FROM AUTHOR]