遗传算法用于电子枪发射系统的全局最优化设计

常用的优化设计方法，如单纯形法、Powell法等，易陷入局部最优解，而遗传算法是一种新兴的直接搜索最优化算法，它模拟达尔遗传选择与自然进化的理论，根据“适生存”和“优胜劣汰”的原则，借助“复制”、“交换”、“突变”等操作可以得到全局最优解，本将遗传算法运用于电子枪发射系统的最优化设计，得到了使交叠点半径尽可能小的发射系统的最佳结构和相应电参量。     

基于复合形算子的基础支护桩优化设计智能算法研究

本文通过遗传算法和传统复合形搜索法相结合,基于对遗传算法算子计算结构的调整,并将遗传算法与神经网络相结合,提出并研究了一种新的优化设计方法,协同求解复杂工程中的优化问题。并针对悬臂式支护桩的优化设计的数学模型,采用该算法进行了优化设计分析;计算结果表明,该算法可克服遗传算法最终进化至最优解较慢和人工神经网络易陷入局部解的缺陷,具有较好的全局性和收敛速度。     

刚度分段线性系统隔振抗冲击优化设计研究

提出了可应用于非线性系统冲击计算的DDAM改进方法,建立了附加弹性限位器的动力系统隔振抗冲击等效线性模型,设计了隔振抗冲击的优化框图,利用有限元法及遗传算法优化设计得到了pareto最优解集,根据该最优解集可选择有利于工程应用的隔振抗冲击优化方案,由参数反演得到系统的优化设计参数。该设计思路可以在保持良好隔振效果的同时,提高舰艇设备的抗冲击性能,对舰艇隔振抗冲设计具有较好的参考价值。     

遗传算法与惩罚函数法在机械优化设计中的应用

提出了应用于机械优化设计的"遗传算法+惩罚函数法"的通用算法.它非常适合求解复杂的非线性约束优化问题.本通用算法既克服了传统优化方法的缺点,得到了一个较为理想的全域最优解;同时也改善了遗传算法的局限性.     

基于模糊理论的机械多目标优化设计

多目标优化设计各分目标间的矛盾性和不可公度性增加了解决问题的难度,常规求解多目标优化设计方法一般只能求出问题的有效解,而得不到设计的最优结果。该文以蜗杆传动多目标优化设计为例,采用改进的遗传算法求得若干有效解后,根据模糊理论中的相似优先比法从中确定出最有效解,即最优解,并可排出它们的优劣顺序。     

遗传算法在桁架结构优化设计中的应用

本文提出桁架结构系统优化设计的新方法—遗传算法。它与常规化算法的不同之处在于从多个初始点开始寻优，并采用交迭和变异算子避免过早地收敛到局部最优解，可获得全局最优解，且不受初始值影响。该算法不必求导计算，编程简单、快捷，尤其适用于具有离散变量的结构优化设计问题。     

桁架结构优化设计的遗传算法

本文提出了桁架结构系统优化设计的新方法遗传算法，它不同于常规优化算法的特点在于，从多个初始点开始寻优，并采用交迭和变异算子避免过早地收敛到局部最优解，可获得全局最优解，且不受初始值影响。     

整车物流装载方案优化与验证

目的以轿运车使用数量最少为目标研究整车物流中乘用车的装载问题,以降低物流成本。方法建立轿运车混合装载的数学模型,并用枚举法列出混合装载的所有装载方案;筛选装载率在90%以上的方案建立组合矩阵,以此和乘用车的数量类型等作为约束条件,建立求解轿运车最少数量的数学模型;用遗传算法求最优解,并对计算结果进行验证。结果仿真结果表明遗传算法计算得到的最优配载方案与枚举出的最优解相近,但遗传算法计算时间仅为枚举法计算时间的1/200左右。结论用遗传算法对整车物流中乘用车的装载问题求最优方案的方法收敛速度快,计算结果与理论最优解相近,可兼顾计算时间和计算效果。     

遗传算法用于衍射光学元件的优化设计

提出了一种基于遗传算法的衍射光学元件优化设计方法;在衍射光学元件设计中遗传算法运行参数对遗传算法性能有一定的影响:采用较大的群体规模,遗传算法越容易获得最优解;交叉算子越大,遗传算法全局搜索能力越强;选择算子对遗传算法的影响不是太大;如果要进一步提高解的精度,可选取较大的终止代数。数值计算结果表明,用遗传算法优化设计的衍射光学元件,其误差小于 5.2%,衍射效率达到 91.2%。遗传算法很适合衍射光学元件的优化设计。     

一种用于匹配场反演的遗传算法

遗传算法在接近全局最优解时,存在搜索速度变慢、过早收敛、个体的多样性减少很快、甚至陷入局部最优解等问题。通过在遗传算法中引入模拟退火因子、混沌因子和多样性测度因子,在很大程度上克服了原有遗传算法的早熟、局部搜索能力差的缺点。同时,又能发挥原有遗传算法的强大的全局搜索能力,保证了改进后的混合遗传算法能较好地收敛于其全局最优值。     

A genetic algorithm approach to single and multiobjective structural optimization with discrete–continuous variables

A search procedure with a philosophical basis in molecular biology is adapted for solving single and multiobjective structural optimization problems. This procedure, known as a genetic algorithm (GA). utilizes a blending of the principles of natural genetics and natural selection. A lack of dependence on the gradient information makes GAs less susceptible to pitfalls of convergence to a local optimum. To model the multiple objective functions in the problem formulation, a co-operative game theoretic approach is proposed. Examples dealing with single and multiobjective geometrical design of structures with discrete–continuous design variables, and using artificial genetic search are presented. Simulation results indicate that GAs converge to optimum solutions by searching only a small fraction of the solution space. The optimum solutions obtained using GAs compare favourably with optimum solutions obtained using gradient-based search techniques. The results indicate that the efficiency and power of GAs can be effectively utilized to solve a broad spectrum of design optimization problems with discrete and continuous variables with similar efficiency.     

Handbook of Nonparametric Statistics

To search for an optimum in a large search space, Wu, Mao, and Ma suggested the sequential elimination of levels (SELs)-method to find an optimal setting. Genetic algorithms (GAs) can be used to improve on this method. To make the search procedure more efficient, new ideas of forbidden array and weighted mutation are introduced. Relaxing the condition of orthogonality, GAs are able to accommodate a variety of design points, which allows more flexibility and enhances the likelihood of getting the best setting in fewer runs, particularly in the presence of interactions. The search procedure is enriched by a Bayesian method for identifying the important main effects and two-factor interactions. Illustration is given with the optimization of three functions, one of which is from Shekel's family. A real example on compound optimization is also given.     

A Genetic Algorithm for Target Tracking in FLIR Video Sequences Using Intensity Variation Function

Automatic target tracking in forward-looking infrared (FLIR) imagery is a challenging research area in computer vision. This task could be even more critical when real-time requirements have to be taken into account. In this context, techniques exploiting the target intensity profile generated by an intensity variation function (IVF) proved to be capable of providing significant results. However, one of their main limitations is represented by the associated computational cost. In this paper, an alternative approach based on genetic algorithms (GAs) is proposed. GAs are search methods based on evolutionary computations, which exploit operators inspired by genetic variation and natural selection rules. They have been proven to be theoretically and empirically robust in complex space searches by their founder, J. H. Holland. Contrary to most optimization techniques, whose goal is to improve performances toward the optimum, GAs aim at finding near-optimal solutions by performing parallel searches in the solution space. In this paper, an optimized target search strategy relying on GAs and exploiting an evolutionary approach for the computation of the IVF is presented. The proposed methodology was validated on several data sets, and it was compared against the original IVF implementation by Bal and Alam. Experimental results showed that the proposed approach is capable of significantly improving performances by dramatically reducing algorithm processing time.     

Performance of genetic algorithms in search for water splitting perovskites

We examine the performance of genetic algorithms (GAs) in uncovering solar water light splitters over a space of almost 19,000 perovskite materials. The entire search space was previously calculated using density functional theory to determine solutions that fulfill constraints on stability, band gap, and band edge position. Here, we test over 2500 unique GA implementations in finding these solutions to determine whether GA can avoid the need for brute force search, and thereby enable larger chemical spaces to be screened within a given computational budget. We find that the best GAs tested offer almost a 6 times efficiency gain over random search, and are comparable to the performance of a search based on informed chemical rules. In addition, the GA is almost 10 times as efficient as random search in finding half the solutions within the search space. By employing chemical rules, the performance of the GA can be further improved to approximately 12–17 better than random search. We discuss the effect of population size, selection function, crossover function, mutation rate, fitness function, and elitism on the final result, finding that selection function and elitism are especially important to GA performance. In addition, we determine that parameters that perform well in finding solar water splitters can also be applied to discovering transparent photocorrosion shields. Our results indicate that coupling GAs to high-throughput density functional calculations presents a promising method to rapidly search large chemical spaces for technological materials.     

Truss optimization on shape and sizing with frequency constraints based on genetic algorithm

Truss optimization on shape and sizing with frequency constraints are highly nonlinear dynamic optimization problems. Coupling of two different types of design variables, nodal coordinates and cross-sectional areas, often lead to divergence while multiple frequency constraints often cause difficult dynamic sensitivity analysis. So optimal criteria method and mathematical programming, which need complex dynamic sensitivity and are easily trapped into the local optima, are difficult to solve the problems. To solve the truss shape and sizing optimization simply and effectively, a Niche Hybrid Genetic Algorithm (NHGA) is proposed. The objective of NHGA is to enhance the exploitation capacities while preventing the premature convergence simultaneously based on the new hybrid architecture. Niche techniques and adaptive parameter adjustment are used to maintain population diversity for preventing the premature convergence while simplex search is used to enhance the local search capacities of GAs. The proposed algorithm effectively alleviates premature convergence and improves weak exploitation capacities of GAs. Several typical truss optimization examples are employed to demonstrate the validity, availability and reliability of NHGA for solving shape and sizing optimization of trusses with multiple frequency constraints.     

A hybrid global–local approach for optimization of injection gate locations in liquid composite molding process simulations

The optimization of injection gate locations in liquid composite molding processes by trial and error based methods is time consuming and requires an elevated level of intuition, even when high fidelity physics-based numerical models are available. Optimization based on continuous sensitivity equations (CSE) and gradient search algorithms focused towards minimizing the mold infusion time gives a robust approach that will converge to local optima based on the initial solution. Optimization via genetic algorithms (GA) utilizes natural selection as a means of finding the optimal solution in the global domain; the computed solution is at best, close to the global optimum with further refinement still possible. In this paper, we present a hybrid global–local search approach that combines evolutionary GAs with gradient-based searches via the CSE. The hybrid approach provides a global search with the GA for a predetermined amount of time and is subsequently further refined with a gradient-based search via the CSE. In our hybrid method, we utilize the efficiency of gradient searches combined with the robustness of the GA. The resulting combination has been demonstrated to provide better and more physically correct results than either method alone. The hybrid method provides optimal solutions more quickly than GA alone and more robustly than CSE based searches alone. A resin infusion quality parameter that measures the deviation from a near uniform mold volume infusion rate is defined. The effectiveness of the hybrid method with a modified objective function that includes both the infusion time and the defined mold infusion quality parameter is demonstrated.     

An enhanced genetic algorithm for structural topology optimization

Genetic algorithms (GAs) have become a popular optimization tool for many areas of research and topology optimization an effective design tool for obtaining efficient and lighter structures. In this paper, a versatile, robust and enhanced GA is proposed for structural topology optimization by using problem‐specific knowledge. The original discrete black‐and‐white (0–1) problem is directly solved by using a bit‐array representation method. To address the related pronounced connectivity issue effectively, the four‐neighbourhood connectivity is used to suppress the occurrence of checkerboard patterns. A simpler version of the perimeter control approach is developed to obtain a well‐posed problem and the total number of hinges of each individual is explicitly penalized to achieve a hinge‐free design. To handle the problem of representation degeneracy effectively, a recessive gene technique is applied to viable topologies while unusable topologies are penalized in a hierarchical manner. An efficient FEM‐based function evaluation method is developed to reduce the computational cost. A dynamic penalty method is presented for the GA to convert the constrained optimization problem into an unconstrained problem without the possible degeneracy. With all these enhancements and appropriate choice of the GA operators, the present GA can achieve significant improvements in evolving into near‐optimum solutions and viable topologies with checkerboard free, mesh independent and hinge‐free characteristics. Numerical results show that the present GA can be more efficient and robust than the conventional GAs in solving the structural topology optimization problems of minimum compliance design, minimum weight design and optimal compliant mechanisms design. It is suggested that the present enhanced GA using problem‐specific knowledge can be a powerful global search tool for structural topology optimization. Copyright © 2005 John Wiley & Sons, Ltd.     

A discrete hybrid differential evolution algorithm for solving integer programming problems

Differential evolution (DE) is one of the most prominent new evolutionary algorithms for solving real-valued optimization problems. In this article, a discrete hybrid differential evolution algorithm is developed for solving global numerical optimization problems with discrete variables. Orthogonal crossover is combined with DE crossover to achieve crossover operation, and the simplified quadratic interpolation (SQI) method is employed to improve the algorithm's local search ability. A mixed truncation procedure is incorporated in the operations of DE mutation and SQI to ensure that the integer restriction is satisfied. Numerical experiments on 40 test problems including seventeen large-scale problems with up to 200 variables have demonstrated the applicability and efficiency of the proposed method.