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- W139793179 abstract "There are several search methods that try to emulate some processes observed in nature. The methods in this class have been given different names, such as stochastic programming methods, evolutionary algorithms, metaheuristic methods, and so on. The methods also fall into the class of direct search methods that do not require use of derivatives of functions in their search. In this chapter, four methods are presented and explained: genetic algorithms, differential evolution (DE) (can be considered as a variation of the genetic algorithm), ant colony optimization (ACO) and particle swarm optimization (PSO). The basic ideas of the methods are described and a step-by-step algorithm for each is presented. The methods are usually discussed for the unconstrained optimization problem, assuming that the constraints can be included via a penalty function approach. The methods tend to converge into a global minimum point, although there is no guarantee. There is no precise stopping criterion for such algorithms; most compute function values at several thousand points and thus are computationally quite extensive. They can be implemented for parallel computations to speed up the search for an acceptable solution. Some examples are presented to illustrate use of some of the methods." @default.
- W139793179 created "2016-06-24" @default.
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- W139793179 date "2012-01-01" @default.
- W139793179 modified "2023-09-23" @default.
- W139793179 title "Nature-Inspired Search Methods" @default.
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- W139793179 doi "https://doi.org/10.1016/b978-0-12-381375-6.00028-0" @default.
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