A Multi-Strategy Enhanced Farthest Better or Nearest Worse Optimizer for Complex Optimization Problems

An enhanced Farthest Better or Nearest Worse Optimizer (EFNO) is developed to overcome the limitations of the original FNO, including insufficient population diversity, and slow and premature convergence. To achieve a more effective balance between exploration and exploitation, three complementary strategies are incorporated: an adaptive weighted Euclidean distance mechanism to improve solution selection, an elite-guided archive reinforcement strategy for stage-wise differentiated population guidance, and a nearest-worse-individual-based reverse-reinforcement mechanism to enhance diversity. The effectiveness of EFNO is evaluated on the CEC2017 and CEC2022 benchmark suites under different dimensional settings. Comparative experiments with eleven State-of-the-Art algorithms, including a benchmark-winning method, are conducted to assess convergence behavior and optimization accuracy. The results indicate that EFNO consistently achieves superior performance and obtains the best overall ranking. To further demonstrate its applicability, EFNO is applied to two real-world engineering optimization problems and a robot path-planning task. In the path-planning experiments, six random grid-based environments with different sizes (20 × 20, 30 × 30, and 50 × 50) and obstacle densities (20% and 40%) are constructed. Experimental results show that EFNO outperforms all competing algorithms in terms of solution quality and robustness, confirming its effectiveness and strong generalization capability.