A six-element Yagi-Uda array is optimally designed using Central Force Optimization (CFO) with a small amount of pseudo randomly injected negative gravity. CFO is a simple, deterministic metaheuristic analogizing grav...A six-element Yagi-Uda array is optimally designed using Central Force Optimization (CFO) with a small amount of pseudo randomly injected negative gravity. CFO is a simple, deterministic metaheuristic analogizing gravitational kinematics (motion of masses under the influence of gravity). It has been very effective in addressing a wide range of antenna and other problems and normally employs only positive gravity. With positive gravity the six element CFO-designed Yagi array described here exhibits excellent performance with respect to the objectives of impedance bandwidth and forward gain. This paper addresses the question of what happens when a small amount of negative gravity is injected into the CFO algorithm. Does doing so have any effect, beneficial, negative or neutral? In this particular case negative gravity improves CFO’s exploration and creates a region of optimality containing many designs that perform about as well as or better than the array discovered with only positive gravity. Without some negative gravity these array configurations are overlooked. This Yagi-Uda array design example suggests that antennas optimized or designed using deterministic CFO may well benefit by including a small amount of negative gravity, and that the negative gravity approach merits further study.展开更多
This paper investigates the effect of adding three extensions to Central Force Optimization when it is used as the Global Search and Optimization method for the design and optimization of 6-elementYagi-Uda arrays. Tho...This paper investigates the effect of adding three extensions to Central Force Optimization when it is used as the Global Search and Optimization method for the design and optimization of 6-elementYagi-Uda arrays. Those exten</span><span><span style="font-family:Verdana;">sions are </span><i><span style="font-family:Verdana;">Negative</span></i> <i><span style="font-family:Verdana;">Gravity</span></i><span style="font-family:Verdana;">, </span><i><span style="font-family:Verdana;">Elitism</span></i><span style="font-family:Verdana;">, and </span><i><span style="font-family:Verdana;">Dynamic</span></i> <i><span style="font-family:Verdana;">Threshold</span></i> <i><span style="font-family:Verdana;">Optimization</span></i><span style="font-family:Verdana;">. T</span></span><span style="font-family:Verdana;">he basic CFO heuristic does not include any of these, but adding them substan</span><span style="font-family:Verdana;">tially improves the algorithm’s performance. This paper extends the work r</span><span style="font-family:Verdana;">eported in a previous paper that considered only negative gravity and which </span><span style="font-family:Verdana;">showed a significant performance improvement over a range of optimized a</span><span style="font-family:Verdana;">rrays. Still better results are obtained by adding to the mix </span><i><span style="font-family:Verdana;">Elitism</span></i><span style="font-family:Verdana;"> and </span><i><span style="font-family:Verdana;">DTO</span></i><span style="font-family:Verdana;">. An overall improvement in best fitness of 19.16% is achieved by doing so. While the work reported here was limited to the design/optimization of 6-</span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">element Yagis, the reasonable inference based on these data is that any antenna design/optimization problem, indeed any Global Search and Optimiza</span><span style="font-family:Verdana;">tion problem, antenna or not, utilizing Central Force Optimization as the Gl</span><span style="font-family:Verdana;">obal Search and Optimization engine will benefit by including all three extensions, probably substantially.展开更多
中心引力优化算法(Central Force Optimization,CFO)是一种新型的基于天体动力学的多维搜索优化算法.该算法是一种确定性的优化算法,利用一组质子在万有引力作用下的运动,搜索决定空间的最优值,而这组质子按照两个来源于天体力学的迭代...中心引力优化算法(Central Force Optimization,CFO)是一种新型的基于天体动力学的多维搜索优化算法.该算法是一种确定性的优化算法,利用一组质子在万有引力作用下的运动,搜索决定空间的最优值,而这组质子按照两个来源于天体力学的迭代方程在空间移动.本文利用天体力学理论对该算法中质子运动方程做了深入的研究,并利用天体力学中万有引力定理对质子运动方程做了推导,建立起天体力学与CFO算法之间的联系,通过天体力学中数学分析的方法对该算法中质子收敛性能进行了分析,最后通过严格的数学推导证明出无论初始时质子是何种分布,CFO算法中所有的质子始终都会收敛于CFO空间的确定最优解.本文结论为了进一步深入研究该算法提供了理论基础.展开更多
中心引力优化算法(central force optimization,简称CFO)是一种新型的基于天体动力学的多维搜索优化算法.该算法是一种确定性的优化算法,利用一组质子在万有引力作用下的运动,搜索目标函数在决策空间上的最优值.利用天体力学理论对该算...中心引力优化算法(central force optimization,简称CFO)是一种新型的基于天体动力学的多维搜索优化算法.该算法是一种确定性的优化算法,利用一组质子在万有引力作用下的运动,搜索目标函数在决策空间上的最优值.利用天体力学理论对该算法中质子运动方程进行了深入的研究,并利用天体力学中万有引力定理对质子运动方程进行了推导,建立起天体力学与CFO算法之间的联系,通过天体力学中数学分析的方法对该算法中质子收敛性能进行了分析,最后,通过严格的数学推导证明出:无论初始时质子是何种分布,CFO算法中所有的质子始终都会收敛于CFO空间的确定最优解.作为测试效果,将CFO算法与常见的BP训练算法相结合,提出了CFO-BP训练算法,优化前馈型人工神经网络的权值和结构.实验结果表明,采用CFO-BP算法优化神经网络比其他常见优化算法有更好的收敛精度和收敛速度.展开更多
中心引力优化(Central Force Optimization,CFO)算法是一种新型多维搜索确定型启发式优化算法,但由于它的初始探测器(Probe)计算复杂而导致CFO算法运行时间过长。针对初始探测器计算复杂问题,提出一种均匀设计方法,依此方法提出了基于...中心引力优化(Central Force Optimization,CFO)算法是一种新型多维搜索确定型启发式优化算法,但由于它的初始探测器(Probe)计算复杂而导致CFO算法运行时间过长。针对初始探测器计算复杂问题,提出一种均匀设计方法,依此方法提出了基于均匀设计的CFO算法。将提出的CFO算法应用到典型测试函数中,并与CFO算法进行比较。数值结果表明,该算法保证寻优能力同时减少了CFO算法的运行时间,从而提高了CFO算法的效率。展开更多
A novel approach for improving antenna bandwidth is described using a 6-element Yagi-Uda array as an example. The new approach applies Central Force Optimization, a deterministic metaheuristic, and Variable Z0 technol...A novel approach for improving antenna bandwidth is described using a 6-element Yagi-Uda array as an example. The new approach applies Central Force Optimization, a deterministic metaheuristic, and Variable Z0 technology, a novel, proprietary design and optimization methodology, to produce an array with 33.09% fractional impedance bandwidth. This array’s performance is compared to its CFO-optimized Fixed Z0counterpart, and to the performance of a 6-ele- ment Dominating Cone Line Search-optimized array. Both CFO-optimized antennas exhibit better performance than the DCLS array, especially with respect to impedance bandwidth. Although the Yagi-Uda antenna was chosen to illustrate this new approach to antenna design and optimization, the methodology is entirely general and can be applied to any antenna against any set of performance objectives.展开更多
文摘A six-element Yagi-Uda array is optimally designed using Central Force Optimization (CFO) with a small amount of pseudo randomly injected negative gravity. CFO is a simple, deterministic metaheuristic analogizing gravitational kinematics (motion of masses under the influence of gravity). It has been very effective in addressing a wide range of antenna and other problems and normally employs only positive gravity. With positive gravity the six element CFO-designed Yagi array described here exhibits excellent performance with respect to the objectives of impedance bandwidth and forward gain. This paper addresses the question of what happens when a small amount of negative gravity is injected into the CFO algorithm. Does doing so have any effect, beneficial, negative or neutral? In this particular case negative gravity improves CFO’s exploration and creates a region of optimality containing many designs that perform about as well as or better than the array discovered with only positive gravity. Without some negative gravity these array configurations are overlooked. This Yagi-Uda array design example suggests that antennas optimized or designed using deterministic CFO may well benefit by including a small amount of negative gravity, and that the negative gravity approach merits further study.
文摘This paper investigates the effect of adding three extensions to Central Force Optimization when it is used as the Global Search and Optimization method for the design and optimization of 6-elementYagi-Uda arrays. Those exten</span><span><span style="font-family:Verdana;">sions are </span><i><span style="font-family:Verdana;">Negative</span></i> <i><span style="font-family:Verdana;">Gravity</span></i><span style="font-family:Verdana;">, </span><i><span style="font-family:Verdana;">Elitism</span></i><span style="font-family:Verdana;">, and </span><i><span style="font-family:Verdana;">Dynamic</span></i> <i><span style="font-family:Verdana;">Threshold</span></i> <i><span style="font-family:Verdana;">Optimization</span></i><span style="font-family:Verdana;">. T</span></span><span style="font-family:Verdana;">he basic CFO heuristic does not include any of these, but adding them substan</span><span style="font-family:Verdana;">tially improves the algorithm’s performance. This paper extends the work r</span><span style="font-family:Verdana;">eported in a previous paper that considered only negative gravity and which </span><span style="font-family:Verdana;">showed a significant performance improvement over a range of optimized a</span><span style="font-family:Verdana;">rrays. Still better results are obtained by adding to the mix </span><i><span style="font-family:Verdana;">Elitism</span></i><span style="font-family:Verdana;"> and </span><i><span style="font-family:Verdana;">DTO</span></i><span style="font-family:Verdana;">. An overall improvement in best fitness of 19.16% is achieved by doing so. While the work reported here was limited to the design/optimization of 6-</span></span></span><span><span><span style="font-family:""> </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">element Yagis, the reasonable inference based on these data is that any antenna design/optimization problem, indeed any Global Search and Optimiza</span><span style="font-family:Verdana;">tion problem, antenna or not, utilizing Central Force Optimization as the Gl</span><span style="font-family:Verdana;">obal Search and Optimization engine will benefit by including all three extensions, probably substantially.
文摘中心引力优化算法(Central Force Optimization,CFO)是一种新型的基于天体动力学的多维搜索优化算法.该算法是一种确定性的优化算法,利用一组质子在万有引力作用下的运动,搜索决定空间的最优值,而这组质子按照两个来源于天体力学的迭代方程在空间移动.本文利用天体力学理论对该算法中质子运动方程做了深入的研究,并利用天体力学中万有引力定理对质子运动方程做了推导,建立起天体力学与CFO算法之间的联系,通过天体力学中数学分析的方法对该算法中质子收敛性能进行了分析,最后通过严格的数学推导证明出无论初始时质子是何种分布,CFO算法中所有的质子始终都会收敛于CFO空间的确定最优解.本文结论为了进一步深入研究该算法提供了理论基础.
文摘中心引力优化算法(central force optimization,简称CFO)是一种新型的基于天体动力学的多维搜索优化算法.该算法是一种确定性的优化算法,利用一组质子在万有引力作用下的运动,搜索目标函数在决策空间上的最优值.利用天体力学理论对该算法中质子运动方程进行了深入的研究,并利用天体力学中万有引力定理对质子运动方程进行了推导,建立起天体力学与CFO算法之间的联系,通过天体力学中数学分析的方法对该算法中质子收敛性能进行了分析,最后,通过严格的数学推导证明出:无论初始时质子是何种分布,CFO算法中所有的质子始终都会收敛于CFO空间的确定最优解.作为测试效果,将CFO算法与常见的BP训练算法相结合,提出了CFO-BP训练算法,优化前馈型人工神经网络的权值和结构.实验结果表明,采用CFO-BP算法优化神经网络比其他常见优化算法有更好的收敛精度和收敛速度.
文摘中心引力优化(Central Force Optimization,CFO)算法是一种新型多维搜索确定型启发式优化算法,但由于它的初始探测器(Probe)计算复杂而导致CFO算法运行时间过长。针对初始探测器计算复杂问题,提出一种均匀设计方法,依此方法提出了基于均匀设计的CFO算法。将提出的CFO算法应用到典型测试函数中,并与CFO算法进行比较。数值结果表明,该算法保证寻优能力同时减少了CFO算法的运行时间,从而提高了CFO算法的效率。
文摘A novel approach for improving antenna bandwidth is described using a 6-element Yagi-Uda array as an example. The new approach applies Central Force Optimization, a deterministic metaheuristic, and Variable Z0 technology, a novel, proprietary design and optimization methodology, to produce an array with 33.09% fractional impedance bandwidth. This array’s performance is compared to its CFO-optimized Fixed Z0counterpart, and to the performance of a 6-ele- ment Dominating Cone Line Search-optimized array. Both CFO-optimized antennas exhibit better performance than the DCLS array, especially with respect to impedance bandwidth. Although the Yagi-Uda antenna was chosen to illustrate this new approach to antenna design and optimization, the methodology is entirely general and can be applied to any antenna against any set of performance objectives.