Hengyang Normal University

About: Hengyang Normal University is a education organization based out in Hengyang, China. It is known for research contribution in the topics: Graphene & Adsorption. The organization has 1087 authors who have published 1280 publications receiving 13850 citations. The organization is also known as: Hengyang Teachers' College & Héngyáng Shīfàn Xuéyuàn.

A dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution

Abstract: Many multi-objective optimization problems in the real world are dynamic, with objectives that conflict and change over time. These problems put higher demands on the algorithm’s convergence performance and the ability to respond to environmental changes. Confronting these two points, this paper proposes a dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution (ADNEPSO). To overcome the instability of the traditional decomposition method for the changing Pareto optimal front (POF) shape, the proposed algorithm utilizes the complementary characteristics in the search area of the adversarial vector, and the two populations are alternately updated and co-evolved by adversarial search directions. Additionally, a novel particle update strategy is proposed to select promising neighborhood information to guide evolution and enhance diversity. To improve the ability to cope with environmental changes, an effective dynamic response mechanism is proposed, including three parts: archive set prediction, exploration of global optimal information, and retention of excellent particles to accelerate convergence to the Pareto optimal set (POS) in the new environment. The proposed algorithm is tested on a series of benchmark problems and compared to several state-of-the-art algorithms. The results show that ADNEPSO performed excellently in both convergence and diversity, and is highly competitive in dealing with dynamic problems.

Loong: A Family of Involutional Lightweight Block Cipher Based on SPN Structure

Abstract: In past few years, as security ciphers in the Internet of Things (IoT), the research of lightweight block cipher has attracted tremendous attention in cryptography. The SPN structure has been widely used in the design of block cipher. However, the encryption and decryption processes of ciphers based on the SPN structure are different. We design a new SPN structure, which is perfect for lightweight block cipher. The new SPN structure makes that the encryption process is the same as decryption. Moreover, input and output data directions are the same for encryption and decryption processes. Thus, the same process can absolutely be shared in decryption and encryption both for software and hardware implementation. Further, we propose a family of involutional lightweight block cipher, called Loong, based on the proposed SPN structure and components. Rigorous analysis indicates that Loong is of high security against cryptanalysis, especially the differential attack and linear attack. As shown by our experiments and comparisons, Loong is compact in hardware environment and is suitable for the IoT.