Modified Backtracking Search Optimization Algorithm Inspired by Simulated Annealing for Constrained Engineering Optimization Problems

Joint Authors

Hu, Zhongbo
Xia, Xuewen
Wang, Hailong
Sun, Yuqiu
Su, Qinghua

Source

Computational Intelligence and Neuroscience

Issue

Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-27, 27 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2018-02-13

Country of Publication

Egypt

No. of Pages

27

Main Subjects

Biology

Abstract EN

The backtracking search optimization algorithm (BSA) is a population-based evolutionary algorithm for numerical optimization problems.

BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor.

This affects the convergence speed of the algorithm.

In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA) to overcome the deficiency of BSA.

In the BSAISA, the amplitude control factor (F) is modified based on the Metropolis criterion in simulated annealing.

The redesigned F could be adaptively decreased as the number of iterations increases and it does not introduce extra parameters.

A self-adaptive ε-constrained method is used to handle the strict constraints.

We compared the performance of the proposed BSAISA with BSA and other well-known algorithms when solving thirteen constrained benchmarks and five engineering design problems.

The simulation results demonstrated that BSAISA is more effective than BSA and more competitive with other well-known algorithms in terms of convergence speed.

American Psychological Association (APA)

Wang, Hailong& Hu, Zhongbo& Sun, Yuqiu& Su, Qinghua& Xia, Xuewen. 2018. Modified Backtracking Search Optimization Algorithm Inspired by Simulated Annealing for Constrained Engineering Optimization Problems. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-27.
https://search.emarefa.net/detail/BIM-1130849

Modern Language Association (MLA)

Wang, Hailong…[et al.]. Modified Backtracking Search Optimization Algorithm Inspired by Simulated Annealing for Constrained Engineering Optimization Problems. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-27.
https://search.emarefa.net/detail/BIM-1130849

American Medical Association (AMA)

Wang, Hailong& Hu, Zhongbo& Sun, Yuqiu& Su, Qinghua& Xia, Xuewen. Modified Backtracking Search Optimization Algorithm Inspired by Simulated Annealing for Constrained Engineering Optimization Problems. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-27.
https://search.emarefa.net/detail/BIM-1130849

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1130849