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 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.