基于精英保留策略与爆炸算子的改进遗传算法

针对标准遗传算法(standard genetic algorithm，SGA)应用于数值优化存在收敛缓慢、易陷入局部优解和精度低等问题，提出一种具有爆炸算子的改进遗传算法(FGA)。引入爆炸算子(fire algorithm，FA)，通过局部最优解集爆炸产生新个体以弥补SGA算法寻优过程中种群多样性不足的缺陷, 从而提高算法在解析域的全局搜索能力；加入精英保留策略使每代中的最优个体都能得以保留，避免交叉和变异操作遗失全局最优解。为验证算法的优化性能，选用4个经典测试函数对SGA与FGA这2种算法的优化性能进行对比，算例结果表明，本文所提算法具有更好的全局搜索能力、收敛性能以及计算精度。

An Improved Genetic Algorithm Based on Elite Retention Strategy and Explosion Operators

When standard genetic algorithm (SGA) is applied to numerical optimization, the convergence is general slow and it is easy to fall into local optimal solution with low accuracy. Aimed at the problems, an improved genetic algorithm (FGA) with detonation operator is proposed. The explosion operator (fire algorithm, FA) through the local optimal solution set of explosion of new individuals makes up for the defects of SGA algorithm and the insufficiency of population diversity.Therefore, the search ability of algorithm in global analytic domain is improved.The induction of the elitist strategy, for which the best individual of each generation can be preserved, avoids crossover and mutation the operating loss of the global optimal solution. To verify the optimization performance of the algorithm, four classical test functions were used to compare the optimization performance of the two algorithms: SGA and FGA. The results of the example show that the proposed algorithm has better global search capability, convergence performance and computational accuracy.