Opposition-based learning (OBL) is an optimization method widely applied to algorithms. Through analysis, it has been found that different variants of OBL demonstrate varying performance in solving different problems, which makes it crucial for multiple OBL strategies to co-optimize. Therefore, this study proposed a dynamic allocation of OBL in differential evolution for multi-role individuals. Before the population update in DAODE, individuals in the population played multiple roles and were stored in corresponding archives. Subsequently, different roles received respective rewards through a comprehensive ranking mechanism based on OBL, which assigned an OBL strategy to maintain a balance between exploration and exploitation within the population. In addition, a mutation strategy based on multi-role archives was proposed. Individuals for mutation operations were selected from the archives, thereby influencing the population to evolve toward more promising regions. Experimental results were compared between DAODE and state of the art algorithms on the benchmark suite presented at the 2017 IEEE conference on evolutionary computation (CEC2017). Furthermore, statistical tests were conducted to examine the significance differences between DAODE and the state of the art algorithms. The experimental results indicated that the overall performance of DAODE surpasses all state of the art algorithms on more than half of the test functions. Additionally, the results of statistical tests also demonstrated that DAODE consistently ranked first in comprehensive ranking.

Path planning for Unmanned Aerial Vehicles (UAVs) in complex environments presents several challenges. Traditional algorithms often struggle with the complexity of high-dimensional search spaces, leading to inefficiencies. Additionally, the non-linear nature of cost functions can cause algorithms to become trapped in local optima. Furthermore, there is often a lack of adequate consideration for real-world constraints, for example, due to the necessity for obstacle avoidance or because of the restrictions of flight safety. To address the aforementioned issues, this paper proposes a dynamic weighted spherical particle swarm optimization (DW-SPSO) algorithm. The algorithm adopts a dual Sigmoid-based adaptive weight adjustment mechanism for balancing global exploration and local exploitation, as well as a lens-based opposition learning one to improve search flexibility and solution diversity. Simulation experiments on real digital elevation models demonstrate that DW-SPSO significantly outperforms recent state-of-the-art particle swarm optimization (PSO) variants in terms of path safety, smoothness, and convergence speed. The performance superiority is statistically validated by the Wilcoxon signed-rank test. The results confirm the algorithm’s effectiveness in generating high-quality UAV paths under diverse threat conditions, offering a robust solution for autonomous navigation systems.

Feature selection serves as a critical preprocessing step in machine learning, focusing on identifying and preserving the most relevant features to improve the efficiency and performance of classification algorithms. Particle Swarm Optimization has demonstrated significant potential in addressing feature selection challenges. However, there are inherent limitations in Particle Swarm Optimization, such as the delicate balance between exploration and exploitation, susceptibility to local optima, and suboptimal convergence rates, hinder its performance. To tackle these issues, this study introduces a novel Leveraged Opposition-Based Learning method within Fitness Landscape Particle Swarm Optimization, tailored for wrapper-based feature selection. The proposed approach integrates: (1) a fitness-landscape adaptive strategy to dynamically balance exploration and exploitation, (2) the lever principle within Opposition-Based Learning to improve search efficiency, and (3) a Local Selection and Re-optimization mechanism combined with random perturbation to expedite convergence and enhance the quality of the optimal feature subset. The effectiveness of is rigorously evaluated on 24 benchmark datasets and compared against 13 advanced metaheuristic algorithms. Experimental results demonstrate that the proposed method outperforms the compared algorithms in classification accuracy on over half of the datasets, whilst also significantly reducing the number of selected features. These findings demonstrate its effectiveness and robustness in feature selection tasks.

The vehicle routing problem with time windows (VRPTW) is a classical NP-hard combinatorial optimization problem, where NP denotes nondeterministic polynomial time, and it plays a critical role in modern logistics and transportation systems. Although competitive swarm optimization (CSO) algorithms have demonstrated strong performance in continuous optimization, their effective application to discrete combinatorial problems such as VRPTW remains challenging. In this paper, a hybrid competitive swarm optimization and tabu search algorithm (CSO-TS) is proposed to solve the VRPTW. To enhance the search capability of the CSO framework, a tabu search mechanism with an adaptive neighborhood operation strategy is integrated. Moreover, to achieve a better balance between exploration and exploitation, a path diversity index is introduced to quantitatively evaluate solution diversity based on four distinct indices. The proposed CSO-TS algorithm is tested on 56 Solomon benchmark instances and obtains 23 optimal solutions. Extensive computational experiments and comparative analyses demonstrate that CSO-TS outperforms or is competitive with nine state-of-the-art algorithms in terms of solution quality.