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

Adriana Lara, Gustavo Sanchez, Carlos A.Coello Coello, Oliver Schütze

Research output: Contribution to journalArticlepeer-review

166 Scopus citations

Abstract

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.

Original languageEnglish
Article number5352350
Pages (from-to)112-132
Number of pages21
JournalIEEE Transactions on Evolutionary Computation
Volume14
Issue number1
DOIs
StatePublished - Feb 2010
Externally publishedYes

Keywords

  • Continuation
  • Hill climber
  • Memetic strategy
  • Multiobjective optimization

Fingerprint

Dive into the research topics of 'HCS: A new local search strategy for memetic multiobjective evolutionary algorithms'. Together they form a unique fingerprint.

Cite this