UMP Institutional Repository

A hybrid SP-QPSO algorithm with parameter free adaptive penalty method for constrained global optimization problems

Fatemeh, D. B. and Loo, C. K. and Kanagaraj, G. and Ponnambalam, S. G. (2018) A hybrid SP-QPSO algorithm with parameter free adaptive penalty method for constrained global optimization problems. Journal of Modern Manufacturing Systems and Technology, 1. pp. 15-26. ISSN 2636-9575

[img]
Preview
Pdf
A hybrid SP-QPSO algorithm with parameter.pdf

Download (1MB) | Preview

Abstract

Most real-life optimization problems involve constraints which require a specialized mechanism to deal with them. The presence of constraints imposes additional challenges to the researchers motivated towards the development of new algorithm with efficient constraint handling mechanism. This paper attempts the suitability of newly developed hybrid algorithm, Shuffled Complex Evolution with Quantum Particle Swarm Optimization abbreviated as SP-QPSO, extended specifically designed for solving constrained optimization problems. The incorporation of adaptive penalty method guides the solutions to the feasible regions of the search space by computing the violation of each one. Further, the algorithm’s performance is improved by Centroidal Voronoi Tessellations method of point initialization promise to visit the entire search space. The effectiveness and the performance of SP-QPSO are examined by solving a broad set of ten benchmark functions and four engineering case study problems taken from the literature. The experimental results show that the hybrid version of SP-QPSO algorithm is not only overcome the shortcomings of the original algorithms but also outperformed most state-of-the-art algorithms, in terms of searching efficiency and computational time.

Item Type: Article
Uncontrolled Keywords: Constrained optimization, Hybrid algorithm, Swarm intelligence, Penalty method
Subjects: T Technology > TS Manufactures
Faculty/Division: Faculty of Manufacturing Engineering
Depositing User: Noorul Farina Arifin
Date Deposited: 25 Sep 2018 05:07
Last Modified: 12 Nov 2018 01:44
URI: http://umpir.ump.edu.my/id/eprint/22212
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item