文章基本信息

标题：A novel hybrid backtracking search optimization algorithm for continuous function optimization
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作者：Nama, S. ; Saha, A.
期刊名称：Decision Science Letters
印刷版ISSN：1929-5804
电子版ISSN：1929-5812
出版年度：2019
卷号：8
期号：2
页码：163-174
DOI：10.5267/j.dsl.2018.7.002
语种：English
出版社：Growing Science Publishing Company
摘要：Stochastic optimization algorithm provides a robust and efficient approach for solving complex real world problems. Backtracking Search Optimization Algorithm (BSA) is a new stochastic evolutionary algorithm and the aim of this paper is to introduce a hybrid approach combining the BSA and Quadratic approximation (QA), called HBSAfor solving unconstrained non-linear, non-differentiable optimization problems. For the validity of the proposed method the results are compared with five state-of-the-art particle swarm optimization (PSO) variant approaches in terms of the numerical result of the solutions. The sensitivity analysis of the BSA control parameter (F) is also performed.
关键词：Backtracking Search Optimization Algorithm (BSA);Quadratic approximation (QA);Hybrid Algorithm;Unconstrained non-linear function optimization