Multi strategy Improved Enterprise Development Optimizer for Optimization Tasks

The multi-strategy improved enterprise development algorithm (MIED) is proposed to address the limitations of the original Enterprise Development (ED) optimizer, which suffers from proneness to local optima and low convergence accuracy in complex optimization tasks. MIED integrates four key strategies: a new worst individual update method for task activities to reduce randomness, a global optimal-guided local search formula for structure activities to enhance exploitation, a novel search formula with adaptive dynamic convergence factors for technology activities to balance exploitation and exploration, and a cellular topological structure-based search formula for people activities to boost exploration. Comparative experiments with ED, seven state-of-the-art algorithms on CEC2017/CEC2022 benchmark functions, and six practical engineering problems demonstrate that MIED achieves significantly improved accuracy, stronger global search capability, and faster convergence. This work advances meta-heuristic optimization by providing an effective framework for balancing exploration and exploitation, and opens new directions for applying enterprise development-inspired mechanisms to complex real-world optimization scenarios.