Robot routing using clustering-based parallel genetic algorithm with migration

Chiu, Ko-Ming; Liu, Jing‐Sin

doi:10.1109/mfcist.2011.5949511

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…A clustering algorithm based on common intersection area of SNs is proposed in [7], it groups the nodes having common intersection are in a cluster. Order of to be visited by ME is obtained by using GA. A genetic algorithm based cluster was proposed for reducing latency in [12].…”

Section: Clusteringmentioning

confidence: 99%

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A Cluster–based Approach for Minimizing Energy Consumption by Reducing Travel Time of Mobile Element in WSN

Prashanth

Nandury

2020

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Envoy Node Identification (ENI) and Halting Location Identifier (HLI) algorithms have been developed to reduce the travel time of Mobile Element (ME) by determining Optimal Path(OP) in Wireless Sensor Networks. Data generated by cluster members will be aggregated at the Cluster Head (CH) identified by ENI for onward transmission to the ME and it likewise decides an ideal path for ME by interfacing all CH/Envoy Nodes (EN). In order to reduce the tour length (TL) further HLI determines finest number of Halting Locations that cover all ENs by taking transmission range of CH/ENs into consideration. Impact of ENI and HLI on energy consumption and travel time of ME have been examined through simulations.

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Section: Clusteringmentioning

confidence: 99%

“…In literature various approaches makes use of heuristic approaches like Travelling Salesmen Problem (TSP) and Genetic Algorithms (GA) for defining ME's optimal tour [7,12,20].…”

Section: Introductionmentioning

confidence: 99%

A Cluster–based Approach for Minimizing Energy Consumption by Reducing Travel Time of Mobile Element in WSN

Prashanth

Nandury

2020

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…A bunch of studies has focused on the parallel genetic algorithms to increase the speed of the convergence as well as appropriate management of huge amounts of data and heavy computations [2][3][4][5]. However, a group of researchers has concentrated on the hybrid approaches to overcome the limitations of simple GA.…”

Section: Introductionmentioning

confidence: 99%

A New Genetic-Based Approach for Function Optimization

Rabiee

Vali

2014

AMR

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We present a novel genetic-based algorithm for optimizing n-D simple-bounded continuous functions. In this paper, we propose a new mutation operator, called rotational mutation. The proposed approach starts from the vertices of the polytope created by the simple bounds, as the initial population. Similar to the conventional genetic algorithm, we calculate the optimum point of each population based on its cost value using the elitism mechanism. Then, we create the new generations based on the proposed rotational mutation and the conventional crossover operators. We have evaluated the algorithm on the two well-known test problems. Experimental results showed that the proposed approach outperforms the conventional genetic algorithm, in terms of the number of generations.

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