An augmented EDA with dynamic diversity control and local neighborhood search for coevolution of optimal negotiation strategies

2012 ◽  
Vol 38 (4) ◽  
pp. 600-619 ◽  
Author(s):  
Jeonghwan Gwak ◽  
Kwang Mong Sim
Keyword(s):  
Neighborhood Search ◽  
Negotiation Strategies ◽  
Local Neighborhood ◽  
Diversity Control

The solution of the concurrent layout scheduling problem in the job-shop environment through a local neighborhood search algorithm

2020 ◽  
Vol 144 ◽  
pp. 113096 ◽  
Author(s):  
Eva Selene Hernández-Gress ◽  
Juan Carlos Seck-Tuoh-Mora ◽  
Norberto Hernández-Romero ◽  
Joselito Medina-Marín ◽  
Pedro Lagos-Eulogio ◽  
...  
Keyword(s):  
Job Shop ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Local Neighborhood ◽  
Neighborhood Search Algorithm

Skip Neighborhood Hybrid Particle Swarm Optimization Algorithm

2011 ◽  
Vol 311-313 ◽  
pp. 1863-1868
Author(s):  
Jian Jun Li ◽  
Bin Yu ◽  
Wu Ping Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Particle Swarm ◽  
Global Search ◽  
Local Optimization ◽  
Neighborhood Search ◽  
Swarm Optimization ◽  
Local Neighborhood ◽  
Hybrid Particle Swarm Optimization ◽  
Speed Accuracy

Traditional Particle Swarm Optimization (PSO) uses single search strategy and is difficult to balance the global search with local search, and easy to fall into local optimization, a new algorithm which integrates global search with local neighborhood search is presented. The algorithm performs the global search in parallel with the local search by the feedback of the global optimal particle and the information interaction of local neighborhood. Meanwhile, with a new neighborhood topology to control the search space, the algorithm can avoid the local optimization successfully. Tested by four classical functions, the new algorithm performs well on optimization speed, accuracy and success rate.

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