Path planning is one of the hotspots in the research of automotive engineering. Aiming at the issue of robot path planning with the goal of finding a collision-free optimal motion path in an environment with barriers, this study proposes an adaptive parallel arithmetic optimization algorithm (APAOA) with a novel parallel communication strategy. Comparisons with other popular algorithms on 18 benchmark functions are committed. Experimental results show that the proposed algorithm performs better in terms of solution accuracy and convergence speed, and the proposed strategy can prevent the algorithm from falling into a local optimal solution. Finally, we apply APAOA to solve the problem of robot path planning.

Improving localization performance is one of the critical issues in Wireless Sensor Networks (WSN). As a range-free localization algorithm, Distance Vector-Hop(DV-Hop) is well-known for its simplicity but is hindered by its low accuracy and poor stability. Therefore, it is necessary to improve DV-Hop to achieve a competitive performance. However, the comprehensive performance of WSN is limited by computing and storage capabilities of sensor nodes. In this paper, we propose an algorithm with parallel and compact techniques based on Whale Optimization Algorithm (PCWOA) to improve DV-Hop performance. The compact technique saves memory consumption by reducing the original population. The parallel techniques enhance the ability to jump out of local optimization and improve the solution accuracy. The proposed algorithm is tested on CEC2013 benchmark functions and compared with some popular algorithms and compact algorithms. Experimental results show that the improved algorithm achieves competitive results over compared algorithms. Finally, simulation research is conducted to verify the localization performance of our proposed algorithm.

High-dimensional optimization problems face challenges from exponentially expanding search spaces and deceptive local optima resisting metaheuristics. In order to address these issues, a reinforcement learning-guided Animated Oat Optimization Algorithm with a dynamic niching strategy, called RLDN-AOO, is proposed in this research. RLDN-AOO offers the following major novelties: i) a mathematically formulated three-state dynamic niching mechanism that adaptively partitions the population, preserves diversity, and enhances the algorithm's ability to escape local optima, and ii) a reinforcement learning strategy selection mechanism is proposed to address the issue of the algorithm's inadequate dynamic adaptability. We compared it with state-of-the-art algorithms (CEC2017, Dim = 50, 100, 200, 500), including LSHADE-SPACMA, CMA-ES variants, and RL-based optimizers. In addition, we applied it in the optimization of BP neural networks. Experimental results showed that RLDN-AOO achieves competitive performance across most benchmarks and, in some cases, performs comparably to LSHADE-SPACMA variants. The source code of RLDN-AOO is openly accessible via https://github.com/robingit77/RLDN-AOO.