Convergence enhanced genetic algorithm with successive zooming method for solving continuous optimization problems

A dynamic parameter encoding method was previously presented by Schraudolph and Belew [J Mach Learn 9 (1992) 9] for solving optimizing problems using discrete zooming factors. In contrast, the current paper proposes a successive zooming genetic algorithm (SZGA) for identifying global solutions using continuous zooming factors. To improve the local fine-tuning capability of a genetic algorithm (GA), a new method is introduced whereby the search space is zoomed around the design point with the best fitness per 100 generations. Furthermore, the reliability of the optimized solution is determined based on a theory of probability. To demonstrate the superiority of the proposed algorithm, a simple genetic algorithm, micro-genetic algorithm, and the proposed algorithm were compared as regards their ability to minimize multi-modal continuous functions and simple continuous functions. The results confirmed that the proposed SZGA significantly improved the ability of a GA to identify a precise global minimum. As an example of structural optimization, SZGA was applied to the optimal location of support points for weight minimization in the radial gate of a dam structure. The proposed algorithm identified a more exact optimum value than the conventional GAs.     

基于h-距离的DNA编码序列设计

针对DNA编码序列设计问题,将其转换为带约束的多目标优化问题,在单链DNA集合中引入h-距离,构造了DNA序列间的共享函数,应用小种群遗传算法,对DNA编码序列设计问题进行求解。与已有结果比较,算法可以得到更好的DNA序列且计算效率较高。算法可用于DNA计算中编码序列的具体设计。     

Structural topology optimization using a genetic algorithm with a morphological geometric representation scheme

This paper describes a versatile methodology for solving topology design optimization problems using a genetic algorithm (GA). The key to its effectiveness is a geometric representation scheme that works by specifying a skeleton which defines the underlying topology/connectivity of a structural continuum together with segments of material surrounding the skeleton. The required design variables are encoded in a chromosome which is in the form of a directed graph that embodies this underlying topology so that appropriate crossover and mutation operators can be devised to recombine and help preserve any desirable geometry characteristics of the design through succeeding generations in the evolutionary process. The overall methodology is first tested by solving ‘target matching’ problems—simulated topology optimization problems in each of which a ‘target’ geometry is first created and predefined as the optimum solution, and the objective of the optimization problem is to evolve design solutions to converge towards this target shape. The methodology is then applied to design two path-generating compliant mechanisms—large-displacement flexural structures that undergo some desired displacement paths at some point when given a straight line input displacement at some other point—by an actual process of topology/shape optimization.     

多分支混沌变异的头脑风暴优化算法

头脑风暴优化算法是一种受人类群体行为启发的新型群智能优化算法。该算法通过模拟人类使用头脑风暴创造性解决问题的行为,在解空间中分析个体分布,并使用变异生成新个体,多次迭代求得最优解,具有较高的鲁棒性和自适应能力。针对头脑风暴优化算法精度较差、易陷入局部最优导致早熟收敛的缺陷,提出了一种多分支混沌变异的头脑风暴优化算法。该算法选取8种混沌映射,设计了一种多分支混沌变异算子。当原始算法陷入局部最优时,使用多分支混沌变异生成新个体,利用多种混沌运动的遍历性、随机性和多样性,扩大了混沌空间的范围,增强了算法全局搜索的能力。对10个经典测试函数的10、20、30维问题进行测试,并与原始头脑风暴优化算法、粒子群优化算法、遗传算法和布谷鸟搜索算法进行对比,实验结果表明,所提出的算法可以有效避免陷入局部最优,具有更高的稳定性和全局搜索能力。     

基于微遗传算法的稀疏天线阵列优化方法

为生成无栅瓣、高空间分辨率的方向图,均匀平面阵列使用的天线单元数量很多,实现难度和成本高。稀疏布阵只需要相对较少的单元数目,会出现旁瓣电平升高、测向模糊等问题。通过对天线阵列特性进行分析,以全向一致、无栅瓣、低旁瓣的高分辨方向图为目标,利用微遗传算法对多重圆环阵列单元的位置参数进行优化,性能分析表明综合出的多重圆环阵列具有方位角对称、旁瓣电平低、起伏小的特点,且该算法优化效率高、收敛速度快。     

基于遗传算法的压气机叶片的多目标三维优化

为了提高轴流压气机的效率,需要研究一种新的高性能的转子叶片.采用人工神经网络与遗传算法寻优相结合的方法对某单级轴流压气机亚音速叶片进行三维叶片型线优化设计.优化目标是尽可能的提高转子叶片的总压比、流量和等熵效率.优化仿真结果显示,流动分离区明显后移,损失显著降低,等熵效率提高了0.48%,同时总压比和流量也都得到了提高,优化叶片的气动性能较原型叶片明显提高.结果表明,优化方法能很好的完成亚音速叶片的优化设计,是获得低损失高效率性能的叶片的有效途径.     

对偶四元数更新算法在划船运动下的优化设计

适合于计算机执行的对偶四元数更新算法是实现对偶四元数捷联惯导系统功能的关键。为此,将螺旋矢量作为工具,在给出螺旋矢量求解方法的基础上,通过分析合理选择划船运动作为测试条件,以二子样为例对基于螺旋矢量的对偶四元数更新算法进行优化设计。与传统的划船误差优化补偿算法在性能上进行分析与对比,结果表明,对偶优化算法具有一定的性能优势,它不但从结构上将旋转运动与平移运动统一表征,而且使2类运动参数的估计精度达到一致最优。     

Blended biogeography-based optimization for constrained optimization

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.     

A genetic algorithm with memory for mixed discrete-continuous design optimization

This paper describes a new approach for reducing the number of the fitness function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed additions to the GA make the search more effective and rapidly improve the fitness value from generation to generation. The additions involve memory as a function of both discrete and continuous design variables, multivariate approximation of the fitness function in terms of several continuous design variables, and localized search based on the multivariate approximation. The approximation is demonstrated for the minimum weight design of a composite cylindrical shell with grid stiffeners.     

求解约束优化的一个自适应杂交差分演化算法

结合基于可行性规则的约束处理技术,构造了一个求解约束优化问题的自适应杂交差分演化模拟退火算法。该算法以差分演化算法为基础,用模拟退火策略来增强种群的多样性,用一个基于可行性规则的约束处理技术来处理不等式约束,且自适应化关键控制参数,避开人为控制参数的困难。在标准测试集上的实验结果表明该算法的有效性,与同类算法的比较表明了该算法的优越性。     

A new chaotic multi-verse optimization algorithm for solving engineering optimization problems

Multi-verse optimization algorithm (MVO) is one of the recent meta-heuristic optimization algorithms. The main inspiration of this algorithm came from multi-verse theory in physics. However, MVO like most optimization algorithms suffers from low convergence rate and entrapment in local optima. In this paper, a new chaotic multi-verse optimization algorithm (CMVO) is proposed to overcome these problems. The proposed CMVO is applied on 13 benchmark functions and 7 well-known design problems in the engineering and mechanical field; namely, three-bar trust, speed reduce design, pressure vessel problem, spring design, welded beam, rolling element-bearing and multiple disc clutch brake. In the current study, a modified feasible-based mechanism is employed to handle constraints. In this mechanism, four rules were used to handle the specific constraint problem through maintaining a balance between feasible and infeasible solutions. Moreover, 10 well-known chaotic maps are used to improve the performance of MVO. The experimental results showed that CMVO outperforms other meta-heuristic optimization algorithms on most of the optimization problems. Also, the results reveal that sine chaotic map is the most appropriate map to significantly boost MVO’s performance.     

A new design optimization framework based on immune algorithm and Taguchi's method

This paper describes an innovative optimization approach that offers significant improvements in performance over existing methods to solve shape optimization problems. The new approach is based on two-stages which are (1) Taguchi's robust design approach to find appropriate interval levels of design parameters (2) Immune algorithm to generate optimal solutions using refined intervals from the previous stage. A benchmark test problem is first used to illustrate the effectiveness and efficiency of the approach. Finally, it is applied to the shape design optimization of a vehicle component to illustrate how the present approach can be applied for solving shape design optimization problems. The results show that the proposed approach not only can find optimal but also can obtain both better and more robust results than the existing algorithm reported recently in the literature.     

基于微种群遗传算法和RBF神经网络的散乱数据重建方法

利用微种群遗传算法,结合性能优越的径向基函数神经网络,建立了适用于散乱数据曲面重建的径向基函数网络模型.采用微种群遗传算法完成对神经网络的权值优化,可避免早熟收敛,且有较快的收敛速度.实验结果表明,用这种方法解决散乱数据点的重建问题,具有较高的精度.     

一种求解高维复杂优化问题的动态自适应和声搜索算法

为了更好地提高求解高维复杂优化问题的能力,提出一种动态自适应和声搜索(DSHS)算法。该算法采用正交试验来设计算法的初始化和声记忆库;利用多维动态自适应调整算子和单维和声微调算子相结合的策略进行和声创作;改进和声音调调解步长,从而增强算法的扰动能力,避免其陷入局部搜索。通过6个标准Benchmark函数测试表明,该算法在全局搜索能力、收敛速度和稳定性方面都有明显提高。     

供电网络优化设计的模拟退火算法

对供电网络优化设计提出了一种新算法。把供电网络优化设计问题抽象成图论问题，应用图论最优化方法解决该问题。同时提出了多边形变换方法，用模拟退火算法对供电网络进行优化设计，最终得到一个费用最小电网。     

遗传算法GA结合BFGS预测玻璃组成

复杂组成玻璃的定量设计过程往往涉及复杂的多目标优化问题，依照传统方法逐个实现目标性质设计，常常顾此失彼，玻璃开发周期很长，为此将多个性质加权平均后转化为一个综合指数，通过对综合指数的模拟来实现多个目标的优化，而这一过程常常需要强劲的优化算法，对于这种变量多、函数关系复杂的优化问题，传统的基于梯度的算法常常过早收敛于局部最优，适合全局搜索的遗传算法的局部精细搜索能力不强，因此将遗传算法与BFGS算法结合用于玻璃陶瓷复合材料组分设计，弥补了二者的缺点，实现了玻璃组成快速推确的程序设计，作为一种通用算法，GA-BFGS算法亦可用于其它最优化过程。     

基于免疫算法和EDA 的混合多目标优化算法

在免疫多目标优化算法的基础上,引入了分布估计算法(EDA)对进化种群进行建模采样的思想,提出了一种求解复杂多目标优化问题的混合优化算法HIAEDA(hybrid immune algorithm with EDA for multi-objectiveoptimization).HIAEDA 的进化过程混合了两种后代产生策略:一种是基于交叉变异的克隆选择算子,用于在父代种群周围进行局部搜索的同时开辟新的搜索区域;另一种是基于EDA 的模型采样算子,用于学习多目标优化问题决策变量之间的相关性,提高算法求解复杂多目标优化问题的能力.在分析两种算子搜索行为的基础上,讨论了两者在功能上的互补性,并利用有限马尔可夫链的性质证明了HIAEDA 算法的收敛性.对测试函数和实际工程问题的仿真实验结果表明,HIAEDA 与NSGAII 算法和基于EDA 的进化多目标优化算法RM-MEDA 相比,在收敛性和多样性方面均表现出明显优势,尤其是对于决策变量之间存在非线性关联的复杂多目标优化问题,优势更为突出.     

Optimizing thermal design of data center cabinets with a new multi-objective genetic algorithm

There is an ever increasing need to use optimization methods for thermal design of data centers and the hardware populating them. Airflow simulations of cabinets and data centers are computationally intensive and this problem is exacerbated when the simulation model is integrated with a design optimization method. Generally speaking, thermal design of data center hardware can be posed as a constrained multi-objective optimization problem. A popular approach for solving this kind of problem is to use Multi-Objective Genetic Algorithms (MOGAs). However, the large number of simulation evaluations needed for MOGAs has been preventing their applications to realistic engineering design problems. In this paper, details of a substantially more efficient MOGA are formulated and demonstrated through a thermal analysis simulation model of a data center cabinet. First, a reduced-order model of the cabinet problem is constructed using the Proper Orthogonal Decomposition (POD). The POD model is then used to form the objective and constraint functions of an optimization model. Next, this optimization model is integrated with the new MOGA. The new MOGA uses a “kriging” guided operation in addition to conventional genetic algorithm operations to search the design space for global optimal design solutions. This approach for optimal design is essential to handle complex multi-objective situations, where the optimal solutions may be non-obvious from simple analyses or intuition. It is shown that in optimizing the data center cabinet problem, the new MOGA outperforms a conventional MOGA by estimating the Pareto front using 50% fewer simulation calls, which makes its use very promising for complex thermal design problems. Recommended by: Monem Beitelmal     

基于多目标演化算法的逻辑电路设计

电路演化设计是新兴的研究热点,通过对电路演化设计的基本原理的介绍,提出了基于演化算法特别是改进的遗传算法(GeneticAlgorithm),根据具有一定逻辑功能的真值表,依据多个设计目标,以较少的运算量和较高的效率来演化设计出一个较优的逻辑电路。通过对几个具体实例的研究分析,说明了该设计思想的有效性和先进性。     

Interval optimization of rotor-bearing systems with dynamic behavior constraints using an interval genetic algorithm

A new interval optimization algorithm is presented in this paper. In engineering, most optimization algorithms focus on exact parameters and optimum objectives. However, exact parameters are not easy to be manufactured to because of manufacturing errors and expensive manufacturing cost. To account for these problems, it is necessary to estimate interval design parameters and allowable objective error. This is the first paper to propose a new interval optimization algorithm within the context of Genetic Algorithms. This new algorithm, the Interval Genetic Algorithm (IGA), can neglect interval analysis and determines the optimum interval parameters. Furthermore, it can also effectively maximize the design scope. The optimizing ability of the IGA is tested through the interval optimization of a two-dimensional function. Then the IGA is applied to rotor-bearing systems. The results show that the IGA is effective in deriving optimal interval design parameters within the allowable error when minimizing shaft weight and/or transmitted force of rotor-bearing systems.