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.

In this work, we present a new construction method for reversible codes. We employ composite matrices derived from group rings and show how to construct these matrices so that they are also reversible. Also in this work, we give an algorithm for calculating conflict free DNA codes that satisfy the Hamming distance, the reverse, the reverse-complement, the GC-content constraints with each DNA codeword being free from reverse complement sub-strings. By employing our construction method for reversible codes and our algorithm, we construct a number of DNA codes that satisfy the above constraints. Many of the codes we obtain have better parameters than some known DNA codes and many have parameters that are new to the literature.

Additive codes have become an increasingly important topic in algebraic coding theory due to their applications in quantum error-correction and quantum computing. Linear Complementary Dual (LCD) codes play an important role for improving the security of information against certain attacks. Motivated by these facts, we define additive complementary dual codes (ACD for short) over a finite abelian group in terms of an arbitrary duality on the ambient space and examine their properties. We show that the best minimum weight of ACD codes is always greater than or equal to the best minimum weight of LCD codes of the same size and that this inequality is often strict. We give some matrix constructions for quaternary ACD codes from a given quaternary ACD code and also from a given binary self-orthogonal code. Moreover, we construct an algorithm to determine if a given quaternary additive code is an ACD code with respect to all possible symmetric dualities. We also determine the largest minimum distance of quaternary ACD codes for lengths$n \leq 10$. The obtained codes are either optimal or near optimal according to Bierbraueret al+. (2009).

Inverse synthetic aperture radar (ISAR) image is constructed using 2-D spatial distributions of the radar cross section of a target. ISAR data gathered from moving targets might include interphase error that causes a blurry effect in the ISAR image. In this article, an efficient motion compensation (MC) scheme depending on multicriteria decision using surrogate-based optimization (SbO) for minimizing the entropy and maximizing the sharpness of the images is proposed to remove the blur from the images. In order to provide a multicriteria decision, Pareto optimality is exploited to balance two criteria of the entropy and sharpness synchronously. A signal with an interphase error is input to the MC system for determining the global optimal motion parameters of the velocity and acceleration so as to focus on the ISAR image. The proposed scheme is implemented to four simulated ISAR scenarios reported elsewhere through two aircraft models for performance demonstration and comparison with artificial bee colony (ABC), differential evolution (DE), and particle swarm optimization (PSO) implemented in the literature. It is pointed out that the proposed scheme is more successful and efficient in view of the image focusing quality as well as the numerical results such as the motion parameters, the entropy and sharpness, and structural similarity (SSIM) index. The results also show that the SbO outperforms the other methods as the velocity and acceleration increase.