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Title of Journal: J Comb Optim

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Abbravation: Journal of Combinatorial Optimization

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Kluwer Academic Publishers

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DOI

10.1007/bf01398429

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ISSN

1573-2886

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Penalty guided genetic search for redundancy optim

Authors: Rashika Gupta Manju Agarwal
Publish Date: 2006/08/14
Volume: 12, Issue: 3, Pages: 257-
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Abstract

This paper presents a genetic algorithm GA for parallel redundancy optimization in seriesparallel power systems exhibiting multistate behavior optimizing the reliability subject to constraints The components are binary and chosen from a list of products available in the market and are being characterized by their feeding capacity reliability cost and weight System reliability is defined as the ability to satisfy consumer demand and is presented as a piecewise cumulative load curveIn GA to handle infeasible solutions penalty strategies are used Penalty technique keep a certain amount of infeasible solutions in each generation so as to enforce genetic search towards an optimal solution from sides of both feasible and infeasible regions We here present a dynamic adaptive penalty function which helps the algorithm to search efficiently for optimal/near optimal solution To evaluate system reliability a fast procedure based on universal generating function is used An example considering a multistate seriesparallel power system is solved considering both homogeneous and heterogeneous types of redundancy Also an example considering price discounts is solved The effectiveness of the penalty function and the proposed algorithm is studied and shown graphically

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  11. Evaluation performance of genetic algorithm and tabu search algorithm for solving the Max-RWA problem in all-optical networks
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