Solving multimodal optimization problems (MMOPs) is a challenging task which needs locating multiple global optimal solutions simultaneously with high accuracy. Current popular niching-based evolutionary algorithms (EAs) for solving MMOPs usually divide the population into several separate species to search for different optimal solutions. However, achieving effective information exchange between species to enhance the performance of overall algorithm remains a challenge in current niching-based EAs, which will directly affect the efficiency of the multimodal optimization algorithm. In this paper, the process of the different species locating peaks in MMOPs is regarded as an evolutionary multitask (EMT) optimization problem and an EMT framework with bi-knowledge transfer for MMOPs is proposed. An explicit knowledge transfer (E-KT) strategy is designed to transfer the optimal individual of the species with the fastest convergence speed to other species, thereby facilitating the acceleration their convergence. Moreover, in order to further improve the information exchange between species, a species center-based implicit knowledge transfer (I-SCKT) strategy is designed to improve the diversity of the population. The performance of MTBKTMMOP is tested on the widely used CEC’2013 benchmark and five practical flexible job shop problems. The experimental results of MTBKTMMOP are compared with 9 state-of-the-art MMOPs algorithms and show that our MTBKTMMOP is superior to all of them. Besides, the experimental results also show that the MTBKTMMOP achieves breakthroughs in handling with a large number of optimal solutions or high-dimensional MMOPs, which provides a new and effective method for dealing with MMOPs.

In the realm of mobile robotics, the capability to navigate and map uncharted territories is paramount, and Simultaneous Localization and Mapping (SLAM) stands as a cornerstone technology enabling this capability. While traditional SLAM methods like Extended Kalman Filter (EKF) and FastSLAM have made strides, they often struggle with the complexities of non-linear dynamics and non-Gaussian noise, particularly in dynamic settings. Moreover, these methods can be computationally intensive, limiting their applicability in real-world scenarios. This paper introduces an innovative enhancement to the FastSLAM framework by integrating Multi-Objective Particle Swarm Optimization (MO-PSO), aiming to bolster the robustness and accuracy of SLAM in mobile robots. We outline the theoretical underpinnings of FastSLAM and underscore its significance in robotic autonomy for mapping and exploration. Our approach innovates by crafting a specialized fitness function within the MO-PSO paradigm, which is instrumental in optimizing the particle distribution and addressing the challenges inherent in traditional particle filtering methods. This strategic fusion of MO-PSO with FastSLAM not only circumvents the pitfalls of particle degeneration, but also enhances the overall robustness and precision of the SLAM process across a spectrum of operational environments. Our empirical evaluation involves testing the proposed method on three distinct simulation benchmarks, comparing its performance against four other algorithms. The results indicate that our MO-PSO-enhanced FastSLAM method outperforms the traditional particle filtering approach by significantly reducing particle degeneration and ensuring more reliable and precise SLAM performance in challenging environments. This research demonstrates that the integration of MO-PSO with FastSLAM is a promising direction for improving SLAM in mobile robots, providing a robust solution for accurate mapping and localization even in complex and unknown settings.

Deep learning models are inherently vulnerable to adversarial examples, particularly in black-box settings where attackers have limited knowledge of the target model. Existing attack algorithms often face challenges in balancing effectiveness and efficiency. Adversarial perturbations generated in such settings can be suboptimal and require large query budgets to achieve high success rates. In this paper, we investigate the transferability of Multiple Universal Adversarial Perturbations (MUAPs), showing that they can affect a large portion of samples across different models. Based on this insight, we propose SMPack, a staged black-box adversarial example generation algorithm that integrates surrogate and query schemes. By combining MUAPs with surrogate models, SMPack effectively overcomes the black-box constraints and improves the efficiency of generating adversarial examples. Additionally, we optimize this process using a Genetic Algorithm (GA), allowing for efficient search of the perturbation space while conserving query budget. We evaluated SMPack against eight popular attack algorithms: OnePixel, SimBA, FNS, GA, SFGSM, SPGD, FGSM, and PGD, using four publicly available datasets: MNIST, SVHN, CIFAR-10, and ImageNet. The experiments involved 500 random correctly classified samples for each dataset. Our results show that SMPack outperforms existing black-box attack methods in both attack success rate (ASR) and query efficiency, while maintaining competitive performance with white-box methods. SMPack provides an efficient and effective solution for generating adversarial examples in black-box settings. The integration of MUAPs, surrogate schemes, and genetic optimization addresses the key limitations of existing methods, offering a robust alternative for generating adversarial perturbations with reduced query budget.