EBCA: an Enhanced Besiege and Conquer Algorithm with Rapid Strategy Change and Targeted Disruption Formation

The Besiege and Conquer Algorithm (BCA) is a novel metaheuristic inspired by guerrilla warfare. However, BCA suffers from several limitations, including stagnation at local optima, inadequate information utilization, and reduced population diversity. In this paper, we propose the Enhanced Besiege and Conquer Algorithm (EBCA) to address these issues through two key improvements. First, Rapid Strategy Change, incorporating the Judge, Spread, and Migration mechanisms, is introduced to enhance the algorithm's ability to escape local minima and accelerate convergence. Second, Targeted Disruption Formation, involving the Target Selection and Disruption Formation mechanisms, is designed to mitigate the risk of excessive search space boundary crossing and promote the generation of diverse solutions, thereby improving population diversity. The performance of EBCA is evaluated using 29 benchmark functions from IEEE CEC 2017 and 12 benchmark functions from IEEE CEC 2022, and compared with 15 other algorithms (BCA, LEA, HOA, FLO, HO, ARO, COA, DE, JADE, SHADE, L\_SHADE, PSO, APSO, CLPSO, ELPSO). Additionally, the algorithm is tested on four engineering design problems to demonstrate its practical applicability. The experimental results, supported by the Wilcoxon Signed-Rank test, show that EBCA outperforms many of the competing algorithms, particularly in high-dimensional and complex problems. These results highlight EBCA's strong competitiveness and make it a promising candidate for addressing challenges in swarm intelligence optimization.