基于混沌搜索的简化粒子群优化算法
On a Simple Particle Swarm Optimization on the Basis of Chaotic Search
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摘要: 针对带有收缩因子的粒子群优化算法（CFPSO ）容易陷入局部极值、进化后期的收敛速度慢和精度低等缺点，采用简化粒子群优化（sCFPSO ）方程与混沌搜索技术相结合的方法，提出了基于混沌搜索的简化粒子群优化（CsCFPSO）算法。该算法利用分段线性混沌映射（PWLCM ）的遍历性和类随机性来完成混沌搜索，从而加快sCFP-SO算法跳出局部极值点而继续优化。经过6个经典测试函数对该算法进行实验，结果表明其对于粒子群优化具有很好的使用价值，它可以准确地消去局部极值，确保收敛速度和精度，该算法是通过缩小种群数和进化代数来实现的。Abstract: The particle swarm optimization algorithm with constriction factor (CFPSO) has some demerits , such as relapsing into local extremum ,slow convergence velocity and low convergence precision in the late evolutionary .A chaotic optimization-based simple particle swarm optimization equation with constriction factor has been developed .Piecewise linear chaotic map has been employed to perform chaotic optimization due to its ergodicity and stochasticity .Consequently ,the particles are accelerated to overstep the local ex-tremum in sCFPSO algorithm .The experiment results of six classic benchmark functions show that the proposed algorithm improves extraordinarily the convergence velocity and precision in evolutionary optimi-zation ,and can break away efficiently from the local extremum .Furthermore ,the algorithm obtains better optimization results with smaller populations and evolutionary generations .Therefore ,the proposed algo-rithm improves the practicality of the particle swarm optimization .
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