Fuzzy preference-based multi-objective optimization method

Authors:
Sivakumar Ramakrishnan;Yahya Abu Hasan
Affiliations:
School of Materials and Mineral Resources Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, Malaysia;School of Mathematical Science, Universiti Sains Malaysia, Nibong Tebal, Malaysia
Venue:
Artificial Intelligence Review
Year:
2013

Citing 3
Cited 0

Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers

PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
An overview of evolutionary algorithms in multiobjective optimization

Evolutionary Computation

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Abstract

Multiobjective evolutionary computation is still quite young and there are many open research problems. This paper is an attempt to design a hybridized Multiobjective Evolutionary Optimization Algorithm with fuzzy logic called Fuzzy Preference-Based Multi---Objective Optimization Method (FPMOM). FPMOM as an integrated components of Multiobjective Optimization Technique, Evolutionary Algorithm and Fuzzy Inference System able to search and filter the pareto-optimal and provide a good trade-off solution for the multiobjective problem using fuzzy inference method to choose the user intuitive based specific trade-off requirement. This paper will provide a new insight into the behaviourism of interactive Multiobjective Evolutionary Algorithm optimization problems using fuzzy inference method.