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In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem or a machine learning problem, especially with incomplete or imperfect information or limited computation capacity.
In mathematical optimization and computer science, heuristic (from Greek εὑρίσκω "I find, discover" [1]) is a technique designed for problem solving more quickly when classic methods are too slow for finding an exact or approximate solution, or when classic methods fail to find any exact solution in a search space.
Gigerenzer & Gaissmaier (2011) state that sub-sets of strategy include heuristics, regression analysis, and Bayesian inference. [14]A heuristic is a strategy that ignores part of the information, with the goal of making decisions more quickly, frugally, and/or accurately than more complex methods (Gigerenzer and Gaissmaier [2011], p. 454; see also Todd et al. [2012], p. 7).
An essential feature is the exploitation in some part of the algorithms of features derived from the mathematical model of the problems of interest, thus the definition "model-based heuristics" appearing in the title of some events of the conference series dedicated to matheuristics matheuristics web page.
A local search heuristic is performed through choosing an initial solution x, discovering a direction of descent from x, within a neighborhood N(x), and proceeding to the minimum of f(x) within N(x) in the same direction. If there is no direction of descent, the heuristic stops; otherwise, it is iterated.
The following pseudocode presents the simulated annealing heuristic as described above. It starts from a state s 0 and continues until a maximum of k max steps have been taken. In the process, the call neighbour( s ) should generate a randomly chosen neighbour of a given state s ; the call random(0, 1) should pick and return a value in the ...
This is a chronological table of metaheuristic algorithms that only contains fundamental computational intelligence algorithms.
This heuristic optimization method was proposed in 2007 by Rabanal et al. [31] The applicability of RFD to other NP-complete problems has been studied, [32] and the algorithm has been applied to fields such as routing [33] and robot navigation. [34] The main applications of RFD can be found at the survey Rabanal et al. (2017). [35]