A Novel Knowledge Space Based on Optimization Hardness

DOI

10.2991/mmebc-16.2016.4How to use a DOI?

Keywords

Memetic Algorithm, Optimization Hardness, Effective High-frequency Ratio, Fitness Distance Correlation

Abstract

This paper uses exhaustive disturbed particle swarm optimizer(EDPSO) as local search algorithm for memetic algorithm, and improves it by using a novel knowledge space mechanism. The innovative mechanismbased on two key points: 1. using effective high-frequency ratio (EHFR) as a novel indicator for problem features and a control parameters of density and range of disturbance, as same as fitness distance correlation; 2. Using the table of sampling spatial distribution to enhance the effective of disturbance behavior. Then, this paper regards. At last, the result of the comparative analysis between the test results of ant colony optimizer (ACO), differential evolution (DE), and OHBMA indicates that OHBMA shows better performance than ACO and DE.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lu Ren
AU  - Jie Fang
PY  - 2016/06
DA  - 2016/06
TI  - A Novel Knowledge Space Based on Optimization Hardness
BT  - Proceedings of the 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer
PB  - Atlantis Press
SP  - 19
EP  - 24
SN  - 2352-5401
UR  - https://doi.org/10.2991/mmebc-16.2016.4
DO  - 10.2991/mmebc-16.2016.4
ID  - Ren2016/06
ER  -