A Cultural Algorithm for Scheduling of Hydro Producer in the Power Market

Author(s):  
Xiaohui Yuan ◽  
Hao Nie ◽  
Li He ◽  
Cailin Li ◽  
Yongchuan Zhang
2007 ◽  
Vol 127 (4) ◽  
pp. 573-580 ◽  
Author(s):  
Toshiyuki Sawa ◽  
Yuji Nakata ◽  
Mitsuo Tsurugai ◽  
Shigenari Sugiyama

2016 ◽  
Vol 11 (4) ◽  
pp. 381
Author(s):  
Madan Mohan Tripathi ◽  
Anil Kumar Pandey ◽  
Amit Verma ◽  
Krishan Gopal Upadhyay ◽  
Dinesh Chandra

2017 ◽  
Author(s):  
Mikael Hilddn ◽  
Hannu Huuki ◽  
Visa Kivisaari ◽  
Maria Kopsakangas-Savolainen

Author(s):  
Vaishali R. Kulkarni ◽  
Veena Desai ◽  
Raghavendra Kulkarni

Background & Objective: Location of sensors is an important information in wireless sensor networks for monitoring, tracking and surveillance applications. The accurate and quick estimation of the location of sensor nodes plays an important role. Localization refers to creating location awareness for as many sensor nodes as possible. Multi-stage localization of sensor nodes using bio-inspired, heuristic algorithms is the central theme of this paper. Methodology: Biologically inspired heuristic algorithms offer the advantages of simplicity, resourceefficiency and speed. Four such algorithms have been evaluated in this paper for distributed localization of sensor nodes. Two evolutionary computation-based algorithms, namely cultural algorithm and the genetic algorithm, have been presented to optimize the localization process for minimizing the localization error. The results of these algorithms have been compared with those of swarm intelligence- based optimization algorithms, namely the firefly algorithm and the bee algorithm. Simulation results and analysis of stage-wise localization in terms of number of localized nodes, computing time and accuracy have been presented. The tradeoff between localization accuracy and speed has been investigated. Results: The comparative analysis shows that the firefly algorithm performs the localization in the most accurate manner but takes longest convergence time. Conclusion: Further, the cultural algorithm performs the localization in a very quick time; but, results in high localization error.


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