Hybridizing evolutionary algorithms with opportunistic local search

2013 ◽  
Author(s):  
Christian Gießen
Keyword(s):  
Evolutionary Algorithms ◽  
Local Search

scholarly journals Hybridization of evolutionary algorithms and local search by means of a clustering method

2006 ◽  
Vol 36 (3) ◽  
pp. 534-545 ◽  
Author(s):  
A.C. Martinez-Estudillo ◽  
C. Hervas-Martinez ◽  
F.J. Martinez-Estudillo ◽  
N. Garcia-Pedrajas
Keyword(s):  
Evolutionary Algorithms ◽  
Local Search ◽  
Clustering Method

Nelder-Mead Evolutionary Hybrid Algorithms

2011 ◽  
pp. 1191-1196 ◽  
Author(s):  
Sanjoy Das
Keyword(s):  
Evolutionary Algorithms ◽  
Local Search ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Space ◽  
Simplex Algorithm ◽  
Stochastic Methods ◽  
Great Success ◽  
Local Minima ◽  
Swarm Optimization

Real world optimization problems are often too complex to be solved through analytic means. Evolutionary algorithms are a class of algorithms that borrow paradigms from nature to address them. These are stochastic methods of optimization that maintain a population of individual solutions, which correspond to points in the search space of the problem. These algorithms have been immensely popular as they are derivativefree techniques, are not as prone to getting trapped in local minima, and can be tailored specifically to suit any given problem. The performance of evolutionary algorithms can be improved further by adding a local search component to them. The Nelder-Mead simplex algorithm (Nelder & Mead, 1965) is a simple local search algorithm that has been routinely applied to improve the search process in evolutionary algorithms, and such a strategy has met with great success. In this article, we provide an overview of the various strategies that have been adopted to hybridize two wellknown evolutionary algorithms - genetic algorithms (GA) and particle swarm optimization (PSO).

Modified Differential Evolution for Tension/Compression String Design Problem

2013 ◽  
Vol 705 ◽  
pp. 523-527
Author(s):  
Li Jian ◽  
Cheng Jiu Yin ◽  
Sachio Hirokawa ◽  
Yoshiyuki Tabata
Keyword(s):  
Social Networks ◽  
Evolutionary Algorithms ◽  
Local Search ◽  
Differential Evolution ◽  
Design Problem ◽  
Global Search ◽  
The Other ◽  
High Connectivity ◽  
The Difference

This paper introduces a modified differential evoluiton method to solve the tension/compression string design problem. The modification is derived from mechanisms of social networks. In the proposed method, each individual will be attracted by the knowed best individual following the connectivity between each other. The connectivity is calculated based on the difference of the variables in each vector. The individuals with high connectivity tend to perform local search while those with poor connectivity tend to perform global search instead. The approach was employed for a tension/compression string design problem and by comparisons with the other evolutionary algorithms, the proposed method privided better resutls.

scholarly journals Modified Biogeography-Based Optimization with Local Search Mechanism

2013 ◽  
Vol 2013 ◽  
pp. 1-24 ◽  
Author(s):  
Quanxi Feng ◽  
Sanyang Liu ◽  
Qunying Wu ◽  
GuoQiang Tang ◽  
Haomin Zhang ◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Local Search ◽  
Optimization Algorithm ◽  
Experimental Tests ◽  
Effective Population ◽  
Migration Operator ◽  
Search Mechanism ◽  
Simulation Results

Biogeography-based optimization (BBO) is a new effective population optimization algorithm based on the biogeography theory with inherently insufficient exploration capability. To address this limitation, we proposed a modified BBO with local search mechanism (denoted as MLBBO). In MLBBO, a modified migration operator is integrated into BBO, which can adopt more information from other habitats, to enhance the exploration ability. Then, a local search mechanism is used in BBO to supplement with modified migration operator. Extensive experimental tests are conducted on 27 benchmark functions to show the effectiveness of the proposed algorithm. The simulation results have been compared with original BBO, DE, improved BBO algorithms, and other evolutionary algorithms. Finally, the performance of the modified migration operator and local search mechanism are also discussed.

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