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This article first introduces the basic principle and model of ant colony algorithm which is a kind of swarm intelligence algorithm, then analyzes the defect in large-scale optimization problems of basic ant colony algorithm, and concludes with an improved algorithm to improve execution efficiency and solution quality in solving large-scale problems.
Airline seat allocation problem has been studied by many researchers. In this paper, we develop an airline seat allocation model with multiple bookings considering the uncertain passenger demand. Using the idea of robust optimization, we assume that the probability of every booking request from a certain fare class distributing in a symmetrical interval. And the total deviation of booking requests...
Automotive steering mechanism is designed applying a theoretical method of robust optimization. A math modal on steering mechanism with clearances based on robust optimization is established. The movement precision in fourteen positions of steering mechanism during steering course is chosen as objective function. Distance between kingpins, axle-base, the bottom angle of trapezoid mechanism and the...
A math model on planetary gear is built based on deterministic optimization method. The minimum volume of planetary gear is chosen as optimization objective. Teeth number of sun gear, face width, module and gear number are chosen as design variable. Applying Monte Carlo method robustness on the planetary gear is analyzed. Robustness analysis is included in probability distribution of objective and...
To simplify the design and realization of controller, the method of robust H∞ static output feedback is discussed in this paper. A sufficient condition that satisfies given performance index is presented for constrained system, and an H∞ static output feedback control design method is proposed. By converting the constrained conditions and the elliptical set that contains output trajectory into a set...
This paper presents a kernel-based fuzzy c-means algorithm with partition index maximization, called KPIM algorithm. The proposed KPIM algorithm is more robust than the partition index maximization algorithm proposed by Özdemir and Akarum. Experiments show that the advantage of KPIM are robust properties: (1) robust to fuzziness parameter m, (2) robust to outlier, (3) robust to image artifacts; and...
Based on mean-semivariance theory, a robust optimization model is developed for China's social security fund asset allocation, which the uncertainty of expected return and risk on security fund investment is described by several expected return vectors and covariance matrices. Then, we have done an empirical analysis by using the actual data from China. Empirical results show that: this method is...
Aiming at the characteristics such as multivariable, strong coupling, nonlinear and time-varying parameters for the coordinated control system of unit power plant, a single-nerve cell network combined with PID control method was presented. A modified mutative scale chaotic optimization algorithm was proposed for the use of tuning the weight parameters of neural network and PID parameters. The new...
We present a general framework for tracking image regions in two views simultaneously based on sum-of-squared differences (SSD) minimization. Our method allows for motion models up to affine transformations. Contrary to earlier approaches, we incorporate the well-known epipolar constraints directly into the SSD optimization process. Since the epipolar geometry can be computed from the image directly,...
In real life optimization problems, it is very important to have high quality solutions (optimal). But when uncertainty becomes part of the optimization problem, solutions should be optimal and robust to the uncertain environmental changes. This paper focuses on finding robust optimal solution for the vehicle routing problem with stochastic demands VRPSD. In this case when the uncertainty of the customers...
The design and application of termination criteria has become an important aspect in evolutionary multi-objective optimization. Online convergence detection (OCD) determines when further generations are no longer promising based on statistical tests on a set of performance indicators. The behavior of OCD mainly depends on two parameters, the number of preceding generations considered in the statistical...
This paper presents a study for using Kriging metamodeling in combination with Covariance Matrix Adaptation Evolution Strategies (CMA-ES) to find robust solutions. A general, archive based, framework is proposed for integrating Kriging within CMA-ES, including a method to utilize the covariance matrix of the CMA-ES in a straightforward way to improve the accuracy of the Kriging predictions without...
In this study, the model-based fault detection and isolation (FDI) approach of parity-space is adapted to the diagnosis of sensor faults in power systems. Hardware redundancy is conventionally utilized to overcome this problem. However, this is an expensive solution. Instead, we propose to detect and locate faults by the systematic use of the system's analytical redundancy, with a global view of the...
In this paper, the Uncertain CARP (UCARP) is investigated. In UCARP, the demands of tasks and the deadheading costs of edges are stochastic and one has to design a robust solution for all possible environments. A problem model and a robustness measure for solutions are defined according to the requirements in reality. Three benchmark sets with uncertain parameters are generated by extending existing...
We present basic ideas related to application of Dominance-based Rough Set Approach (DRSA) in interactive Evolutionary Multiobjective Optimization (EMO). In the proposed methodology, the preference information elicited by the decision maker in successive iterations consists in sorting some solutions in the current population into “relatively good” and “others”, or in comparing some pairs of solutions...
The goal of multimodal optimisation is to identify multiple desirable optima of a fitness landscape within a single run of an evolutionary algorithm. Typically, one must resort to niching methods to perform this task, and such methods often require the use of a niche radius to distinguish between optima. Typically, this niche radius is difficult to set, leading to suboptimal performance of niching...
The paper presents a Predication mode based Routing Algorithm based on ACO (PRACO) to achieve the energy-aware data-gathering routing structure in wireless sensor networks (WSN). We adopt series model ARMA to analyze dynamic tendency in data traffic and deduce the construction of load factor, which can help to reveal the future energy status of sensor in WSN. By checking the load factor in heuristic...
Dynamic optimization problems (DOPs) are those whose specifications change over time during the optimization, resulting in continuously moving optima. Most research work on DOPs is based on the assumption that the goal of addressing DOPs is to track the moving optima. In this paper, we first point out the practical limitations on tracking the moving optima. We then propose to find optimal solutions...
This paper presents a generic machine learning based approach to devise performance assessment functions for any kind of optimization problem. The need of a performance assessment process taking into account robustness of the solutions is stressed and a general methodology for devising a function to estimate such a performance on any given engineering problem is formalized. This methodology is used...
Robust wideband beamforming with a real-time array testbed will be studied in this paper. For the practical consideration of uncertainty, channel impulse responses or channel transform functions of RF chains can not be obtained exactly due to the limitation of sounding and calibration procedure. Thus robust optimization will be applied to wideband beamforming. It is assumed that the uncertainty will...
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