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- W2109641505 abstract "This chapter aims to address the challenging and demanding issue of solving a continuous nonlinear constrained global optimization problem. We propose four stochastic methods that rely on a population of points to diversify the search for a global solution: genetic algorithm, differential evolution, artificial fish swarm algorithm and electromagnetism-like mechanism. The performance of different variants of these algorithms is analyzed using a benchmark set of problems. Three different strategies to handle the equality and inequality constraints of the problem are addressed. An augmented Lagrangian-based technique, the tournament selection based on feasibility and dominance rules, and a strategy based on ranking objective and constraint violation are presented and tested. Numerical experiments are reported showing the effectiveness of our suggestions. Two well-known engineering design problems are successfully solved by the proposed methods." @default.
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- W2109641505 date "2013-01-01" @default.
- W2109641505 modified "2023-10-16" @default.
- W2109641505 title "On Challenging Techniques for Constrained Global Optimization" @default.
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- W2109641505 doi "https://doi.org/10.1007/978-3-642-30504-7_26" @default.
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