Abstract
Heuristic algorithms (or simply heuristics) are methods that seek for high quality solutions within a reasonable (limited) amount of time without being able to guarantee optimality. They often come out as a result of imitation of the real world (physics, nature, biology, etc.). In this paper, we give an overview of some heuristic algorithms for combinatorial optimization problems. At the beginning, some definitions related to combinatorial optimization, as well as the principle (framework) and basic features of the heuristics for combinatorial problems are concerned. Then, several popular heuristic algorithms are discussed, namely: descent local search, simulated annealing, tabu search, genetic algorithms, ant algorithms, and iterated local search. The unified paradigms of these heuristics are given. Finally, we present some results of comparisons of these algorithms for the well-known combinatorial problem, the quadratic assignment problem.
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