Comprehensive learning Jaya algorithm for engineering design optimization problems

Jaya algorithm (JAYA) is a recently developed metaheuristic algorithm for global optimization problems. JAYA has a very simple structure and only needs the essential population size and terminal condition for solving optimization problems. However, JAYA is easy to get trapped in the local optimum fo...

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Bibliographic Details
Published in:Journal of intelligent manufacturing Vol. 33; no. 5; pp. 1229 - 1253
Main Authors: Zhang, Yiying, Jin, Zhigang
Format: Journal Article
Language:English
Published: New York Springer US 01.06.2022
Springer Nature B.V
Subjects:
Advanced manufacturing technologies
Algorithms
Business and Management
Control
Design engineering
Design optimization
Global optimization
Heuristic methods
Machine learning
Machines
Manufacturing
Mechatronics
Population number
Processes
Production
Robotics
Source code
Jaya algorithm
Metaheuristic algorithm
Comprehensive learning
Engineering optimization
ISSN:0956-5515, 1572-8145
Online Access:Get full text
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Summary:Jaya algorithm (JAYA) is a recently developed metaheuristic algorithm for global optimization problems. JAYA has a very simple structure and only needs the essential population size and terminal condition for solving optimization problems. However, JAYA is easy to get trapped in the local optimum for solving complex global optimization problems due to its single learning strategy. Motivated by this disadvantage of JAYA, this paper presents an improved JAYA, named comprehensive learning JAYA algorithm (CLJAYA), for solving engineering design optimization problems. The core idea of CLJAYA is the designed comprehensive learning mechanism by making full use of population information. The designed comprehensive learning mechanism consists of three different learning strategies to improve the global search ability of JAYA. To investigate the performance of CLJAYA, CLJAYA is first evaluated by the well-known CEC 2013 and CEC 2014 test suites, which include 50 multimodal test functions and eight unimodal test functions. Then CLJAYA is employed to solve five real-world engineering optimization problems. Experimental results demonstrate that CLJAYA can achieve better solutions for most test problems than JAYA and the other compared algorithms, which indicates the designed comprehensive learning mechanism is very effective. In addition, the source code of the proposed CLJAYA can be loaded from https://www.mathworks.com/matlabcentral/fileexchange/82134-the-source-code-for-cljaya .
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ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-020-01723-6