The applicability of the extremum principles of entropy generation and entransy dissipation is studied for heat exchanger optimization. The extremum principle of entransy dissipation gives better optimiza-tion results...The applicability of the extremum principles of entropy generation and entransy dissipation is studied for heat exchanger optimization. The extremum principle of entransy dissipation gives better optimiza-tion results when heat exchanger is only for the purpose of heating and cooling, while the extremum principle of entropy generation is better for the heat exchanger optimization when it works in the Brayton cycle. The two optimization principles are approximately equivalent when the temperature drops of the streams in a heat exchanger are small.展开更多
粒子群优化(PSO)算法是一种新兴的群体智能优化技术,由于其原理简单、参数少、效果好等优点已经广泛应用于求解各类复杂优化问题.而影响该算法收敛速度和精度的2个主要因素是粒子个体极值与全局极值的更新方式.通过分析粒子的飞行轨迹...粒子群优化(PSO)算法是一种新兴的群体智能优化技术,由于其原理简单、参数少、效果好等优点已经广泛应用于求解各类复杂优化问题.而影响该算法收敛速度和精度的2个主要因素是粒子个体极值与全局极值的更新方式.通过分析粒子的飞行轨迹和引入广义中心粒子和狭义中心粒子,提出双中心粒子群优化(double center particle swarm optimization,DCPSO)算法,在不增加算法复杂度条件下对粒子的个体极值和全局极值更新方式进行更新,从而改善了算法的收敛速度和精度.采用Rosenbrock和Rastrigrin等6个经典测试函数,按照固定迭达次数和固定时间长度运行2种方式进行测试,验证了新算法的可行性和有效性.展开更多
基金Supported by Major State Basic Research Development Program of China (Grant No. 2007CB206901)
文摘The applicability of the extremum principles of entropy generation and entransy dissipation is studied for heat exchanger optimization. The extremum principle of entransy dissipation gives better optimiza-tion results when heat exchanger is only for the purpose of heating and cooling, while the extremum principle of entropy generation is better for the heat exchanger optimization when it works in the Brayton cycle. The two optimization principles are approximately equivalent when the temperature drops of the streams in a heat exchanger are small.
文摘粒子群优化(PSO)算法是一种新兴的群体智能优化技术,由于其原理简单、参数少、效果好等优点已经广泛应用于求解各类复杂优化问题.而影响该算法收敛速度和精度的2个主要因素是粒子个体极值与全局极值的更新方式.通过分析粒子的飞行轨迹和引入广义中心粒子和狭义中心粒子,提出双中心粒子群优化(double center particle swarm optimization,DCPSO)算法,在不增加算法复杂度条件下对粒子的个体极值和全局极值更新方式进行更新,从而改善了算法的收敛速度和精度.采用Rosenbrock和Rastrigrin等6个经典测试函数,按照固定迭达次数和固定时间长度运行2种方式进行测试,验证了新算法的可行性和有效性.
