In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity. Metaheuristics sample a set of solutions which is too large to be completely sampled. Metaheuristics may make few assumptions about the optimization problem being solved, and so they may be usable for a variety of problems.
Most literature on metaheuristics is experimental in nature, describing empirical results based on computer experiments with the algorithms. But some formal theoretical results are also available, often on convergence and the possibility of finding the global optimum. Many metaheuristic methods have been published with claims of novelty and practical efficacy. While the field also features high-quality research, many of the publications have been of poor quality; flaws include vagueness, lack of conceptual elaboration, poor experiments, and ignorance of previous literature.
These are properties that characterize most metaheuristics:
Metaheuristics are strategies that guide the search process.
The goal is to efficiently explore the search space in order to find near-optimal solutions.
Techniques which constitute metaheuristic algorithms range from simple local search procedures to complex learning processes.
Metaheuristic algorithms are approximate and usually non-deterministic.
Metaheuristics are not problem-specific.
Euler diagram of the different classifications of metaheuristics.
There are a wide variety of metaheuristics and a number of properties with respect to which to classify them.
Local search vs. Global search
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.
On the other hand, Memetic algorithms represent the synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. An example of memetic algorithm is the use of a local search algorithm instead of a basic mutation operator in evolutionary algorithms.
R. Balamurugan; A.M. Natarajan; K. Premalatha (2015). "Stellar-Mass Black Hole Optimization for Biclustering Microarray Gene Expression Data,". Applied Artificial Intelligence an International Journal. taylor & francis. 29 (4): 353-381.
^ abcdeBianchi, Leonora; Marco Dorigo; Luca Maria Gambardella; Walter J. Gutjahr (2009). "A survey on metaheuristics for stochastic combinatorial optimization". Natural Computing: an international journal. 8 (2): 239-287. doi:10.1007/s11047-008-9098-4.
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