Double row layout problem (DRLP) involves identifying the exact locations of machines participating in a production task on two rows. There are typically multiple layouts with approximately optimal material handling c...
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Double row layout problem (DRLP) involves identifying the exact locations of machines participating in a production task on two rows. There are typically multiple layouts with approximately optimal material handling cost for a DRLP. These layouts often exhibit significantly different layout configurations. Identifying multiple global or local optimal layouts can provide layout designers with a wide range of options, which is of great significance for enhancing the maintainability, scalability, and customisability of the facility. However, most existing studies on DRLPs typically focus on designing a single optimal layout. In this paper, we study a multi-modal optimization of double row layout problem (MDRLP). A hybrid approach combing a fast niching memetic algorithm and linear programming (FNMA-LP) is proposed for MDRLP to locate multiple global or local optimal layouts with a similar quality. First, a fast niching memetic algorithm is developed to find a set of approximate optimal machine sequences. Then, LP is employed to optimise the exact locations of machines for each machine sequence. To evaluate the performance of the proposed algorithm, FNMA-LP is compared against three popular multi-modal algorithms and a state-of-the-art single-modal algorithm developed for DRLP. Experiments show that our approach outperforms competing approaches on almost all problem instances.
This paper introduces a new description-centric algorithm for web document clustering based on memeticalgorithms with niching Methods, Term-Document Matrix and Bayesian Information Criterion. The algorithm defines th...
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ISBN:
(纸本)9781424481262
This paper introduces a new description-centric algorithm for web document clustering based on memeticalgorithms with niching Methods, Term-Document Matrix and Bayesian Information Criterion. The algorithm defines the number of clusters automatically. The memeticalgorithm provides a combined global and local strategy for a search in the solution space and the niching methods to promote diversity in the population and prevent the population from converging too quickly (based on restricted competition replacement and restrictive mating). The memeticalgorithm uses the K-means algorithm to find the optimum value in a local search space. Bayesian Information Criterion is used as a fitness function, while FP-Growth is used to reduce the high dimensionality in the vocabulary. This resulting algorithm, called WDC-NMA, was tested with data sets based on Reuters-21578 and DMOZ, obtaining promising results (better precision results than a Singular Value Decomposition algorithm). Also, it was also then initially evaluated by a group of users.
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