Topics in global optimization

1982 ◽  
pp. 18-33 ◽  
Author(s):  
A. V. Levy ◽  
A. Montalvo ◽  
S. Gomez ◽  
A. Calderon
Keyword(s):  
Global Optimization

Global Optimization

1993 ◽  
Author(s):  
Reiner Horst ◽  
Hoang Tuy
Keyword(s):  
Global Optimization

Probability Models in Global Optimization

Informatica ◽  
2016 ◽  
Vol 27 (2) ◽  
pp. 323-334 ◽  
Author(s):  
James M. Calvin
Keyword(s):  
Global Optimization ◽  
Probability Models

Hybrid orthogonal CMAES for solving global optimization problems

2013 ◽  
Vol 32 (4) ◽  
pp. 981-985
Author(s):  
Ya-fei HUANG ◽  
Xi-ming LIANG ◽  
Yi-xiong CHEN
Keyword(s):  
Global Optimization ◽  
Optimization Problems

Catechize Global Optimization through Leading Edge Firefly Based Zone Routing Protocol

2020 ◽  
Vol 13 (2) ◽  
pp. 147-157 ◽  
Author(s):  
Neha Sharma ◽  
Sherin Zafar ◽  
Usha Batra
Keyword(s):  
Global Optimization ◽  
Routing Protocol ◽  
Firefly Algorithm ◽  
Leading Edge ◽  
Packet Delivery Ratio ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
Zone Routing Protocol ◽  
End To End Delay ◽  
End To End

Background: Zone Routing Protocol is evolving as an efficient hybrid routing protocol with an extremely high potentiality owing to the integration of two radically different schemes, proactive and reactive in such a way that a balance between control overhead and latency is achieved. Its performance is impacted by various network conditions such as zone radius, network size, mobility, etc. Objective: The research work described in this paper focuses on improving the performance of zone routing protocol by reducing the amount of reactive traffic which is primarily responsible for degraded network performance in case of large networks. The usage of route aggregation approach helps in reducing the routing overhead and also help achieve performance optimization. Methods: The performance of proposed protocol is assessed under varying node size and mobility. Further applied is the firefly algorithm which aims to achieve global optimization that is quite difficult to achieve due to non-linearity of functions and multimodality of algorithms. For performance evaluation a set of benchmark functions are being adopted like, packet delivery ratio and end-to-end delay to validate the proposed approach. Results: Simulation results depict better performance of leading edge firefly algorithm when compared to zone routing protocol and route aggregation based zone routing protocol. The proposed leading edge FRA-ZRP approach shows major improvement between ZRP and FRA-ZRP in Packet Delivery Ratio. FRA-ZRP outperforms traditional ZRP and RA-ZRP even in terms of End to End Delay by reducing the delay and gaining a substantial QOS improvement. Conclusion: The achievement of proposed approach can be credited to the formation on zone head and attainment of route from the head hence reduced queuing of data packets due to control packets, by adopting FRA-ZRP approach. The routing optimized zone routing protocol using Route aggregation approach and FRA augments the QoS, which is the most crucial parameter for routing performance enhancement of MANET.

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