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

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

Published on: December 9, 2012

Multimodal optimization using a bi-objective evolutionary algorithm.

Kalyanmoy Deb¹, Amit Saha

¹Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, USA. deb@iitk.ac.in

Evolutionary Computation

|May 20, 2011

Summary

This summary is machine-generated.

This study introduces a novel method for multimodal optimization, transforming single-objective problems into bi-objective ones to find multiple optimal solutions. This approach successfully identifies numerous optima in complex, high-variable problems.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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

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

Published on: December 9, 2012

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Computational intelligence
Optimization algorithms
Evolutionary computation

Background:

Multimodal optimization aims to find diverse optimal solutions for better user choice.
Evolutionary algorithms (EA) are suitable for capturing multiple solutions.
Existing methods often use niching schemes within single-objective EA frameworks.

Purpose of the Study:

To develop a novel strategy for single-objective multimodal optimization.
To convert multimodal problems into bi-objective optimization problems.
To identify all optimal solutions as part of the weak Pareto-optimal set.

Main Methods:

Transforming single-objective multimodal optimization into a bi-objective problem.
Modifying domination definitions.
Formulating an artificial objective function.
Applying the method to scalable, high-variable, and constrained test problems.

Main Results:

Successfully identified up to 500 optima in test problems.
Solved problems with up to 16 variables and 48 optima.
Demonstrated scalability in terms of optima, constraints, and variables.
Introduced the first scalable multimodal constrained test problems.

Conclusions:

The bi-objective conversion strategy is effective for single-objective multimodal optimization.
This novel approach expands the capabilities of evolutionary optimization.
The findings open new research and application possibilities in optimization.