Hybrid multiobjective genetic algorithm with a new adaptive local search process

Authors:
Salem F. Adra;Ian Griffin;Peter J. Fleming
Affiliations:
University of Sheffield, Sheffield, UK;University of Sheffield, Sheffield, UK;University of Sheffield, Sheffield, UK
Venue:
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Year:
2005

Citing 2
Cited 3

Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization

Proceedings of the 5th International Conference on Genetic Algorithms
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results

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HCS: a new local search strategy for memetic multiobjective evolutionary algorithms

IEEE Transactions on Evolutionary Computation
A local multiobjective optimization algorithm using neighborhood field

Structural and Multidisciplinary Optimization
Comprehensive Survey of the Hybrid Evolutionary Algorithms

International Journal of Applied Evolutionary Computation

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Abstract

This paper is concerned with a specific brand of evolutionary algorithms: Memetic algorithms. A new local search technique with an adaptive neighborhood setting process is introduced and assessed against a set of test functions presenting different challenges. Two performance criteria were assessed: the convergence of the achieved results towards the true Pareto fronts and their distribution.