A hyper-curvature balanced indicator and adaptive phase exploration co-driven evolutionary algorithm for many-objective optimization

Key Points

• The new hyper-curvature balanced indicator demonstrates superior performance in many-objective optimization tasks, especially in complex scenarios.
• Key metrics show enhanced optimization efficiency compared to traditional methods, focusing on three main objectives in controlled tests.
• Adaptive phase exploration within the algorithm optimizes search space more effectively, leading to better solutions over varied test conditions.
• Implications suggest this could set a benchmark for future optimization algorithms, addressing gaps in present methodologies.