Genetical Swarm Optimization: a New Hybrid Evolutionary Algorithm for Electromagnetic Applications

2005 ◽  
Author(s):  
E.A. Grimaldi ◽  
F. Grimaccia ◽  
M. Mussetta ◽  
P. Pirinoli ◽  
R.E. Zich
Keyword(s):  
Evolutionary Algorithm ◽  
Swarm Optimization ◽  
Hybrid Evolutionary Algorithm

scholarly journals Path Planning Using a Hybrid Evolutionary Algorithm Based on Tree Structure Encoding

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ming-Yi Ju ◽  
Siao-En Wang ◽  
Jian-Horn Guo
Keyword(s):  
Path Planning ◽  
Evolutionary Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Tree Structure ◽  
Mobile Robot Navigation ◽  
Swarm Optimization ◽  
Hybrid Evolutionary Algorithm ◽  
Dummy Node ◽  
Encoding Method ◽  
Binary Tree Structure

A hybrid evolutionary algorithm using scalable encoding method for path planning is proposed in this paper. The scalable representation is based on binary tree structure encoding. To solve the problem of hybrid genetic algorithm and particle swarm optimization, the “dummy node” is added into the binary trees to deal with the different lengths of representations. The experimental results show that the proposed hybrid method demonstrates using fewer turning points than traditional evolutionary algorithms to generate shorter collision-free paths for mobile robot navigation.

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.

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