共查询到19条相似文献,搜索用时 359 毫秒
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针对目前已有的粒子群优化算法求解有等式约束优化问题时对收敛速度和解的精度的影响,提出了一种新的基于参数方程的粒子群优化算法.它是粒子群在初始化和选代进化过程中使用求解参数方程的方法处理等式约束设计出的粒子群优化算法.数值实验结果表明,新算法是有效的.它不仅提高了收敛速度和解的精度,而且是一种通用的智能算法. 相似文献
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对约束优化问题,为了避免罚因子和等式约束转化为不等式约束时引入的约束容忍度参数所带来的不便,本文在基本教与学优化(Teaching-learning-based optimization,TLBO)算法中加入了自我学习过程并提出了一种求解约束优化问题的协同进化教与学优化算法,使得罚因子和约束容忍度随种群的进化动态调整.对7个常见测试函数的数值实验验证了算法求解带有等式和不等式约束优化问题的有效性. 相似文献
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基于逻辑"或"约束优化的实时系统设计 总被引:1,自引:0,他引:1
标准约束优化问题的等式或不等式约束之间是逻辑"与"关系,目前已经有很多高效、收敛的优化算法.但是,在实际应用中有很多更一般的约束优化问题,其等式或不等式约束之间不仅包含逻辑"与"关系,而且还包含逻辑"或"关系,现有的针对标准约束优化问题的各种算法不再适用.给出一种新的数学变换方法,把具有逻辑"或"关系的不等式约束转换为一组具有逻辑"与"关系的不等式,并应用到实时单调速率调度算法的可调度性判定充要条件中,把实时系统设计表示成混合布尔型整数规划问题,利用经典的分支定界法求解.实验部分指出了各种方法的优缺点. 相似文献
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基于雁群启示的粒子群优化算法的几何约束求解 总被引:1,自引:0,他引:1
几何约束是约束求解技术中最关键的问题之一.求解一个几何约束问题的最终目的是确定几何图形中每一个几何体的具体坐标位置.几何约束问题可以等价为求解非线性方程组问题.约束问题转化为一个优化问题.本文采用基于雁群启示的粒子群优化算法来求解该问题.该算法受雁群飞行特征启发,一方面将粒子排序,每个粒子跟随其前面那个较优粒子飞行,保持了多样性;另一方面使每个粒子利用更多其他粒子的有用信息,粒子之间的竞争被增强.两个方面的结合将平衡速度和精度之间的矛盾.实验表明,该方法可以提高几何约束求解的效率和收敛性. 相似文献
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近年来,多目标优化问题引起了广泛关注,其求解目标多、目标函数复杂,当前方法通常将所有目标加权后求解,但这些方法会造成解集缺乏准确性.针对上述情况,本文首先根据目标分解的框架:辅助目标和等价目标约束优化框架,该框架是将约束优化的问题分解为辅助目标和等价目标相结合的优化问题,同时动态调整所分解出的对应子问题的权值,使分解出的子问题求解趋向于等价目标求解.其次基于粒子群优化算法和灰狼优化算法的各自优势,提出参数自适应的粒子群灰狼混合算法,混合算法的优势集合了粒子群算法的收敛性快和灰狼算法的搜索过程多样性,从而提高粒子进化过程的准确性.通过IEEE CEC2017数据集测试的结果表明:在调参合适的情况下,获得的函数最优值个数多于乌鸦搜索、受约束的模拟退火、带约束的水循环等经典算法,在10D情况下,28个测试函数中11个测试函数表现最佳;在30D的情况下,12个测试函数表现最佳. 相似文献
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带自适应感知能力的粒子群优化算法 总被引:1,自引:0,他引:1
提出一种求解约束优化问题的改进粒子群优化算法。它利用可行性判断规则处理约束条件,更新个体最优解和全局最优解。通过为粒子赋予自适应感知能力,算法能较好地平衡全局和局部搜索,且有能力跳出局部极值,防止早熟。边界附近粒子的感知结果被用来修正其飞行速度以加强算法对约束边界的搜索。实验结果表明,新算法收敛速度快,寻优能力强,能很好地求解约束优化问题。 相似文献
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求解约束优化问题的一种新方法--基于量子粒子群优化算法 总被引:2,自引:0,他引:2
在用粒子群优化(PSO)算法求解约束优化问题时,处理好约束条件是取得良好的优化效果的关键。针对群体智能和约束优化问题的特点,提出了一种在每次迭代中有选择地保留一定数量不可行解的方法——DCFI(DirectChooseFixedInfeasiblesolutions)法,并把它结合到最近提出的量子粒子群优化(QDPSO)算法中。该算法可以利用保留下来的不可行解来帮助搜索靠近边界的最优解,同时又可以避免罚因子的选择问题。数值实验显示了该算法的有效性。 相似文献
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一种新的遗传算法求解有等式约束的优化问题 总被引:2,自引:0,他引:2
针对有等式约束的优化问题,提出了一种新的遗传算法.该算法是在种群初始化、交叉、变异操作过程中使用求解参数方程的方法处理等式约束,违反不等式约束的个体用死亡罚函数进行惩罚设计出的实数编码遗传算法.数值实验结果表明,新算法性能优于现有其它算法;它不仅可以处理线性等式约束,而且还可以处理非线性等式约束,同时提高了收敛速度和解的精度,是一种通用强、高效稳健的智能算法. 相似文献
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基于自适应粒子群算法的约束布局优化研究 总被引:1,自引:0,他引:1
二维带平衡及不干涉约束的圆集在圆容器内的布局优化问题(如卫星舱布局)在理论上属于带性能约束的布局优化M题,它是NP—hard问题的难点,由于它的复杂性,传统的粒子群优化算法难于求解.通过对传统的粒子群优化算法的多重改进,提出了一种自适应粒子群优化算法,该算法在整个搜索过程中,既能保持粒子群原有基本结构,同时又能扩大搜索范围,在提高多样性的同时保证搜索精度,从而加快了收敛速度,有效避免早熟收敛问题,得到最优解.将改进后的算法应用于约束布局问题,建立了此类问题的粒子群算法,通过3个算例的数值计算,验证了该算法的可行性和有效性. 相似文献
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求解约束优化问题的改进灰狼优化算法 总被引:3,自引:0,他引:3
针对基本灰狼优化(GWO)算法存在求解精度低、收敛速度慢、局部搜索能力差的问题,提出一种改进灰狼优化(IGWO)算法用于求解约束优化问题。该算法采用非固定多段映射罚函数法处理约束条件,将原约束优化问题转化为无约束优化问题,然后利用IGWO算法对转换后的无约束优化问题进行求解。在IGWO算法中,引入佳点集理论生成初始种群,为算法全局搜索奠定基础;为了提高局部搜索能力和加快收敛,对当前最优灰狼个体执行Powell局部搜索。采用几个标准约束优化测试问题进行仿真实验,结果表明该算法不仅克服了基本GWO的缺点,而且性能优于差分进化和粒子群优化算法。 相似文献
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求解工程约束优化问题的PSO-ABC混合算法* 总被引:1,自引:1,他引:0
针对包含约束条件的工程优化问题,提出了基于人工蜂群的粒子群优化PSO-ABC算法。将PSO中较优的粒子作为ABC算法的蜜源,并使用禁忌表存储其局部极值,克服粒子群优化算法易陷入局部最优的缺陷。采用可行性规则进行约束处理,将粒子种群分为可行子群和不可行子群,并在ABC算法产生蜜源的过程中保留部分较优的可行解和不可行解的信息,弥补了可行性规则处理最优点位于约束边界附近的问题时存在的不足。四个典型工程优化设计的实验结果表明,该算法能够寻得更优的约束最优化解,且稳健性更强。 相似文献
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A hybrid multi-swarm particle swarm optimization to solve constrained optimization problems 总被引:3,自引:1,他引:2
In the real-world applications, most optimization problems are subject to different types of constraints. These problems are
known as constrained optimization problems (COPs). Solving COPs is a very important area in the optimization field. In this
paper, a hybrid multi-swarm particle swarm optimization (HMPSO) is proposed to deal with COPs. This method adopts a parallel
search operator in which the current swarm is partitioned into several subswarms and particle swarm optimization (PSO) is
severed as the search engine for each sub-swarm. Moreover, in order to explore more promising regions of the search space,
differential evolution (DE) is incorporated to improve the personal best of each particle. First, the method is tested on
13 benchmark test functions and compared with three stateof-the-art approaches. The simulation results indicate that the proposed
HMPSO is highly competitive in solving the 13 benchmark test functions. Afterward, the effectiveness of some mechanisms proposed
in this paper and the effect of the parameter setting were validated by various experiments. Finally, HMPSO is further applied
to solve 24 benchmark test functions collected in the 2006 IEEE Congress on Evolutionary Computation (CEC2006) and the experimental
results indicate that HMPSO is able to deal with 22 test functions. 相似文献
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Biogeography-based optimization (BBO) is a new evolutionary optimization method that is based on the science of biogeography. We propose two extensions to BBO. First, we propose a blended migration operator. Benchmark results show that blended BBO outperforms standard BBO. Second, we employ blended BBO to solve constrained optimization problems. Constraints are handled by modifying the BBO immigration and emigration procedures. The approach that we use does not require any additional tuning parameters beyond those that are required for unconstrained problems. The constrained blended BBO algorithm is compared with solutions based on a stud genetic algorithm (SGA) and standard particle swarm optimization 2007 (SPSO 07). The numerical results demonstrate that constrained blended BBO outperforms SGA and performs similarly to SPSO 07 for constrained single-objective optimization problems. 相似文献
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为提高约束优化模型的求解精度,提出一种改进的水波优化算法。设计主-从异构种群,结合ε约束处理技术使主群实现探索可行解,从群利用可行解搜寻全局最优解。为加快收敛速度和增强信息交互,主群中个体可以依概率进行个体间学习,设计水波波长函数,使其随着水波的适应度值和违反约束度及时调整。为避免早期收敛,从群采用自适应学习策略以平衡群体的探索和利用。设计随迭代次数变化的放松约束度,提高算法收敛精度。对比实验结果表明,该算法可以获得高质量的可行解。 相似文献
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This paper introduces an improved accelerated particle swarm optimization algorithm (IAPSO) to solve constrained nonlinear optimization problems with various types of design variables. The main improvements of the original algorithm are the incorporation of the individual particles memories, in order to increase swarm diversity, and the introduction of two selected functions to control balance between exploration and exploitation, during search process. These modifications are used to update particles positions of the swarm. Performance of the proposed algorithm is illustrated through six benchmark mechanical engineering design optimization problems. Comparison of obtained computation results with those of several recent meta-heuristic algorithms shows the superiority of the IAPSO in terms of accuracy and convergence speed. 相似文献
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A ranking selection-based particle swarm optimizer for engineering design optimization problems 总被引:2,自引:2,他引:0
Particle swarm optimization (PSO) algorithms have been proposed to solve optimization problems in engineering design, which
are usually constrained (possibly highly constrained) and may require the use of mixed variables such as continuous, integer,
and discrete variables. In this paper, a new algorithm called the ranking selection-based PSO (RSPSO) is developed. In RSPSO,
the objective function and constraints are handled separately. For discrete variables, they are partitioned into ordinary
discrete and categorical ones, and the latter is managed and searched directly without the concept of velocity in the standard
PSO. In addition, a new ranking selection scheme is incorporated into PSO to elaborately control the search behavior of a
swarm in different search phases and on categorical variables. RSPSO is relatively simple and easy to implement. Experiments
on five engineering problems and a benchmark function with equality constraints were conducted. The results indicate that
RSPSO is an effective and widely applicable optimizer for optimization problems in engineering design in comparison with the
state-of-the-art algorithms in the area. 相似文献