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Larrañaga, Pedro; Kuijpers, Cindy M. H.; Murga, R.H.; Inza, Iñaki; Dizdarevic, S.

doi:10.1023/a:1006529012972

Cited by 654 publications

(112 citation statements)

References 29 publications

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“…A single point matching crossover method (SPMCM) is established to achieve the above conditions. The detailed principle of SPMCM is shown in Figure 3, and largely draws on partially-mapped crossover (PMX) (Larranaga et al 1999). First of all, randomly generate one crossover point in the parent chromosomes P and Q, and define the areas between starting point and crossover point as the matching areas, such as 7-1-3 of parent chromosome P and 1-7-8 of parent chromosome Q.…”

Section: Improved Genetic Algorithmmentioning

confidence: 99%

Research on Assembly Sequence Planning and Optimization of Precast Concrete Buildings

Wang

Yuan

Sun

2018

Journal of Civil Engineering and Management

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Abstract. Due to more complex structure and increasing prefabrication rate of precast concrete buildings, the assembly order between their constituent components is getting more and more attention. In order to solve the assembly sequence planning and optimization (ASPO) problem in precast concrete buildings, Building Information Modelling (BIM) and Improved Genetic Algorithm (IGA) are organically combined to propose a new method called BIM-IGA-based ASPO method. This method uses BIM for parametric modelling, uses IGA to search for an optimal assembly sequence, and then uses BIM again for visual simulation to further test the assembly sequence. Besides, IGA, which is improved in coding mode, crossover operation and mutation operation, is also used to achieve the dynamic adjustment of assembly sequence in construction process. A full-text example is used to explain the detailed operating principle of BIM-IGA-based ASPO method. The results indicate that the method can effectively find an optimal assembly sequence to reduce the assembly difficulty of a precast concrete building.

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Section: Improved Genetic Algorithmmentioning

confidence: 99%

Research on Assembly Sequence Planning and Optimization of Precast Concrete Buildings

Wang

Yuan

Sun

2018

Journal of Civil Engineering and Management

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“…The GA used for the planing is based on the pathrepresentation described in (Larrañaga et al (1999)), and uses the four mutations: Displacement, exchange, inversion, and insertion (Fogel (1993); Banzhaf (1990); Michalewicz (1999)). This GA does not guarantee to find the optimal solution nor to converge to it, however it will often converge on good candidate solutions.…”

Section: Automatic Planningmentioning

confidence: 99%

An Autonomous Robotic System for Mapping Weeds in Fields

Hansen

García-Ruiz

Kazmi

et al. 2013

IFAC Proceedings Volumes

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“…The total distance of a tour depends on a chosen starting city (Laporte, 1992). The 2-exchange swapping strategy randomly selects two cities in the tour and swaps them to get a new tour (Larranaga et al, 1999).…”

Section: Tour Construction and Representationmentioning

confidence: 99%

“…A tour can be represented in many ways, e.g., path representation, binary string representation, binary matrix representation, edge recombination crossover in binary representation (Larranaga et al, 1999). In this work, the binary string representation is selected to encode all tours for GPR input because it is simple and performs well when making test prediction.…”

Section: Tour Construction and Representationmentioning

confidence: 99%

“…In this work, the binary string representation is selected to encode all tours for GPR input because it is simple and performs well when making test prediction. Each city in a tour is encoded as a string of [log 2 n] bits and then a complete tour becomes a string of n[log 2 n] bits (Larranaga et al, 1999); that is, a tour 4→2→1→3 is represented by (011001000010).…”

Section: Tour Construction and Representationmentioning

confidence: 99%

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A Gaussian Process Regression Model for the Traveling Salesman Problem

Kongkaew

Pichitlamken

2012

Journal of Computer Science

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Problem statement: Traveling Salesman Problem (TSP) is a famous NP hard problem. Many approaches have been proposed up to date for solving TSP. We consider a TSP tour as a dependent variable and its corresponding distance as an independent variable. If a predictive function can be formulated from arbitrary sample tours, the optimal tour may be predicted from this function. Approach: In this study, a combined procedure of the Nearest Neighbor (NN) method, Gaussian Process Regression (GPR) and the iterated local search is proposed to solve a deterministic symmetric TSP with a single salesman. The first tour in the sample is constructed by the nearest neighbor algorithm and it is used to construct other tours by the random 2-exchange swap. These tours and their total distances are training data for a Gaussian process regression model. A GPR solution is further improved with the iterated 2-opt method. In the numerical experiments, our algorithm is tested on many TSP instances and it is compared with the Genetic Algorithm (GA) and the Simulated Annealing (SA) algorithm. Results: The proposed method can find good TSP tours within a reasonable computational time for a wide range of TSP test problems. In some cases, it outperforms GA and SA. Conclusion: Our proposed algorithm is promising for solving the TSP.

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Cited by 654 publications

References 29 publications

Research on Assembly Sequence Planning and Optimization of Precast Concrete Buildings

Research on Assembly Sequence Planning and Optimization of Precast Concrete Buildings

An Autonomous Robotic System for Mapping Weeds in Fields

A Gaussian Process Regression Model for the Traveling Salesman Problem

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