scholarly journals A new mathematical formulation and a hybrid evolutionary algorithm for re-entrant flow-shop problem with release date

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Reza Behmanesh ◽  
Kamran Kianfar
Keyword(s):  
Evolutionary Algorithm ◽  
Flow Shop ◽  
Mathematical Formulation ◽  
Release Date ◽  
Hybrid Evolutionary Algorithm ◽  
Flow Shop Problem

scholarly journals A two-stage hybrid flow shop problem with dedicated machine and release date

2018 ◽  
Vol 126 ◽  
pp. 214-223
Author(s):  
Zouhour Nabli ◽  
Soulef Khalfallah ◽  
Ouajdi Korbaa
Keyword(s):  
Flow Shop ◽  
Hybrid Flow Shop ◽  
Release Date ◽  
Two Stage ◽  
Flow Shop Problem

scholarly journals Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Lhassane Idoumghar ◽  
Mahmoud Melkemi ◽  
René Schott ◽  
Maha Idrissi Aouad
Keyword(s):  
Energy Consumption ◽  
Embedded Systems ◽  
Evolutionary Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Time Saving ◽  
Local Optima ◽  
Hybrid Evolutionary Algorithm ◽  
Multimodal Function Optimization ◽  
Multimodal Function

The paper presents a novel hybrid evolutionary algorithm that combines Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optima becomes difficult. To avoid premature convergence of PSO, we present a new hybrid evolutionary algorithm, called HPSO-SA, based on the idea that PSO ensures fast convergence, while SA brings the search out of local optima because of its strong local-search ability. The proposed HPSO-SA algorithm is validated on ten standard benchmark multimodal functions for which we obtained significant improvements. The results are compared with these obtained by existing hybrid PSO-SA algorithms. In this paper, we provide also two versions of HPSO-SA (sequential and distributed) for minimizing the energy consumption in embedded systems memories. The two versions, of HPSO-SA, reduce the energy consumption in memories from 76% up to 98% as compared to Tabu Search (TS). Moreover, the distributed version of HPSO-SA provides execution time saving of about 73% up to 84% on a cluster of 4 PCs.

Sign in / Sign up

Export Citation Format

Share Document