MATLAB-Based Dynamic Differential Cooperative Evolution Code with Neighborhood Search

This MATLAB-implemented dynamic differential cooperative evolution code with neighborhood search is designed to solve complex optimization problems. The algorithm employs neighborhood search strategies to escape local optima and dynamically adjusts parameters like crossover rate and mutation factor to enhance global exploration capabilities. Key implemented variants include adaptive differential evolution (which automatically tunes parameters during runtime) and multi-objective differential evolution (handling Pareto-optimal solutions). The code structure features modular design with separate functions for population initialization, neighborhood topology management, and dynamic parameter adaptation. For high-dimensional problems with multiple objectives and constraints, the implementation utilizes constraint-handling mechanisms and archive maintenance strategies to maintain solution diversity while ensuring feasibility. This makes the code particularly effective for engineering optimization, machine learning parameter tuning, and other complex real-world applications requiring robust optimization techniques.