Intelligent Algorithm Optimization Design
in Heat Transfer Problems
Intelligent Algorithm have been used over the last 15 years in the field of heat transfer, which are an optimization tool based on meta-heuristics. They have been developed in the 1970s, but their utilization in heat transfer problems is more recent. In particular, the last couple of years have seen a sharp increase of interest in IAs for heat transfer related optimization problems. Three main families of heat transfer problems using IAs have been identified:
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thermal systems design problems;
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inverse heat transfer problems;
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development of heat transfer correlations.
One approach is to characterize the type of search strategy. One type of search strategy is an improvement on simple local search algorithms. A well known local search algorithm is the hill climbing method which is used to find local optimums. However, hill climbing does not guarantee finding global optimum solutions.
Many metaheuristic ideas were proposed to improve local search heuristic in order to find better solutions. Such metaheuristics include simulated annealing, tabu search, iterated local search, variable neighborhood search, and GRASP. These metaheuristics can both be classified as local search-based or global search metaheuristics.
Other global search metaheuristic that are not local search-based are usually population-based metaheuristics. Such metaheuristics include ant colony optimization, evolutionary computation, particle swarm optimization, Cuckoo Optimization algorithm, genetic algorithms.
