They cover optimization and water resources management, the established yet evolving relationship between genetic algorithms and water resource management, applying genetic algorithms in optimizing the design of irrigation networks, simulated annealing algorithms for optimizing water systems, particle swarm optimization, tabu search for managing groundwater, the harmony search algorithm, and groundwater optimal management using an outer approximation approach.
What does SA stand for?
SA stands for Simulated Annealing
This definition appears very frequently and is found in the following Acronym Finder categories:
- Science, medicine, engineering, etc.
See other definitions of SA
We have 222 other meanings of SA in our Acronym Attic
- short answer
- Show Assistant
- Shredder Afval (Dutch: shredder garbage)
- SicherheitsAbteilung (German: Safety Department)
- Side Angle (geometry)
- Sieve Analysis
- Sigma Alpha (sorority)
- Signature Analysis
- Significant Accomplishments
- Simple Alert
Samples in periodicals archive:
Then, the search techniques such as simulated annealing (Suresh and Sahu, 1993), ant colony optimization technique (Baykasoglu, 2006), genetic algorithm, etc.
We are using branch-and-bound and simulated annealing global optimization techniques to explore the trade-off between improvement in schedule length and time spent for calculation.
Among specific topics are fuzzy relational image compression for face recognition tasks, image retrieval using sieve complement trees, multi-agent systems for analyzing gene expression to identify genes involved in cervical cancer, multi-document summarization based on locally relevant sentences, obtaining teachers' expertise to refine an affective model in an intelligent tutor for learning robotics, solving employee time-tabling in a call center of a telecommunications company in Mexico with simulated annealing, and cognitive maps in transport behavior.
As an algorithm, the main strength of PSO is its fast convergence, which compares favorably with many global optimization algorithms like Genetic Algorithms (GA), Simulated Annealing (SA) and other global optimization algorithms.
Methods like tabu search, beam search, ant colony algorithm, simulated annealing algorithm and genetic algorithm are restricted for solving some particular problems.
Section 4 discusses the Simulated Annealing approach.
This can be partially overcome using varied hill climbing methods such as iterated hill climbing, stochastic hill climbing, random walks, and simulated annealing .