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Ant colony approach to continuous function optimization
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DOI：10.1021/ie990700g.png)
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Abstract
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An ant colony optimization framework has been compared and shown to be a viable alternative approach to other stochastic search algorithms. The algorithm has been tested for variety of different benchmark test functions involving constrained and unconstrained NLP, MILP, and MINLP optimization problems. This novel algorithm handles different types of continuous functions very well and can be successfully used for large-scale process optimization.
Keywords:
SIMULATED ANNEALING APPROACH
ADAPTIVE RANDOM SEARCH
MINLP PROBLEMS
GLOBAL OPTIMIZATION
SYSTEM
DESIGN
NLP
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