具有轮盘反转算子的多Agent算法用于线性系统逼近

Effective multi-Agent algorithm with roulette inversion operator for approximating linear systems

针对John Holland的反转算子在数值优化中的不合理性,提出了一种轮盘反转算子来克服这种不合理性,并结合该算子提出了一种多Agent进化算法(RAER),证明了算法的全局收敛性.无约束优化仿真实验表明,该算法性能好于其他算法.在求解线性系统逼近工程优化问题时,无论在固定区域还是动态扩展区域搜索,算法都能得到更好的模型,较其他算法能够对搜索区域进行更为充分的探索和求精.RAER算法是实际有效的.

The irrationality of the inversion operator designed by John Holland is analyzed and revealed; and a new roulette inversion operator is proposed to cope with this problem. A new multi-agent evolutionary algorithm（RAER） is then developed by integrating the roulette inversion operator. Theoretical analysis shows that RAER converges to the global optimum. Four benchmark functions are used to test the performance of RAER, the results show that RAER achieves a better performance than other algorithms. RAER can be successfully used to solve linear system approximation problems in fixed search areas and dynamically expanded search areas. Especially, in the stable linear system approximation in several enlarged search areas, RAER can find the typical and optimal solutions in one specified area. This demonstrates the efficacy of RAER in practical applications.