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

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Combinatorial optimization with use of guided evolutionary simulated annealing.

P C Yip¹, Y H Pao

¹Dept. of Electr. Eng. and Appl. Phys., Case Western Reserve Univ., Cleveland, OH.

IEEE Transactions on Neural Networks

|January 1, 1995

Summary

Guided evolutionary simulated annealing (GESA) offers a novel heuristic for complex optimization problems. This new method efficiently finds near-optimal solutions for combinatorial and function optimization tasks.

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Area of Science:

Computer Science
Artificial Intelligence
Operations Research

Background:

NP-complete problems often require heuristic methods for efficient solving.
Existing heuristic procedures can be arbitrary and less effective.
Simulated evolution is a common approach for optimization tasks.

Purpose of the Study:

To introduce a new heuristic technique called guided evolutionary simulated annealing (GESA).
To evaluate GESA's effectiveness in solving combinatorial optimization and function optimization problems.
To compare GESA's performance against traditional simulated evolution.

Main Methods:

Incorporating simulated annealing principles into simulated evolution.
Applying GESA to the traveling salesman problem (a benchmark combinatorial optimization task).
Utilizing GESA for function optimization, treating it as a search problem with a complex function.

Main Results:

GESA discovers very good near-optimal solutions after exploring a minimal fraction of the solution space.
The technique consistently yields good near-optimal solutions for complex functions with multiple local minima.
GESA demonstrates superior performance compared to standard simulated evolution.

Conclusions:

Guided evolutionary simulated annealing (GESA) is a practicable and effective method for optimization.
GESA provides a robust alternative to arbitrary heuristics in solving NP-complete and function optimization problems.
The GESA approach offers significant improvements in solution quality and efficiency.