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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Published on: December 9, 2012

Interactive evolutionary multiobjective search and optimization of set-based concepts.

Gideon Avigad¹, Amiram Moshaiov

¹Mechanical Engineering Department, ORT Braude College of Engineering, Karmiel 21982, Israel. gideona@braude.ac.il

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|April 2, 2009

Summary

This summary is machine-generated.

This study introduces a novel evolutionary computation method for interactive concept-based multiobjective problems (IC-MOPs), aiding engineers in conceptual design. The approach enhances human-computer interaction for complex optimization tasks.

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Area of Science:

Engineering Optimization
Evolutionary Computation
Multiobjective Optimization

Background:

Conceptual design stages require effective support for engineers.
Traditional optimization methods may not adequately address the complexity of concept-based multiobjective problems (IC-MOPs).
Human-computer interaction is crucial for navigating complex design spaces.

Purpose of the Study:

To introduce a novel evolutionary computation approach for solving IC-MOPs.
To formally define Pareto-directed IC-MOPs and present their solution.
To develop and evaluate a new interactive algorithm for IC-MOPs.

Main Methods:

Development of a set-based concept representation for solutions.
Formal definition and solution presentation for Pareto-directed IC-MOPs.
Introduction of an interactive concept-based multiobjective evolutionary algorithm.

Main Results:

A new algorithm for IC-MOPs was introduced.
Measures to assess the performance of resulting fronts were devised.
The proposed approach was validated using test functions and an engineering problem.

Conclusions:

The presented evolutionary computation approach effectively addresses IC-MOPs.
The new algorithm shows promise for supporting engineers in conceptual design.
Set-based concept representation facilitates human-computer interaction in optimization.