Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICCIS.2006.252276
Title: Particle swarm assisted incremental evolution strategy for function optimization
Authors: Mo, W.
Guan, S.-U. 
Keywords: Evolution strategy
Multi-variable evolution (MVE)
Particle swarm optimization incremental optimization
Single-variable evolution (SVE)
Issue Date: 2006
Source: Mo, W.,Guan, S.-U. (2006). Particle swarm assisted incremental evolution strategy for function optimization. 2006 IEEE Conference on Cybernetics and Intelligent Systems : -. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCIS.2006.252276
Abstract: This paper presents a new evolutionary approach for function optimization problems Particle Swarm Assisted Incremental Evolution Strategy (PIES). Two strategies are proposed. One is incremental optimization that the whole evolution consists of several phases and one more variable is focused in each phase. The number of phases is equal to the number of variables in maximum. Each phase is composed of two stages: in the single-variable evolution (SVE) stage, a population is evolved with respect to one independent variable in a series of cutting planes; in the multi-variable evolving (MVE) stage, the initial population is formed by integrating the population obtained by the SVE in current phase and by the MVE in the last phase. And then the MVE is taken on the incremented variable set. The second strategy is a hybrid of particle swarm optimization (PSO) and the evolution strategy (ES). PSO is applied to adjust the cutting planes (in SVEs) or hyper-planes (in MVEs) while ES is applied to searching optima in the cutting planes/hyper-planes. The results of experiments show that PIES generally outperforms three other evolutionary algorithms, improved normal GA, PSO and SADE_CERAF, in the sense that PIES finds solutions with more optimal objective values and closer to the true optima. © 2006 IEEE.
Source Title: 2006 IEEE Conference on Cybernetics and Intelligent Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/71370
ISBN: 1424400236
DOI: 10.1109/ICCIS.2006.252276
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

1
checked on Dec 11, 2017

Page view(s)

13
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.