An Empirical Study of Dynamic Triobjective Optimisation Problems

Jiang, Shouyong, Kaiser, M, Wan, S , Guo, J, Yang, S and Krasnogor, N (2018) An Empirical Study of Dynamic Triobjective Optimisation Problems. 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings .

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Dynamic multiobjective optimisation deals with multiobjective problems whose objective functions, search spaces, or constraints are time-varying during the optimisation process. Due to wide presence in real-world applications, dynamic multiobjective problems (DMOPs) have been increasingly studied in recent years. Whilst most studies concentrated on DMOPs with only two objectives, there is little work on more objectives. This paper presents an empirical investigation of evolutionary algorithms for three-objective dynamic problems. Experimental studies show that all the evolutionary algorithms tested in this paper encounter performance degradedness to some extent. Amongst these algorithms, the multipopulation based change handling mechanism is generally more robust for a larger number of objectives, but has difficulty in deal with time-varying deceptive characteristics. © 2018 IEEE.

Additional Information:cited By 0; Conference of 2018 IEEE Congress on Evolutionary Computation, CEC 2018 ; Conference Date: 8 July 2018 Through 13 July 2018; Conference Code:140374
Keywords:Multiobjective optimization, Dynamic multi-objective problems, Dynamic problem, Empirical investigation, Empirical studies, Multi population, Multi-objective problem, Objective functions, Optimisation problems, Evolutionary algorithms
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:35659
Deposited On:17 Apr 2019 12:01

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