Paper Info
Title
Comparison of multistart global optimization algorithms on the BBOB noiseless testbed
Abstract
Multi Level Single Linkage is a multistart, stochastic global optimization method which relies on random sampling and local search. In this paper, we benchmarked three variants of the MLSL algorithm by using two gradient based and a derivative-free local search method on the noiseless function testbed. The three methods were also compared with a commercial multistart solver, called OQNLP (OptQuest/NLP). Our experiment showed that, the results may be influenced essentially by the applied local search procedure. Depending of the type of the problem the gradient based local search methods are faster in the initial stage of the optimization, while the derivative-free method show a superior performance in the final phase for moderate dimensions. Considering the percentage of the solved problems, OQNLP is similar or even better (for multi-modal and weakly structured functions) in 5-D than the MLSL method equipped with the gradient type local search methods, while on 20-D the latter algorithms are usually more faster.
Year
DOI
Venue
2013
10.1145/2464576.2482693
GECCO (Companion)
Keywords
Field
DocType
multistart global optimization algorithm,mlsl method,mlsl algorithm,local search method,derivative-free method,local search procedure,stochastic global optimization method,derivative-free local search method,bbob noiseless testbed,local search,gradient type,commercial multistart solver,benchmarking
Mathematical optimization,Global optimization,Computer science,Testbed,Algorithm,Sampling (statistics),Artificial intelligence,Local search (optimization),Solver,Machine learning,Benchmarking,Single Linkage
Conference
Citations 
PageRank 
References 
0
0.34
4
Authors
1
Name
Order
Citations
PageRank
László Pál1594.78