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中文摘要：
提出一种搜索空间自适应的自适应粒子群优化算法.该算法对不同等级的粒子适应值采取不同的惯性权重,并随着算法的迭代不断缩小粒子群的搜索空间.同时,选择当前代的较优部分粒子直接进入下一代,其他粒子通过在缩小的搜索空间内随机生成,加快了种群收敛速度,同时又能使种群不断跳出局部最优解.几种典型函数的仿真实验表明,该算法在收敛速度和收敛精度上均较标准粒子群优化算法和普通自适应粒子群优化算法有明显提高.
英文摘要：
A particle swarm optimization algorithm with adaptive linear decreasing search space was proposed.The algorithm implemented adaptive inertia weight for different levels of particles＇ fitness,and shrinked the search space during the running time.The best part of particles were chosen and propagated directly to the next generation of particles,the others were randomly generated within the reduced search space.The approach can speed up the convergence rate of population,while enables the population to jump out of the local minimum.Experiment results show that the algorithm can obviously improve convergence speed and solution accuracy comparing with standard particle swarm optimization（PSO）method and ordinary self-adaption PSO method.
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