Large-scale multi-objective optimization problems (LSMOPs) face computational challenges due to high-dimensional search spaces and variable correlations. Existing algorithms often struggle to balance convergence, diversity, and efficiency in high-dimensional scenarios. We propose ATSPCA, a two-stage adaptive algorithm employing PCA-guided perturbation. The first stage uses PCA-based dynamic variable screening to identify key decision variable groups, applying adaptive Gaussian perturbation to guide optimization. The second stage enhances convergence and diversity through an improved competitive swarm optimizer (CSO) and reference pointbased environmental selection. An adaptive transition mechanism bridges the two stages. Tested on LSMOP and WFG benchmarks against state-of-the-art large-scale multi-objective evolutionary algorithms(LSMOEAs), ATSPCA demonstrates superior performance.
This study addresses the Container Stowage Planning Problem (CSPP) from a perspective of vessel-yard collaboration. A bi-level multi-objective evolutionary algorithm based on NSGA-II is proposed, in which the upper-level assigns the containers to bays and the lower-level decides specific position for each container. The algorithm employs adaptive evolutionary strategies to enhance diversity. The result of extensive experiments demonstrates the superiority of the proposed method and practical value for real-world scale CSPP in container terminal.
Community detection in social networks plays a vital role in understanding structural influence and optimizing promotional strategies. This study proposes a capacity-constrained community detection problem (CCDP) model to identify the most influential community under practical limitations of promotion opportunities. Given the NP-hard nature of the problem, an efficient memetic algorithm is developed. Extensive experiments demonstrate its superiority over CPLEX and several heuristic and community detection approaches. Further analysis on convergence, parameter sensitivity, and crossover operator provides deeper insights into the algorithm’s performance.
The successful manipulation of a robotic arm is crucial in industry, in surgical fields, or in space exploration. Many previous studies on evolutionary or genetic algorithms successfully determined effective configurations of arm joints in order to reach a specific target. Nevertheless, most existing methods face challenges in obstacle avoidance, as it might be required to find an extremely specific and not direct result to surpass a certain obstacle, causing local maxima to be found. This paper proposes the use of a known path-finding method such as the A* Search algorithm establish an optimized route for the arm to extend over, before the evolutionary algorithm is applied to determine the configurations of arm joints. Specifically, the process starts with a predefined scenario containing both the target, the starting point and a set of obstacles. The A* algorithm is then used to map a route, which is divided into a set of targets that the robotic arm must follow to reach its final destination. The evaluation function used in this project ensures that proximity to obstacles negatively affects individual fitness. The experimental findings show that this strategy is able to find a way around local maxima quickly and outperform previous purely evolutionary approaches in both 2D and 3D simulated environments.
In recent years, surrogate-assisted evolutionary algorithms (SAEAs) have been extensively utilized to address expensive optimization problems. However, it becomes a great challenge how to train the surrogate using the dynamic data distributed across multiple clients due to privacy and security concerns. To address this issue, this paper proposes a meta-learning-based federated surrogate-assisted evolutionary algorithm for dynamic expensive optimization problems. The proposed algorithm utilizes a gradient-based meta-training algorithm to extract the experiences of historical optimization environments as the initial surrogate parameters in the new environment and then utilizes limited data samples for fine-tuning to achieve fast surrogate adaptation in the new environment. In addition, we utilize differential privacy techniques to add artificial noise to the parameters of the global and local surrogates to protect the private information of clients. Experimental results on a set of dynamic optimization benchmark problems with different settings show that the proposed federated surrogate-assisted evolutionary algorithm can achieve superior optimization performance while protecting data privacy.
