BLOMBO: A HYBRID METAHEURISTIC ALGORITHM BASED ON BEES LIFE OPTIMIZATION AND MIGRATING BIRDS OPTIMIZATION WITH A QUANTUM-INSPIRED EXTENSION

Abstract

Nature-inspired metaheuristic algorithms have demonstrated strong capability in solving complex nonlinear optimization problems; however, achieving
an effective balance between exploration and exploitation remains a critical challenge. In this study, a novel hybrid metaheuristic algorithm, BLOMBO (Bees Life
Optimization and Migrating Birds Optimization), is proposed to address this issue. The algorithm integrates the local exploitation strength of Bees Life
Optimization (BLO) with the cooperative exploration mechanism of Migrating Birds Optimization (MBO) within a unified framework.
To evaluate its performance, BLOMBO is tested on five well-known benchmark functions: Sphere, Rastrigin, Ackley, Rosenbrock, and Griewank. The results are
compared with the standalone BLO and MBO algorithms in terms of convergence speed, solution quality, and computational efficiency. Experimental results
indicate that BLOMBO achieves competitive and stable performance across all benchmark functions, often providing improved convergence behavior and
maintaining a strong balance between exploration and exploitation.These findings suggest that the proposed hybrid approach is a promising alternative for solving
complex optimization problems, particularly in scenarios requiring both global search capability and local refinement.