HCS: A New Local Search Strategy for Memetic Multiobjective Evolutionary Algorithms

In this paper, we propose and investigate a new local search strategy for multiobjective memetic algorithms. More precisely, we suggest a novel iterative search procedure, known as the Hill Climber with Sidestep (HCS), which is designed for the treatment of multiobjective optimization problems, and...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation 2010-02, Vol.14 (1), p.112-132
Main Authors: Lara, A., Sanchez, G., Coello Coello, C.A., Schutze, O.
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Continuation
Costs
Design optimization
Evolutionary algorithms
Evolutionary computation
Exact sciences and technology
Genetic algorithms
Genetics
hill climber
Information geometry
Iterative algorithms
Learning and adaptive systems
memetic strategy
multiobjective optimization
Operations research
Optimization
Pareto optimality
Product design
Searching
Sorting
Strategy
Strength
Switches
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Summary:In this paper, we propose and investigate a new local search strategy for multiobjective memetic algorithms. More precisely, we suggest a novel iterative search procedure, known as the Hill Climber with Sidestep (HCS), which is designed for the treatment of multiobjective optimization problems, and show further two possible ways to integrate the HCS into a given evolutionary strategy leading to new memetic (or hybrid) algorithms. The pecularity of the HCS is that it is intended to be capable both moving toward and along the (local) Pareto set depending on the distance of the current iterate toward this set. The local search procedure utilizes the geometry of the directional cones of such optimization problems and works with or without gradient information. Finally, we present some numerical results on some well-known benchmark problems, indicating the strength of the local search strategy as a standalone algorithm as well as its benefit when used within a MOEA. For the latter we use the state of the art algorithms Nondominated Sorting Genetic Algorithm-II and Strength Pareto Evolutionary Algorithm 2 as base MOEAs.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2009.2024143