An improved multi-objective grey wolf optimization algorithm with dynamic chaos local search mechanism

In recently years, the multi-objective grey wolf optimization (MOGWO) has been widely accepted to solve problems in various engineering fields because its less required parameters and excellent optimal performance. However, similar to many intelligent optimization algorithms, the grey wolf optimizat...

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Bibliographic Details
Published in:2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) Vol. 9; pp. 2020 - 2024
Main Author: GU, Wei
Format: Conference Proceeding
Language:English
Published: IEEE 11.12.2020
Subjects:
Benchmark testing
Chaos
chaos local search strategy
Conferences
dynamic adaptation strategy
Heuristic algorithm
Heuristic algorithms
Information technology
multi-objective grey wolf optimization
Optimization
Search problems
Online Access:Get full text
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Summary:In recently years, the multi-objective grey wolf optimization (MOGWO) has been widely accepted to solve problems in various engineering fields because its less required parameters and excellent optimal performance. However, similar to many intelligent optimization algorithms, the grey wolf optimization algorithm is easy to fall into local optimum. Due to the grey wolf algorithm is shortcomings, an improved multi-objective grey wolf optimization based on dynamic chaos local search mechanism (DC-MOGWO) is proposed. The proposed algorithm uses chaos local search strategy to improve the ability to jump out of local optimum. Furthermore, a dynamic adaptation strategy is used to improve the search efficiency and precision of the algorithm. The performance of DCL-MOGWO algorithm is compared with MOGWO and other bio-inspired techniques, the results show that its high performance in 6 well-known benchmark problems.
DOI:10.1109/ITAIC49862.2020.9338760