9 relations: Algorithm, Ant colony optimization algorithms, Evolutionary algorithm, Metaheuristic, Metaphor, Methodology, Nature, Particle swarm optimization, Simulated annealing.
Algorithm
In mathematics and computer science, an algorithm is an unambiguous specification of how to solve a class of problems.
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Ant colony optimization algorithms
In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.
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Evolutionary algorithm
In artificial intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm.
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Metaheuristic
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.
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Metaphor
A metaphor is a figure of speech that directly refers to one thing by mentioning another for rhetorical effect.
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Methodology
Methodology is the systematic, theoretical analysis of the methods applied to a field of study.
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Nature
Nature, in the broadest sense, is the natural, physical, or material world or universe.
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Particle swarm optimization
In computer science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.
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Simulated annealing
Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function.
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References
[1] https://en.wikipedia.org/wiki/Adaptive_dimensional_search