Iterative hybridization of DE with local search for the CEC'2015 special session on large scale global optimization

Continuous optimization is an important research field because many real-world problems from very different domains (biology, engineering, data mining, etc.) can be formulated as the optimization of a continuous function. Into continuous optimization, solving high-dimensional optimization problems,...

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Bibliographic Details
Main Authors: Molina, Daniel, Herrera, Francisco
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Benchmark testing
Continuous Optimization
Differential Evolution
Evolutionary computation
Hybridization
Large Scale Global Optimization
Memetic Algorithm
Optimization
Proposals
Sociology
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Summary:Continuous optimization is an important research field because many real-world problems from very different domains (biology, engineering, data mining, etc.) can be formulated as the optimization of a continuous function. Into continuous optimization, solving high-dimensional optimization problems, also called large scale optimization, is a difficult challenge by the huge expansion of the domain search with the dimensionality. In this paper we propose a new hybrid algorithm to tackle this type of optimization, combining a DE with a LS method in a iterative way. The sharing of the best solution between these components in combination with a memory making possible a more in-depth search in each one of them, allowing the algorithm to obtain good results. Experiments are carried out using a benchmark designed for large scale optimization, and the proposal is compared with other algorithms, showing that the algorithm is robust, obtaining good results specially in the most difficult functions.
ISSN:1089-778X
1941-0026
DOI:10.1109/CEC.2015.7257127