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中文摘要：
汽油调合配比生产优化是一种非线性约束的多峰优化问题。针对一般群智能优化算法在解决此类优化中易陷于局部最优解，提出了一种改进的群搜索优化算法——全局群搜索优化算法（GGSO）。该算法采用混沌机制初始化粒子在解空间内均匀分布；在算法前期，保留GSO的追随者进化策略，以保证算法的收敛速度。在算法后期，对追随者引入速度更新和个体最优，以保证算法的收敛精度；在粒子陷入局部极值时，对追随者和游荡者引入一种新的交叉、变异机制和自适应混沌扰动机制，以保证粒子跳出局部极值，提高算法全局寻优性能。分别用4个标准测试函数对优化算法进行测试，结果表明：GGSO算法与标准GSO、线性递减惯性权重粒子群算法（LDWPSO）比较，收敛速度和全局寻优性能有明显优势。汽油在线调合优化实例应用表明：该算法有较快的收敛速度，能够较准确地寻得全局最优。
英文摘要：
The group search optimizer （GSO）, which is inspired by animal searching behaviour and group living theory, is a novel optimization algorithm. In this paper, a novel group search optimizer called global group search optimizer （GGSO） is proposed to improve the performance of standard GSO. In the optimizing, the initial population of GGSO is generated uniformly in the search space. Early in the algorithm, GSO evolutionary strategy is retained and PSO evolutionary strategy is adopted during the later computation period. The main approaches included introducing crossover operation in each iteration to increase the diversity of individuals, breaching the restrictions of local optimization points with a new chaotic disturbance mechanism and mutation operation during the later computation period. Tests are carried out through four standard test functions on GSO, LDWPSO and GGSO independently, the results shows that GGSO has a preferable convergence rate and accuracy. The application of gasoline blending online shows that GGSO is effective.
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