Tabu search and design optimization

Bland, J. A.; Dawson, G. P.

doi:10.1016/0010-4485(91)90089-f

Cited by 98 publications

(37 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, the efficient and flexible models based on optimization techniques were proposed (Naso et al, 2004;Zayed and Minkarah, 2004;Yan et al, 2008). In the past decade, various natural inspired algorithms were developed, such as Simulated Annealing (SA) (Aarts and Korst, 1989), Genetic Algorithm (GA) (Holland, 1975;Goldberg, 1989;Gen and Cheng, 1999), Tabu Search (TS) (Glover, 1989;1990;Bland and Dawson, 1991) algorithm and Particle Swarm Optimization (PSO) Eberhart and Kennedy, 1995), They are probabilistic heuristic algorithms and have been successfully used to address combinatorial optimization problem. These algorithms can provide better solution in comparison to classical algorithms.…”

Section: Methodsmentioning

confidence: 99%

Production Scheduling for Dispatching Ready Mixed Concrete Trucks Using Bee Colony Optimization

Srichandum¹

2010

American Journal of Engineering and Applied Sciences

View full text Add to dashboard Cite

Problem statement: This study proposed a systematic model of delivering Ready Mixed Concrete (RMC) that optimizes the schedule of dispatching RMC trucks. Approach: Firstly, the factors that impact the RMC delivery process are analyzed. Secondly, a model based on Bee Colony Optimization (BCO) was developed in order to find the best dispatching schedule that minimize the total waiting time of RMC trucks at construction sites. Results: To demonstrate its efficiency, the BCO algorithm was applied to solve two dispatching RMC problems. The results obtained from the BCO are compared to those achieved from the conventional approaches i.e., Genetic Algorithm (GA) and Tabu Search (TS) algorithm. Conclusion/Recommendations: The experimental results showed that the BCO approach can quickly generate efficient and flexible solutions to dispatch RMC trucks. Furthermore, the obtained results had higher quality solution efficiently and faster computational time than those obtained from the conventional approaches.

show abstract

Section: Methodsmentioning

confidence: 99%

Production Scheduling for Dispatching Ready Mixed Concrete Trucks Using Bee Colony Optimization

Srichandum¹

2010

American Journal of Engineering and Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In essence, the aspiration overrides the tabu list, in order to avoid missing the potential solutions. There are a number of different aspiration criteria used in the literature [21], [23]. The one we used in this study is to override the tabu list if the resulting solution yields lower value in Eq.…”

Section: Tabu Search (Ts)mentioning

confidence: 99%

Unsupervised Fingerprint Recognition

Tsai

Lin

Tseng

2013

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYThis study extends conventional fingerprint recognition from a supervised to an unsupervised framework. Instead of enrolling fingerprints from known persons to identify unknown fingerprints, our aim is to partition a collection of unknown fingerprints into clusters, so that each cluster consists of fingerprints from the same finger and the number of generated clusters equals the number of distinct fingers involved in the collection. Such an unsupervised framework is helpful to handle the situation where a collection of captured fingerprints are not from the enrolled people. The task of fingerprint clustering is formulated as a problem of minimizing the clustering errors characterized by the Rand index. We estimate the Rand index by computing the similarities between fingerprints and then apply a genetic algorithm to minimize the Rand index. Experiments conducted using the FVC2002 database show that the proposed fingerprint clustering method outperforms an intuitive method based on hierarchical agglomerative clustering. The experiments also show that the number of clusters determined by our system is close to the true number of distinct fingers involved in the collection.

show abstract

“…In this chapter, the size of TL is 3 × n (row × column), n is a number of neighborhoods around current solution. In the TL, the first column is used for storing the moves, the second column is the frequency of a move direction, and the last column is the recency (time to keep solutions) of a move (Bland & Dawson 1991).…”

Section: ) Tabu List Restrictionsmentioning

confidence: 99%

“…If appropriate aspiration criterion is satisfied, the move will be accepted in spite of the tabu classification. Roughly speaking, AC is designed to override tabu status if a move is 'good enough' (Bland & Dawson 1991).…”

Section: ) Aspiration Criterionmentioning

confidence: 99%