Three new stochastic local search algorithms for continuous optimization problems

Sivashan Chetty () and Aderemi Adewumi ()

Computational Optimization and Applications, 2013, vol. 56, issue 3, 675-721

Abstract: This paper introduces three new stochastic local search metaheuristics algorithms namely, the Best Performance Algorithm (BPA), the Iterative Best Performance Algorithm (IBPA) and the Largest Absolute Difference Algorithm (LADA). BPA and IBPA are based on the competitive nature of professional athletes, in them desiring to improve on their best recorded performances. LADA is modeled on calculating the absolute difference between two numbers. The performances of the algorithms have been tested on a large collection of benchmark unconstrained continuous optimization functions. They were benchmarked against two well-known local-search metaheuristics namely, Tabu Search (TS) and Simulated Annealing (SA). Results obtained show that each of the new algorithms delivers higher percentages of the best and mean function values found, compared to both TS and SA. The execution times of these new algorithms are also comparable. LADA gives the best performance in terms of execution time. Copyright Springer Science+Business Media New York 2013

Keywords: Best performance algorithm; Iterative best performance algorithm; Largest absolute difference algorithm; Tabu search; Simulated annealing; Unconstrained continuous optimization problems (search for similar items in EconPapers)
Date: 2013
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1007/s10589-013-9566-3 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:coopap:v:56:y:2013:i:3:p:675-721

Ordering information: This journal article can be ordered from
http://www.springer.com/math/journal/10589

DOI: 10.1007/s10589-013-9566-3

Access Statistics for this article

Computational Optimization and Applications is currently edited by William W. Hager

More articles in Computational Optimization and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().