NDLI: Hybridizing the Electromagnetism-like algorithm with Descent Search for Solving Engineering Design Problems ( SI CMMSE 08 )

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Hybridizing the Electromagnetism-like algorithm with Descent Search for Solving Engineering Design Problems ( SI CMMSE 08 )

Content Provider	Semantic Scholar
Copyright Year	2009
Abstract	In this paper, we present a new stochastic hybrid technique for constrained global optimization. It is a combination of the electromagnetism-like (EM) mechanism with a random local search, which is a derivative-free procedure with high ability of producing a descent direction. Since the original EM algorithm is specifically designed for solving bound constrained problems, the approach herein adopted for handling the inequality constraints of the problem relies on selective conditions that impose a sufficient reduction either in the constraints violation or in the objective function value, when comparing two points at a time. The hybrid EM method is tested on a set of benchmark engineering design problems and the numerical results demonstrate the effectiveness of the proposed approach. A comparison with results from other stochastic methods is also included.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://repositorium.sdum.uminho.pt/bitstream/1822/9670/1/IJCM_09.pdf
Language	English
Access Restriction	Open
Subject Keyword	Algorithmic efficiency Approximation algorithm Backtracking line search Benchmark (computing) Computation Constrained optimization Descent direction Electromagnetic Radiation Engineering design process Entity Name Part Qualifier - adopted Evaluation Expectation–maximization algorithm Experiment Financial cost Flavin-Adenine Dinucleotide Global optimization Handling (Psychology) Hybrid algorithm Integer (number) International System of Units Local search (optimization) Loss function Mathematical optimization Numerical analysis Optimization problem Penalty method Population Parameter Small Social inequality Solutions Stochastic gradient descent travel
Content Type	Text
Resource Type	Article

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