一种用于AGC机组调配的混沌多Agent双重粒子群算法

A Chaos Multi-Agent Dual Particle Swarm Optimization Algorithm for AGC Unit Dispatchment

AGC机组调配问题是一个含连续和离散变量的混合非线性优化问题,提出了一种基于混沌多Agent的双重粒子群算法。该算法以混沌和粒子群优化算法以及多Agent技术为基础,利用混沌映射提高初始种群的质量,引入临界算子增强Agent的多样性。在算法迭代中,每一个Agent通过与其随机配置的邻居竞争、合作,并且吸收了粒子群优化算法的进化机制,可以更稳定、快速地收敛到全局最优解。通过算例仿真结果表明,所提出的算法具有质量高的解、稳定性好的收敛特征和快的寻优速度。

The AGC units deployment deals with the hybrid nonlinear optimization containing continuous and discrete variables, and this paper proposes a chaos multi-Agent dual particle swarm algorithm. This algorithm is based on the chaos, particle swarm optimization algorithm and multi-agent technology, utilizes the chaos mapping to improve the quality of the initial population, and adopts the critical operator to enhance the diversity of the agent. In the algorithm iteration, each agent competes and cooperates its neighbors configured randomly; absorbing the evolutionary mechanism of the particle swarm optimization algorithm, and it can converge to the global optimal solution more stably and fast. The result of the example simulation shows that the proposed algorithm has a high quality solution, good convergence stability characteristics and fast optimization speed.