I-MODE: an interactive multi-objective optimization and decision-making using evolutionary methods

Authors:
Kalyanmoy Deb;Shamik Chaudhuri
Affiliations:
Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, Kanpur, Kanpur, India;Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, Kanpur, Kanpur, India
Venue:
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Year:
2007

Citing 6
Cited 4

Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
I-EMO: an interactive evolutionary multi-objective optimization tool

PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Searching for robust pareto-optimal solutions in multi-objective optimization

EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
A multiobjective evolutionary algorithm toolbox for computer-aidedmultiobjective optimization

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multiobjective optimization and multiple constraint handling with evolutionary algorithms. II. Application example

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Future Challenges

Multiobjective Optimization
An interactive evolutionary multi-objective optimization and decision making procedure

Applied Soft Computing
Toward an estimation of nadir objective vector using a hybrid of evolutionary and local search approaches

IEEE Transactions on Evolutionary Computation
A two-stage preference-based evolutionary multi-objective approach for capability planning problems

Knowledge-Based Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

With the popularity of efficient multi-objective evolutionary optimization (EMO) techniques and the need for such problem-solving activities in practice, EMO methodologies and EMO research and application have received a great deal of attention in the recent past. The first decade of research in EMO area has been spent on developing efficient algorithms for finding a well-converged and well-distributed set of Pareto-optimal solutions, although EMO researchers were always aware of the importance of procedures which would help choose one particular solution from the Pareto-optimal set for implementation. In this paper, we address this long-standing issue and suggest an interactive EMO procedure by collating most salient research in EMO and putting together a step-by-step EMO and decision-making procedure. The idea is implemented in a GUI-based, user-friendly software which allows a user to supply the problem mathematically or by using user-defined macros and enables the user to evaluate solutions directly or by calling an executable software, such as popularly-used MATLAB software for a local search or ANSYS software for finite element analysis, etc. Starting with standard EMO applications, continuing to finding robust, partial, and user-defined preferred frontiers through standard MCDM procedures, the well-coordinated software allows the user to first have an idea of the complete trade-off frontier, then systematically focus in preferred regions, and finally choose a single solution for implementation.