Evolutionary MultiTasking Optimization （EMTO） is one of the new methods in evolutionary computing， which can simultaneously solve multiple related optimization tasks and enhance the optimization of each task through knowledge transfer between tasks. In recent years， more and more research on evolutionary multitasking optimization has been devoted to utilizing its powerful parallel search capability and potential for reducing computational costs to optimize various problems， and EMTO has been used in a variety of real-world scenarios. The researches and applications of EMTO were discussed from four aspects： principle， core design， applications， and challenges. Firstly， the general classification of EMTO was introduced from two levels and four aspects， including single-population multitasking， multi-population multitasking， auxiliary task， and multiform task. Next， the core component design of EMTO was introduced， including task construction and knowledge transfer. Finally， its various application scenarios were introduced and a summary and outlook for future research was provided.

To solve the problems of large channel fading damage， low resource utilization and security loss in high-spectrum short packet communications， a hybrid Intelligent Reflecting Surface （IRS） and relay assisted secure transmission scheme based on cooperative interference was proposed， which used artificial noise to interfere with the channel quality of eavesdroppers in the Multi-Input Single-Output （MISO） system to improve physical layer security. Firstly， the closed-form solution of the base station beamforming vector at the transmitter was derived to optimize the multi-antenna beamforming problem of the base station. Then， the Successive Convex Approximation （SCA） method was used to obtain the optimal allocation ratio of noise power， the gradient descent based Riemannian Manifold （RM） optimization method was used to obtain the optimal phase shift matrix， and the local optimal solutions of three sub-problems were solved respectively. Finally， an alternating optimization algorithm was used to iteratively obtain the global optimal solution. Simulation results show that the proposed algorithm has good convergence performance. When the number of IRS elements is 128， the secrecy rate of the proposed scheme is double of that of the IRS-only scheme and about triple as much as that of the relay-only scheme. In addition， when the location of IRS in the system is not fixed， the optimal power allocation scheme of the hybrid network has higher security performance.

As one of the core technologies of natural language processing， sentence embedding affects the quality and performance of natural language processing system. However， the existing methods are unable to infer the global semantic relationship between sentences efficiently， which leads to the fact that the semantic similarity measurement of sentences in Euclidean space still has some problems. To address the issue， a sentence embedding optimization method based on manifold learning was proposed. In the method， Local Linear Embedding （LLE） was used to perform double weighted local linear combinations to the sentences and their semantically similar sentences， thereby preserving the local geometric information between sentences and providing helps to the inference of the global geometric information. As a result， the semantic similarity of sentences in Euclidean space was closer to the real semantics of humans. Experimental results on seven text semantic similarity tasks show that the proposed method has the average Spearman’s Rank Correlation Coefficient， （SRCC） improved by 1.21 percentage points compared with the contrastive learning-based method SimCSE （Simple Contrastive learning of Sentence Embeddings）. In addition， the proposed method was applied to mainstream pre-trained models. The results show that compared to the original pre-trained models， the models optimized by the proposed method have the average SRCC improved by 3.32 to 7.70 percentage points.

In order to offer secrecy for multicast applications, the traffic encryption key has to be changed whenever a user joins or leaves the system. At present, many approaches based upon individual rekeying bring about the out-of-sync and inefficient problems. The paper proposed the batch rekeying based on queue model, which it would rekey when request to live reached zero by setting request to live to the first rekeying request in the queue. The results of analysis and experiment show that it can not only alleviate the out-of-sync and inefficient problems in the individual rekeying, but also decrease key server's rekeying cost and has better flexibility when compared with batch rekeying based on fixed period.