A hybrid evolutionary graph-based multi-objective algorithm for layout optimization of truss structures

In this paper a new graph-based evolutionary algorithm, gM-PAES, is proposed in order to solve the complex problem of truss layout multi-objective optimization. In this algorithm a graph-based genotype is employed as a modified version of Memetic Pareto Archive Evolution Strategy (M-PAES), a well-known hybrid multi-objective optimization algorithm, and consequently, new graph-based crossover and mutation operators perform as the solution generation tools in this algorithm. The genetic operators are designed in a way that helps the multi-objective optimizer to cover all parts of the true Pareto front in this specific problem. In the optimization process of the proposed algorithm, the local search part of gM-PAES is controlled adaptively in order to reduce the required computational effort and enhance its performance. In the last part of the paper, four numeric examples are presented to demonstrate the performance of the proposed algorithm. Results show that the proposed algorithm has great ability in producing a set of solutions which cover all parts of the true Pareto front.     

车载可补货无人配送小车配送路径研究

无人配送小车由于不适合长距离运输,可与货车搭配完成“最后一公里”配送任务以增加服务范围,这对车辆路径优化问题提出了新的挑战。针对配送小车数量有限、城市配送货物量大且货车停靠限制的特点,提出无人配送小车可补货的大车-小车路径优化问题,即一辆货车搭载多台无人配送小车,由无人配送小车给客户送货,无人配送小车可在货车处补充货物并执行多行程配送。构建以总配送距离最短为目标的整数规划模型,针对此模型设计混合遗传大邻域搜索算法,在遗传算法基础上增加大邻域搜索算法对个体优化。在算法优化过程中先优化小车路径,再在小车路径基础上优化大车路径。数值实验表明,对于小规模问题,所提算法最多花费CPLEX求解时间的6%便获得最优解;在改造的Solomon数据上,所提算法相对于遗传算法平均有95.5%的计算结果优势,相对于大邻域搜索算法平均有7.2%的计算结果优势,且数据量越大,优势越大。     

A hybrid genetic algorithm for the multi-depot open vehicle routing problem

This paper studies the multi-depot open vehicle routing problem (MDOVRP), a variant of the vehicle routing problem (VRP), in which vehicles start from several depots and are not required to return to the depot. Despite the vast amount of literature about VRPs, the MDOVRP has received very little attention from researchers. In this paper, a new hybrid genetic algorithm is presented for finding the routes that minimize the traveling cost of the vehicles. Computational results on a number of test instances indicate the proposed algorithm dominates the CPLEX solver and the existing approach in the literature. Meanwhile, experiments are conducted on multi-depot VRP benchmarks, and the results are compared with a sophisticated tabu search approach and an exact method.     

Optimization of process plans using a constraint-based tabu search approach

A computer-aided process planning system should ideally generate and optimize process plans to ensure the application of good manufacturing practices and maintain the consistency of the desired functional specifications of a part during its production processes. Crucial processes, such as selecting machining resources, determining set-up plans and sequencing operations of a part should be considered simultaneously to achieve global optimal solutions. In this paper, these processes are integrated and modelled as a constraint-based optimization problem, and a tabu search-based approach is proposed to solve it effectively. In the optimization model, costs of the utilized machines and cutting tools, machine changes, tool changes, set-ups and departure from good manufacturing practices (penalty function) are the optimization evaluation criteria. Precedence constraints from the geometric and manufacturing interactions between features and their related operations in a part are defined and classified according to their effects on the plan feasibility and processing quality. A hybrid constraint-handling method is developed and embedded in the optimization algorithm to conduct the search efficiently in a large-size constraint-based space. Case studies, which are used for comparing this approach with the genetic algorithm and simulated annealing approaches, and the proposed constraint-handling method and other constraint methods, are discussed to highlight the performance of this approach in terms of the solution quality and computational efficiency of the algorithm.     

Computer-aided optimization of multi-pass turning operations for continuous forms on CNC lathes

The Computer Numerical Control (CNC) machine is one of the most effective production facilities used in manufacturing industry. Determining the optimal machining parameters is essential in the machining process planning since the machining parameters significantly affect production cost and quality of machined parts. Previous studies involving machining optimization of turning operations concentrated primarily on developing machining models for bar components. Machined parts on the CNC lathes, however, typically have continuous forms. In this study, we formulate an optimization model for turned parts with continuous forms. Also, a stochastic optimization method based on the simulated annealing algorithm and the pattern search is applied to solve this machining optimization problem. Finally, the applications of the developed machining model and the proposed optimization algorithm are established through the numerical examples.     

A hybrid of mode-pursuing sampling method and genetic algorithm for minimization of injection molding warpage

This paper presents a hybrid optimization method for minimizing the warpage of injection molded plastic parts. This proposed method combines a mode-pursuing sampling (MPS) method with a conventional global optimization algorithm, i.e. genetic algorithm, to search for the optimal injection molding process parameters. During optimization, Kriging surrogate modeling strategy is also exploited to substitute the computationally intensive Computer-Aided Engineering (CAE) simulation of injection molding process. With the application of genetic algorithm, the “likelihood-global optimums” are identified; and the MPS method generates and chooses new sample points in the neighborhood of the current “likelihood-global optimums”. By integrating the two algorithms, a new sampling guidance function is proposed, which can divert the search process towards the relatively unexplored region resulting in less likelihood of being trapped at the local minima. A case study of a food tray plastic part is presented, with the injection time, mold temperature, melt temperature and packing pressure selected as the design variables. This case study demonstrates that the proposed optimization method can effectively reduce the warpage in a computationally efficient manner.     

满足“一刀切”要求的木工板排样优化研究

 针对木工板手工排样效率低和材料利用率低问题,提出木工板“一刀切”排样优化算法．在剩余矩形填充算法中添加启发式分块原则,改进的剩余矩形填充算法满足“一刀切”工艺要求．采用遗传算法对矩形件进行排样优化,以提高木工板利用率,降低企业生产成本．为提高算法的优化精度,使用基于指数变换的非线性动态适应度函数,引入精英保护策略,应用部分填充交叉（partially matched crossover）算子．结合剩余矩形填充“一刀切”算法对遗传种群进行解码计算原料利用率,并作为适应度函数值,进行迭代搜索最优解．排样实例表明木工板“一刀切”排样优化算法能够很好地解决多品种大规模木工板排样问题．     

基于改进免疫遗传优化蚁群算法的移动机器人路径寻优研究

针对移动机器人路径规划中使用蚁群算法(ACO)易陷入局部最优和收敛速度慢的问题,提出了一种适用于机器人静态路径寻优的改进免疫遗传优化蚁群算法(IMGAC)。该算法可以根据实际情况自动调整变异概率和变异方式,以及自动调节个体免疫位的长度,将通过改进的变异算子和免疫算子嵌入蚁群算法来提高全局寻优能力与收敛速度。仿真及实验表明:相比于经典ACO算法以及最大最小蚂蚁系统,IMGAC算法收敛速度更快,全局寻优能力更强。利用该算法寻找移动机器人最优路径,提高了静态路径寻优的效果和效率。     

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.     

Construction heuristics for generating tool paths for laser cutters

This paper deals with generating paths for cutting irregular parts nested on thin or thick metal sheets. The objective is to minimise the total time required to cut all parts from the metal sheet explicitly taking the cost of piercing and pre-cutting into account. The problem is modelled as a generalised travelling salesperson problem with special precedence constraints. A set of construction heuristics is presented that incorporates the constraints originating from inner–outer contours, common cuts, piercing points and pre-cuts. Computational tests on a set of real-life cutting problems show that our solution approach is able to generate tool paths that for thick plates spend on average 33.4% less time than those generated by a commercial package for air movements, pre-cuts and sharp angle macros with cutting and piercing times being equal.     

Two-dimensional phase unwrapping using a hybrid genetic algorithm

A novel hybrid genetic algorithm (HGA) is proposed to solve the branch-cut phase unwrapping problem. It employs both local and global search methods. The local search is implemented by using the nearest-neighbor method, whereas the global search is performed by using the genetic algorithm. The branch-cut phase unwrapping problem [a nondeterministic polynomial (NP-hard) problem] is implemented in a similar way to the traveling-salesman problem, a very-well-known combinational optimization problem with profound research and applications. The performance of the proposed algorithm was tested on both simulated and real wrapped phase maps. The HGA is found to be robust and fast compared with three well-known branch-cut phase unwrapping algorithms.     

Particle swarm optimization approach for multi-objective composite box-beam design

This paper presents a multi-agent search technique to design an optimal composite box-beam helicopter rotor blade. The search technique is called particle swarm optimization (‘inspired by the choreography of a bird flock’). The continuous geometry parameters (cross-sectional dimensions) and discrete ply angles of the box-beams are considered as design variables. The objective of the design problem is to achieve (a) specified stiffness value and (b) maximum elastic coupling. The presence of maximum elastic coupling in the composite box-beam increases the aero-elastic stability of the helicopter rotor blade. The multi-objective design problem is formulated as a combinatorial optimization problem and solved collectively using particle swarm optimization technique. The optimal geometry and ply angles are obtained for a composite box-beam design with ply angle discretizations of 10°, 15° and 45°. The performance and computational efficiency of the proposed particle swarm optimization approach is compared with various genetic algorithm based design approaches. The simulation results clearly show that the particle swarm optimization algorithm provides better solutions in terms of performance and computational time than the genetic algorithm based approaches.     

A hybrid Hopfield network-genetic algorithm approach to optimal process plan selection

In the automated manufacturing environment, different sets of alternative process plans can normally be generated to manufacture each part. However, this entails considerable complexities in solving the process plan selection problem because each of these process plans demands specification of their individual and varying manufacturing costs and manufacturing resource requirements, such as machines, fixtures/jigs, and cutting tools. In this paper the problem of selecting exactly one representative from a set of alternative process plans for each part is formulated. The purpose is to minimize, for all the parts to be manufactured, the sum of both the costs of the selected process plans and the dissimilarities in their manufacturing resource requirements. The techniques of Hopfield neural network and genetic algorithm are introduced as possible approaches to solve such a problem. In particular, a hybrid Hopfield network-genetic algorithm approach is also proposed in this paper as an effective near-global optimization technique to provide a good quality solution to the process plan selection problem. The effectiveness of the proposed hybrid approach is illustrated by comparing its performance with that of some published approaches and other optimization techniques, by using several examples currently available in the literature, as well as a few randomly generated examples.     

A two-stage path planning approach for multiple car-like robots based on PH curves and a modified harmony search algorithm

In this article, collision-avoidance path planning for multiple car-like robots with variable motion is formulated as a two-stage objective optimization problem minimizing both the total length of all paths and the task’s completion time. Accordingly, a new approach based on Pythagorean Hodograph (PH) curves and Modified Harmony Search algorithm is proposed to solve the two-stage path-planning problem subject to kinematic constraints such as velocity, acceleration, and minimum turning radius. First, a method of path planning based on PH curves for a single robot is proposed. Second, a mathematical model of the two-stage path-planning problem for multiple car-like robots with variable motion subject to kinematic constraints is constructed that the first-stage minimizes the total length of all paths and the second-stage minimizes the task’s completion time. Finally, a modified harmony search algorithm is applied to solve the two-stage optimization problem. A set of experiments demonstrate the effectiveness of the proposed approach.     

带装载能力的需求依背包拆分车辆路径问题

以最小化总的旅行时间为优化目标,以单车场、单车型、装载能力和需求依背包拆分等为约束条件,将以往客户需求不可拆分的条件松弛为依背包来离散拆分,建立了带装载能力的需求依背包拆分VRP（CVRPSDB）的单目标数学模型。设计了一个自适应禁忌搜索算法（ATSA）对模型进行求解。该算法采用了自适应惩罚机制,构建了一个多邻域结构体,并针对客户点与背包都设计了相应的邻域操作算子,较好地适应了客户需求量的离散拆分程度。经算例测试与文献对比,验证了所设计模型与算法的有效性。     

Reloading Nuclear Reactor Fuel Using Mixed-Integer Nonlinear Optimization

A nodal nuclear reactor reload pattern optimization model is solved using mixed-integer nonlinear optimization techniques. Unlike currently used heuristic search methods, this method enables continuous optimization of the amount of Burnable Poisons in fresh fuel bundles in a natural way, which is shown in the first part of the article. The second part treats an algorithmic extension using dedicated cuts in a mixed-integer nonlinear optimization algorithm, which push the optimization towards solutions where local power peaks in parts of the core are avoided.     

Multi-objective planning of a multiple geostationary spacecraft refuelling mission

The multi-objective optimization of multiple geostationary spacecraft refuelling is investigated in this article. A servicing spacecraft (SSc) and a propellant depot (PD), both parked initially in geostationary Earth orbit (GEO), are utilized to refuel multiple GEO targets of known propellant demand. The capacitated SSc is expected to rendezvous with fuel-deficient GEO targets or the PD for the purpose of refuelling or getting refuelled. The multiple geostationary spacecraft refuelling problem is treated as a multi-variable combinatorial optimization problem with the principal objective of minimizing the propellant consumption and the mission duration. A two-level optimization model is built, and the design variables are the refuelling order X, the refuelling time T and the binary decision variable S. The non-dominated sorting genetic algorithm is employed to solve the up-level optimization problem. For the low-level optimization, an exact algorithm is proposed. Finally, numerical simulations are presented to illustrate the effectiveness and validity of the proposed approach.     

An optimization technique using the characteristics of genetic algorithm

Optimization problems could happen often in discrete or discontinuous search space. Therefore, the traditional gradient‐based methods are not able to apply to this kind of problems. The discrete design variables are considered reasonably and the heuristic techniques are generally adopted to solve this problem, and the genetic algorithm based on stochastic search technique is one of these. The genetic algorithm method with discrete variables can be applied to structural optimization problems, such as composite laminated structures or trusses. However, the discrete optimization adopted in genetic algorithm gives rise to a troublesome task that is a mapping between each strings and discrete variables. And also, its solution quality could be restricted in some cases. In this study, a technique using the genetic algorithm characteristics is developed to utilize continuous design variables instead of discrete design variables in discontinuous solution spaces. Additionally, the proposed algorithm, which is manipulating a fitness function artificially, is applied to example problems and its results are compared with the general discrete genetic algorithm. The example problems are to optimize support positions of an unstable structure with discontinuous solution spaces.     

求解连续空间优化问题的扩展粒子蚁群算法

扩展蚁群算法是蚁群算法创始人Dorigo提出的一种用于求解连续空间优化问题的最新蚁群算法,但该算法的收敛速度参数和局部搜索参数取值缺乏理论指导,因此其性能受算法参数影响较大.本文提出一种求解连续空间优化的扩展粒子蚁群算法,将粒子群算法嵌入到扩展蚁群算法中用于在线优化扩展蚁群算法参数,减少了参数人为调整的盲目性.从而改善扩展蚁群算法的寻径行为.通过将本文提出的算法与遗传算法、克隆选择算法、蚁群算法、扩展蚁群算法对5种典型测试函数优化的结果对比表明,本文算法在搜索速度和全局搜索能力方面均优于其它算法.     

竹林皆伐机路径规划系统设计及计算机仿真

目的设计一种中幼竹林皆伐机路径智能规划系统,实现伐竹机伐竹的路径规划功能,使伐竹机可以遍历需要伐竹的全部节点并避开障碍。方法针对中幼竹林皆伐的特点,探讨一种迪杰斯特拉及A*混合算法,用于解决伐竹机路径规划中的路径优化问题。通过C语言编程,来建立一种基于改进的迪杰斯特拉及A*混合算法的中幼竹林皆伐机路径智能规划系统仿模型,并使用C++编程,实现系统模型的仿真,并调用Windows GDI实现仿真结果的显示。结果仿真结果显示,采用文中建立的中幼竹林皆伐机路径规划系统进行伐竹机的路径规划,实现了伐竹路径规划的目标,且相较直接采伐的路径,伐竹机伐竹总里程降低了47.6%,节省了伐竹机伐竹总里程,大大提升了伐竹效率。结论文中所讨论的改进的迪杰斯特拉及A*混合算法可以实现伐竹路径规划的功能,路径规划系统可以求得最优伐竹路径的一个近似解。