Multiobjective Design of 2D Hyperchaotic System Using Leader Pareto Grey Wolf Optimizer

A chaotic system is a mathematical model exhibiting random and unpredictable behavior. However, existing chaotic systems suffer from suboptimal parameters regarding chaotic indicators. In this study, a novel leader Pareto grey wolf optimizer (LP-GWO) is proposed for multiobjective (MO) design of 2D...

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Vydané v:IEEE transactions on systems, man, and cybernetics. Systems Ročník 54; číslo 9; s. 5237 - 5247
Hlavní autori: Toktas, Abdurrahim, Erkan, Ugur, Ustun, Deniz, Lai, Qiang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 01.09.2024
Predmet:
Chaos
Chaotic communication
Chaotic system
grew wolf optimizer
Heuristic algorithms
Linear programming
modified algorithm
multiobjective (MO) optimization
Optimization
Pareto optimality
Pareto optimization
Vectors
ISSN:2168-2216, 2168-2232
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Shrnutí:A chaotic system is a mathematical model exhibiting random and unpredictable behavior. However, existing chaotic systems suffer from suboptimal parameters regarding chaotic indicators. In this study, a novel leader Pareto grey wolf optimizer (LP-GWO) is proposed for multiobjective (MO) design of 2D parametric hyperchaotic system (2D-PHS). The MO capability of LP-GWO is improved by integrating a LP solution within the Pareto optimal set. The effectiveness of LP-GWO is corroborated through a comparison with regular MO versions of grey wolf optimizer (GWO), artificial bee colony, particle swarm optimization, and differential evolution. Additionally, the validation extends to the exploration of LP-GWO's performance across four variants of the 2D-PHS optimized by the compared algorithms. A 2D-PHS model with eight parameters is conceived and then optimized using LP-GWO by ensuring tradeoff between two objectives: Lyapunov exponent (LE) and Kolmogorov entropy (KE). A globally optimal design is chosen for freely improving the two objectives. The chaotic performance of 2D-PHS significantly outperforms existing systems in terms of precise chaos indicators. Therefore, the 2D-PHS has the best ergodicity and erraticity due to optimal parameters provided by LP-GWO.
ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2024.3401412