A Heuristic Genetic Algorithm for the Single Source Shortest Path Problem

Hasan, B.S.; Khamees, M.A.; Mahmoud, Ashraf

doi:10.1109/aiccsa.2007.370882

Cited by 20 publications

(12 citation statements)

References 8 publications

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“…Careful planning of their path can save on cost, time and operating expenses, ensure longer operational life and help prevent environmental fallouts. So, it is clear that more efficient methods for path design are required ( [14] [15] [16]).…”

Section: ) Consolidated Views That Includes Two Preceding Views Is Nmentioning

confidence: 99%

Application and Comparison of Analytical Hierarchy Process (AHP) and Network Methods in Path Finding of Pipeline Water Transmission System, from Taleghan’s Dam to Hashtgerd New City, Tehran, Iran

Arabi¹,

Gharehhassanloo²

2018

OALib

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These instructions give you guidelines for preparing determination of optimum path by considering technical and engineering, environment and cost management visions. In this point of view from Taleghan's dam to Hashtgerd New City case study, we need to distinguish and apply the essential parameters such as topographic and morphologic conditions, environmental issues by consideration on sustainable development, population distribution patterns, roads and etc. by using base map and extraction of expected points geometrical location from Google Earth, position land surveying done. Then the mentioned co-parameters data layer loaded in GIS environment and after allocation the special coefficient and value for weighting parameters and combine them based on MCE method and the cost map made based on AHP method which Lead to determination of optimum path by using LCPA method. Paths include of ABFA path and two evaluated optimum path compared and indicated that two optimum paths overlapped mostly. Cost comparison between optimum path and ABFA path imply 14% decrease in expenditure Which the main extra expense of ABFA path due to more intersection with rivers, roads and necessity construct of structures to keep water natural regimes, passing unauthorized areas and personal parcels need to pay, path finding only by personal experience underestimating academic science, data analyzing software's which Cause to increasing the length of water supply pipeline.

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Section: ) Consolidated Views That Includes Two Preceding Views Is Nmentioning

confidence: 99%

Application and Comparison of Analytical Hierarchy Process (AHP) and Network Methods in Path Finding of Pipeline Water Transmission System, from Taleghan’s Dam to Hashtgerd New City, Tehran, Iran

Arabi¹,

Gharehhassanloo²

2018

OALib

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“…The pioneer of this heuristic crossover operator is Lixin Tang [15], and proposed to utilize parents' implicit information to produce offspring. In the canonical crossover approach, parents mate to produce pairs of offspring that tend to substitute their parents with no guarantee that an offspring produced would be better than either of its mating parent [16]. The heuristic combination approach uses a special crossover that has a knowledge of a problem to combine two candidate solutions to produce an improved solution.…”

Section: B Proposed Ilsachs Protocolmentioning

confidence: 99%

Iterated local search algorithm for clustering wireless sensor networks

Oladimeji

Dudley

2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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In this paper, a new clustering protocol employing an iterated local search (ILS) to solve cluster head selection problem is proposed. ILS uses a perturbation operator to change an initial random solution to produce a new point in the vicinity of the solution. Using a combination operator, this new point is mated with the random solution producing a new solution. A move from the current solution to the new solution is considered acceptable only for higher fitness value. If a move is rejected after a predetermined search length, the change rate of the current solution is increased in order to explore a wider search space for quality solutions. In each round, this search process continues until good solution that ensures balanced energy consumption is obtained for the network. Furthermore, we propose a sleep scheduling scheme inspired by the Boltzmann Selection process in genetic algorithms. This mechanism stochastically considers coverage effect in the selection of nodes that are required to go into sleep mode in order to conserve energy of sensor nodes. The proposed mechanism of inactive node and cluster head selection protocols are performed sequentially at every round and they form part of the main algorithm proposed, namely the Dynamic Local Search-Based Algorithm for Clustering Hierarchy (DLSACH). The ultimate goal of the DLSACH protocol is to extends the network lifetime of wireless sensor networks by reducing and balancing the energy consumption among sensor nodes during communication processes. Our protocol shows an improved performance compared to state-of-the-art protocols such as LEACH, TCAC and SEECH in terms of improved network lifetime for wireless sensor networks deployment.

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“…This combined population will be the basis for the next selection. The individuals (chromosomes) with better fitness values have better chances of selection [7].…”

Section: Selectionmentioning

confidence: 99%

Localization Algorithm for WSN Application using Genetic Algorithm

Kaur¹,

Kaur²

2015

IJERT

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This paper proposes a localization approach based on genetic algorithm for Wireless Sensor Networks. A new way to calculate the distance between anchor node and unknown node has been elaborated, which help in reducing the power consumption of nodes. Genetic Algorithm is based on natural evalution. The genetic algorithm is a probabilistic search algorithm that iteratively transforms a set (called a population) of mathematical objects (typically fixed-length binary character strings), each with an associated fitness value, into a new population of offspring objects using the Darwinian principle of natural selection and using operations that are patterned after naturally occurring genetic operations, such as crossover (sexual recombination) and mutation. The starting node and the ending node are fixed in node distance optimization algorithm. The algorithm has to cover each and every node, keeping in mind that the node should not be repeated. During the first run of algorithm, it has to calculate the distance and mark that distance as index distance. Then GA makes use the GA operators like crossover and mutation to generated new paths and try to find the minimum distance. If distance calculated is less than the index distance, then it is replaced, otherwise rejected. Termination criteria are met, when the number of iteration is completed. Application for genetic algorithm has been succesfully built using MATLAB tool. The distance between two dummy nodes for constant population has been observed as 26.136m and for random population it has been observed as 34.147m and 39.669m respectively.

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A Heuristic Genetic Algorithm for the Single Source Shortest Path Problem

Cited by 20 publications

References 8 publications

Application and Comparison of Analytical Hierarchy Process (AHP) and Network Methods in Path Finding of Pipeline Water Transmission System, from Taleghan’s Dam to Hashtgerd New City, Tehran, Iran

Application and Comparison of Analytical Hierarchy Process (AHP) and Network Methods in Path Finding of Pipeline Water Transmission System, from Taleghan’s Dam to Hashtgerd New City, Tehran, Iran

Iterated local search algorithm for clustering wireless sensor networks

Localization Algorithm for WSN Application using Genetic Algorithm

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