基于混合果蝇优化算法的选址-库存-配送集成优化研究

本文面向企业运营管理实践,构建了一种基于联合补货策略的选址-库存-配送集成优化新模型。作为典型的NP-hard问题,传统算法难以高效稳定地求解,故本文设计了一种新的混合果蝇优化算法(Fruit Fly Optimization Algorithm, FOA),通过引入进化算法的信息交换、变异、选择操作来增强算法局部寻优能力,采取概率性飞行策略来平衡算法的全局寻优与局部寻优。算例结果表明,新混合FOA算法的准确性和稳定性较标准FOA有了明显的改善,与差分进化、自适应混合差分进化、粒子群优化相比也具有比较优势。     

求解TSP问题的改进离散蝴蝶优化算法

针对离散蝴蝶优化算法求解TSP问题时精度低和收敛速度慢等问题,提出一种改进离散蝴蝶优化算法.为了提升搜索效率,利用贪婪机制初始化种群,同时结合2-opt算子、改进的2-opt算子和模拟退火等策略来提高寻优能力.通过标准TSPLIB数据库中几十个实例仿真实验,并与一些经典、新型的智能算法比较,结果表明提出的算法在寻优能力和鲁棒性方面表现优越.     

具有动应力和动位移约束的离散变量结构拓扑优化设计方法

讨论了动应力、动位移约束下离散变量结构拓扑优化设计问题.首先给出问题的数学模型,然后用拟静力算法,将结构惯性力极值作为静载荷施加到结构上,求得结构的动位移和动内力,将考虑动应力约束和动位移约束的离散变量结构拓扑设计问题化为静应力和静位移约束的优化问题,然后利用两类变量统一考虑的离散变量结构拓扑优化设计的综合算法进行求解.     

约束优化问题的单参数填充函数算法

本文研究约束优化问题的全局优化确定性方法.基于填充函数的定义,具体构造出了一个新的单参数填充函数并做了相关理论证明.结合SQP和BFGS局部极小化算法设计了新的填充函数全局优化算法.数值实验表明,该算法可行有效,具有良好的全局寻优能力.     

具有动应力、位移和稳定性约束的离散变量桁架结构布局优化设计算法

给出了在动应力、动位移和动稳定约束下离散变量结构布局优化设计问题的数学模型,用“拟静力”算法,将具有动应力约束、动位移约束和动稳定约束的离散变量结构布局优化设计问题化为静应力、静位移和静稳定约束的优化问题,然后利用两级优化算法求解该模型．优化过程由两级组成,拓扑级优化和形状级优化．在每一级,都使用了综合算法,并且在搜索过程中都根据两类设计变量的相对差商值进行搜索．对包含稳定约束和不包含稳定约束的优化结果做了比较,结果显示稳定性约束对优化结果产生较大的影响．     

后机身蒙皮的改进优化

后机身蒙皮是结构的重要组成部分,其承受尾翼带来扭转等不同部件传递来的多方向载荷,适于发挥复合材料各向不同性的优势。由于受力方向多样化,合理设计铺层角度、铺层比例、铺层顺序和铺层厚度等参数以获得最大的减重收益,是复合材料蒙皮壁板设计的重点和难点。该文利用hyperworks有限元分析软件,将某型机的后机身上壁金属蒙皮改进为复复合材料结构,进行形状、尺寸和铺层顺序优化,结果表明:优化后满足强度、刚度和稳定性要求且获得了显著的减重效益。     

分层混合局部搜索策略异构多核系统调度

针对遗传算法解决异构多核系统的任务调度问题容易产生早熟现象及其局部寻优能力较差的缺点,将局部搜索算法与遗传算法相结合,创新性地提出一种求解异构多核系统的任务调度问题的分层混合局部搜索遗传算法。该算法提出一种新的分层优化策略以产生初始种群,在变异操作中,对部分个体设计3-opt优化变异,对种群中的优秀个体用改进的Lin-Kernighan算法进行优化。仿真实验结果表明,分层混合局部搜索遗传算法求解异构多核系统的任务调度问题时可以高效获得高质量的解。     

函数优化的小生境蝙蝠算法

针对蝙蝠算法易陷入局部最优解的缺点,利用小生境技术对蝙蝠算法进行了改进,提出一种小生境蝙蝠优化算法.算法基于小生境技术的适应度共享来分隔种群,引入了小生境排挤机制来保持种群多样性,在延续蝙蝠算法原有并行搜索等优势的基础上,提高了算法的金局搜索能力和局部收敛速度,具有可在不同邻域内发现多个解的特点.通过对一系列经典函数测试,并与已有算法进行比较,结果表明该算法在函数优化问题的求解中具有较高的计算效率和精度,以及较好的全局寻优能力.     

顺序相依拆卸线平衡多目标优化模型及算法研究

拆卸是产品回收过程最关键环节之一,拆卸效率直接影响再制造成本。本文在分析现有模型不足基础上,考虑最小化总拆卸时间,建立多目标顺序相依拆卸线平衡问题优化模型,并提出了一种自适应进化变邻域搜索算法。所提算法引入种群进化机制,并采用一种组合策略构建初始种群,通过锦标赛法选择个体进化;在局部搜索时,设计了邻域结构自适应选择策略,并采用基于交叉的全局学习机制加速跳出局部最优,以提高算法寻优能力。对比实验结果,证实了所提模型的合理性以及算法的高效性。     

基于改进的旗鱼优化算法求解Sylvester问题

Sylvester问题又称最小包围圆问题,提出了一种改进的旗鱼优化算法(ISFO)对其进行求解.首先对旗鱼优化算法(SFO)的寻优策略进行分析;其次,针对旗鱼优化算法种群初始化依赖,容易陷入局部最优等问题,引入Arnold映射初始化种群,提高算法的寻优能力;引入反向学习与柯西变异算子策略对全局最优解进行扰动产生新解,平衡算法的开发与勘探能力,避免算法出现早熟现象;然后和基本SFO算法与PSO算法使用6个基准测试函数进行仿真实验对比,结果表明ISFO算法相对于SFO算法收敛速度更快、精度更高、有效避免了早熟现象.最后使用ISFO、SFO、PSO对三个规模案例的Sylvester问题进行求解,证明了ISFO算法求解Sylvester问题的可行性与优越性.     

A hybrid genetic algorithm–neural network strategy for simulation optimization

Simulation optimization aims at determining the best values of input parameters, while the analytical objective function and constraints are not explicitly known in terms of design variables and their values only can be estimated by complicated analysis or time-consuming simulation. In this paper, a hybrid genetic algorithm–neural network strategy (GA–NN) is proposed for such kind of optimization problems. The good approximation performance of neural network (NN) and the effective and robust evolutionary searching ability of genetic algorithm (GA) are applied in hybrid sense, where NNs are employed in predicting the objective value, and GA is adopted in searching optimal designs based on the predicted fitness values. Numerical simulation results and comparisons based on a well-known pressure vessel design problem demonstrate the feasibility and effectiveness of the framework, and much better results are achieved than some existed literature results.     

煤炭码头卸车调度问题多目标优化模型及算法

本文针对煤炭码头卸车调度问题,提出了相应的多约束多目标优化模型,并设计了采用仿真推演策略解码的遗传算法求解。首先,本文考虑列车、煤种、场存、设备、翻堆线和卸车作业过程等约束条件,以卸车效率最大和列车在港时间最短为目标,构建了煤炭码头卸车调度问题多目标数学模型。然后,综合运筹学、遗传算法以及仿真技术,给出了煤炭码头卸车调度问题遗传算法详细设计,包括组合式编码和仿真推演解码方法,染色体生成算法,适应度函数设计,以及采用多种策略的遗传操作及修正等,并列出了算法步骤。实例测试表明,本算法的执行效率高而且优化效果好,结果适用。     

正态云模型果蝇优化算法及其应用

提出了一种基于正态云模型的果蝇优化算法(NCMFOA).该算法通过直接将果蝇位置赋值给气味浓度判定值和引入正态云模型来刻画果蝇嗅觉搜索行为的随机性与模糊性,从而解决了果蝇优化算法(FOA)不能搜索负值空间的缺陷,并有效克服了FOA算法在解决复杂优化问题时容易陷入局部极值的不足.通过正态云模型熵值的动态调整,使得NCMFOA算法在进化的前期阶段具有较强的随机性与模糊性,以提高算法的全局探索能力;随着迭代次数的增加,算法搜索行为的随机性与模糊性逐渐减弱,使得其局部开发能力逐渐增强,算法收敛精度得到提高.此外,通过引入视觉实时更新方案,进一步加速了算法的收敛速度.用经典的基准测试函数验证了NCMFOA算法的可行性与有效性,结果表明该算法具有收敛速度快、收敛精度高以及鲁棒性好等优点,对于高维复杂优化问题,该算法同样获得了良好的优化效果.将NCMFOA算法用于解决混沌系统的参数估计问题,进一步验证了该算法具有较强的解决实际工程优化问题的能力.     

Convergence Criteria for Hierarchical Overlapping Coordination of Linearly Constrained Convex Design Problems

Decomposition of multidisciplinary engineering system design problems into smaller subproblems is desirable because it enhances robustness and understanding of the numerical results. Moreover, subproblems can be solved in parallel using the optimization technique most suitable for the underlying mathematical form of the subproblem. Hierarchical overlapping coordination (HOC) is an interesting strategy for solving decomposed problems. It simultaneously uses two or more design problem decompositions, each of them associated with different partitions of the design variables and constraints. Coordination is achieved by the exchange of information between decompositions. This article presents the HOC algorithm and several new sufficient conditions for convergence of the algorithm to the optimum in the case of convex problems with linear constraints. One of these equivalent conditions involves the rank of the constraint matrix that is computationally efficient to verify. Computational results obtained by applying the HOC algorithm to quadratic programming problems of various sizes are included for illustration.     

Topology Optimization of Trusses—Random Cost Method Versus Evolutionary Algorithms

The recently proposed random cost method is applied to the topology optimization of trusses. Its performance is compared to previous genetic algorithm and evolution strategy simulations. Random cost turns out to be an optimization method with attractive features. In comparison to the genetic algorithm approach of Hajela, Lee and Lin, random cost turns out to be simpler and more efficient. Furthermore it is found that in contrast to evolution strategy, the random cost strategy's ability to find optima, is independent of the initial structure. This characteristic is related to the important capacity of escaping from local optima.     

Optimización mixta de estructuras de transporte de energía: aplicación del algoritmo de recocido simulado

A general methodology to optimize the weight of power transmission structures is presented in this article. This methodology is based on the simulated annealing algorithm defined by Kirkpatrick in the early ‘80s. This algorithm consists of a stochastic approach that allows to explore and analyze solutions that do not improve the objective function in order to develop a better exploration of the design region and to obtain the global optimum. The proposed algorithm allows to consider the discrete behavior of the sectional variables for each element and the continuous behavior of the general geometry variables. Thus, an optimization methodology that can deal with a mixed optimization problem and includes both continuum and discrete design variables is developed. In addition, it does not require to study all the possible design combinations defined by discrete design variables. The algorithm proposed usually requires to develop a large number of simulations (structural analysis in this case) in practical applications. Thus, the authors have developed first order Taylor expansions and the first order sensitivity analysis involved in order to reduce the CPU time required. Exterior penalty functions have been also included to deal with the design constraints. Thus, the general methodology proposed allows to optimize real power transmission structures in acceptable CPU time.     

Multihazard Design: Structural Optimization Approach

The objective of multihazard structural engineering is to develop methodologies for achieving designs that are safe and cost-effective under multiple hazards. Optimization is a natural tool for achieving such designs. In general, its aim is to determine a vector of design variables subjected to a given set of constraints, such that an objective function of those variables is minimized. In the particular case of structural design, the design variables may be member sizes; the constraints pertain to structural strength and serviceability (e.g., keeping the load-induced stresses and deflections below specified thresholds); and the objective function is the structure cost or weight. In a multihazard context, the design variables are subjected to the constraints imposed by all the hazards to which the structure is exposed. In this paper, we formulate the multihazard structural design problem in nonlinear programming terms and present a simple illustrative example involving four design variables and two hazards: earthquake and strong winds. Results of our numerical experiments show that interior-point methods are significantly more efficient than classical optimization methods in solving the nonlinear programming problem associated with our illustrative example.     

Scope of stationary multi-objective evolutionary optimization: a case study on a hydro-thermal power dispatch problem

Many engineering design and developmental activities finally resort to an optimization task which must be solved to get an efficient and often an intelligent solution. Due to various complexities involved with objective functions, constraints, and decision variables, optimization problems are often not adequately suitable to be solved using classical point-by-point methodologies. Evolutionary optimization procedures use a population of solutions and stochastic update operators in an iteration in a manner so as to constitute a flexible search procedure thereby demonstrating promise to such difficult and practical problem-solving tasks. In this paper, we illustrate the power of evolutionary optimization algorithms in handling different kinds of optimization tasks on a hydro-thermal power dispatch optimization problem: (i) dealing with non-linear, non-differentiable objectives and constraints, (ii) dealing with more than one objectives and constraints, (iii) dealing with uncertainties in decision variables and other problem parameters, and (iv) dealing with a large number (more than 1,000) variables. The results on the static power dispatch optimization problem are compared with that reported in an existing simulated annealing based optimization procedure on a 24-variable version of the problem and new solutions are found to dominate the solutions of the existing study. Importantly, solutions found by our approach are found to satisfy theoretical Kuhn–Tucker optimality conditions by using the subdifferentials to handle non-differentiable objectives. This systematic and detail study demonstrates that evolutionary optimization procedures are not only flexible and scalable to large-scale optimization problems, but are also potentially efficient in finding theoretical optimal solutions for difficult real-world optimization problems. Kalyanmoy Deb, Deva Raj Chair Professor. Currently a Finland Distinguished Professor, Department of Business Technology, Helsinki School of Economics, 00101 Helsinki, Finland.     

Optimization of laminated composite plates for maximum fundamental frequency using Elitist-Genetic algorithm and finite strip method

In the present paper, fundamental frequency optimization of symmetrically laminated composite plates is studied using the combination of Elitist-Genetic algorithm (E-GA) and finite strip method (FSM). The design variables are the number of layers, the fiber orientation angles, edge conditions and plate length/width ratios. The classical laminated plate theory is used to calculate the natural frequencies and the fitness function is computed with a semi-analytical finite strip method which has been developed on the basis of full energy methods. To improve the speed of the optimization process, the elitist strategy is used in the Genetic algorithm. The performance of the E-GA is also compared with the simple genetic algorithm and shows the good efficiency of the E-GA algorithm. A multi-objective optimization strategy for optimal stacking sequence of laminated box structure is also presented, with respect to the first natural frequency and critical buckling load, using the weighted summation method to demonstrate the effectiveness of the E-GA. Results are corroborated by comparing with other optimum solutions available in the literature, wherever possible.