Interactive Multiobjective Metaheuristic algorithm integrating BP Neural Network for hybrid indices optimization problems

DOI

10.2991/icsmim-15.2016.128How to use a DOI?

Keywords

Interactive decision making; metaheuristic algorithm; Hybrid indices optimization problems; Multiobjective optimization algorithm.

Abstract

In this paper, we focus on investigating effective approach for tackling the complex hybrid indices optimization problems (HIOPs). Firstly, we analyze the strategy that utilizes machine learning models for breaking restrictions on search ability of MOEAs employed in conventional optimization framework based on interactive evolutionary computation (IEC). Then we take a plant layout design problem as a typical instance of HIOPs to devise an effective mechanism for employing BP network to forecast tacit fitnesses of design solutions. Furthermore, we develop an interactive multiobjective metaheuristic algorithm (IMMA) integrating a BP neural network for HIOPs. We also develop a prototype system based on IMMA for plant layout design optimization and experimental results have verified effectiveness of the proposed IMMA.

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  - Junling Zhang
PY  - 2016/01
DA  - 2016/01
TI  - Interactive Multiobjective Metaheuristic algorithm integrating BP Neural Network for hybrid indices optimization problems
BT  - Proceedings of the 2015 4th International Conference on Sensors, Measurement and Intelligent Materials
PB  - Atlantis Press
SP  - 685
EP  - 693
SN  - 2352-538X
UR  - https://doi.org/10.2991/icsmim-15.2016.128
DO  - 10.2991/icsmim-15.2016.128
ID  - Zhang2016/01
ER  -