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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

Multiobjective synchronization of coupled systems.

Yang Tang¹, Zidong Wang, W K Wong

¹School of Information Science and Technology, Donghua University, Shanghai, China. tangtany@gmail.com

Chaos (Woodbury, N.Y.)

|July 5, 2011

Summary

This summary is machine-generated.

This study optimizes chaotic system synchronization by balancing control cost and convergence speed using a novel evolutionary approach. The method effectively manages coupling forms and strengths for enhanced system control.

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Area of Science:

Control Theory
Chaos Synchronization
Computational Intelligence

Background:

Controlling chaotic systems for synchronization is challenging.
Existing methods often struggle to balance multiple objectives like cost and speed.
Need for advanced optimization techniques in chaotic system analysis.

Purpose of the Study:

To investigate multiobjective synchronization of chaotic systems.
To simultaneously minimize control cost and convergence speed.
To optimize coupling form and strength using a novel evolutionary approach.

Main Methods:

Developed an improved multiobjective evolutionary algorithm with hybrid chromosome representation (binary and real).
Integrated adaptive learning and non-dominated sorting genetic algorithm-II (NSGA-II).
Formulated synchronization as a multiobjective constraint problem to handle coupling form constraints.

Main Results:

The proposed hybrid evolutionary approach effectively optimized coupling parameters for chaotic systems.
Validated the method's performance and effectiveness using Rössler systems (chaotic and hyperchaotic) and delayed chaotic neural networks.
Demonstrated superior performance compared to standard methods in achieving multiobjective synchronization.

Conclusions:

The developed multiobjective evolutionary approach offers an effective solution for chaotic system synchronization.
The hybrid encoding and constraint handling significantly improve optimization of control cost and convergence speed.
This research contributes a robust framework for advanced control of complex dynamical systems.