This paper develops an efficient heuristic to solve the non-homogeneous redundancy allocation problem for multi-state series-parallel systems. Non identical components can be used in parallel to improve the system availability by providing redundancy in subsystems. Multiple component choices are available for each subsystem. The components are binary and chosen from a list of products available on the market, and are characterized in terms of their cost, performance and availability. The objective is to determine the minimal-cost series-parallel system structure subject to a multi-state availability constraint. System availability is represented by a multi-state availability function, which extends the binary-state availability. This function is defined as the ability to satisfy consumer demand that is represented as a piecewise cumulative load curve. A fast procedure is used, based on universal generating function, to evaluate the multi-state system availability. The proposed heuristic approach is based on a combination of space partitioning, genetic algorithms (GA) and tabu search (TS). After dividing the search space into a set of disjoint subsets, this approach uses GA to select the subspaces, and applies TS to each selected sub-M. Ouzineb · M. Nourelfath · M. Gendreau 2 M. Ouzineb et al.space. The design problem, solved in this study, has been previously analyzed using GA. Numerical results for the test problems from previous research are reported, and larger test problems are randomly generated. These results show that the proposed approach is efficient both in terms of both of solution quality and computational time, as compared to existing approaches.
This paper uses a new tabu search meta-heuristic optimization method to solve the redundancy allocation problem for multi-state series-parallel systems. The total search space is divided into a set of disjoint subsets. The proposed tabu search heuristic determines the minimal cost system configuration under availability constraints. A universal generating function technique is applied to evaluate system availability. The algorithm was implemented and illustrated through numerical examples.
Hidden Markov Models (HMM) are used in a wide range of artifificial intelligence applications including speech recognition, computer vision, computational biology and fifinance. Estimating an HMM parameters is often addressed via the Baum-Welch algorithm (BWA), but this algorithm tends to convergence to local optimum of the model parameters. Therefore, optimizing HMM parameters remains a crucial and challenging work. In this paper, a Variable Neighborhood Search (VNS) combined with Baum-Welch algorithm (VNS-BWA) is proposed. The idea is to use VNS to escape from local minima, enable greater exploration of the search space, and enhance the learning capability of HMMs models. The proposed algorithm has entire advantage of combination of the search mechanism in VNS algorithm for training with no gradient information, and the BWA algorithm that utilizes this kind of knowledge. The performance of the proposed method is validated on a real dataset. The results show that the VNS-BWA has better performance fifinding the optimal parameters of HMM models, enhancing its learning capability and classifification performance.
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