Hybridizing electromagnetism-like mechanism algorithm with migration strategy for layout and size optimization of truss structures with frequency constraints

The optimal design of a truss structure with dynamic frequency constraints is a highly nonlinear optimization problem with several local optimums in its search space. In this type of structural optimization problems, the optimization methods should have a high capability to escape from the traps of the local optimums in the search space. This paper presents hybrid electromagnetism-like mechanism algorithm and migration strategy (EM–MS) for layout and size optimization of truss structures with multiple frequency constraints. The electromagnetism-like mechanism (EM) algorithm simulates the attraction and repulsion mechanism between the charged particles in the field of the electromagnetism to find optimal solutions, in which each particle is a solution candidate for the optimization problem. In the proposed EM–MS algorithm, two mechanisms are utilized to update the position of particles: modified EM algorithm and a new migration strategy. The modified EM algorithm is proposed to effectively guide the particles toward the region of the global optimum in the search space, and a new migration strategy is used to provide efficient exploitation between the particles. In order to test the performance of the proposed algorithm, this study utilizes five benchmark truss design examples with frequency constraints. The numerical results show that the EM–MS algorithm is an alternative and competitive optimizer for size and layout optimization of truss structures with frequency constraints.     

Multiobjective layout optimization of robotic cellular manufacturing systems

This paper proposes a multiobjective layout optimization method for the conceptual design of robot cellular manufacturing systems. Robot cellular manufacturing systems utilize one or more flexible robots which can carry out a large number of operations, and can conduct flexible assemble processes. The layout design stage of such manufacturing systems is especially important since fundamental performances of the manufacturing system under consideration are determined at this stage. In this paper, the design criteria for robot cellular manufacturing system layout designs are clarified, and objective functions are formulated. Next, layout design candidates are represented using a sequence-pair scheme to avoid interference between assembly system components, and the use of dummy components is proposed to represent layout areas where components are sparse. A multiobjective genetic algorithm is then used to obtain Pareto optimal solutions for the layout optimization problems. Finally, several numerical examples are provided to illustrate the effectiveness and usefulness of the proposed method.     

A hybrid particle swarm optimization with local search for stochastic resource allocation problem

Discrete and stochastic resource allocation problems are difficult to solve because of the combinatorial explosion of feasible search space. Resource management is important area and a significant challenge is encountered when considering the relationship between uncertainty factors and inputs and outputs of processes in the service and manufacturing systems. These problems are unavailable in closed-form expressions for objective function. In this paper, we propose \(\hbox {PSO}_{\mathrm{OTL}}\), a new approach of the hybrid simulation optimization structure, to achieve a near optimal solution with few simulation replications. The basic search algorithm of particle swarm optimization (PSO) is applied for proper exploration and exploitation. Optimal computing budget allocation combined with PSO is used to reduce simulation replications and provide reliable evaluations and identifications for ranking particles of the PSO procedure. Two-sample t tests were used to reserve good particles and maintain the diversity of the swarm. Finally, trapping in local optimum in the design space was overcome by using the local search method to generate new diverse particles when a similar particle exists in the swarm. This study proposed intelligent manufacturing technology, called the \(\hbox {PSO}_{\mathrm{OTL}}\), and compared it with four algorithms. The results obtained demonstrate the superiority of \(\hbox {PSO}_{\mathrm{OTL}}\) in terms of search quality and computational cost reduction.     

Nature inspired algorithms to optimize robot workcell layouts

Multi-objective layout optimization methods for the conceptual design of robot cellular manufacturing systems are proposed in this paper. Robot cellular manufacturing systems utilize one or more flexible robots which can carry out a large number of operations, and can conduct flexible assemble processes. The layout design stage of such manufacturing systems is especially important since fundamental performances of the manufacturing system under consideration are determined at this stage. Layout area, operation time and manipulability of robot are the three important criteria when it comes to designing manufacturing system. The use of nature inspired algorithms are not extensively explored to optimize robot workcell layouts. The contribution in this paper is the use of five nature-inspired algorithms, viz. genetic algorithm (GA), differential evolution (DE), artificial bee colony (ABC), charge search system (CSS) and particle swarm optimization (PSO) algorithms and to optimize the three design criteria simultaneously. Non-dominated sorting genetic algorithm-II is used to handle multiple objectives and to obtain pareto solutions for the problems considered. The performance of sequence pair and B*-Tree layout representation schemes are also evaluated. It is found that sequence pair scheme performs better than B*-Tree representation and it is used in the algorithms. Numerical examples are provided to illustrate the effectiveness and usefulness of the proposed methods. It is observed that PSO performs better over the other algorithms in terms of solution quality.     

求解约束优化问题的新方法

提出了用于解决约束优化问题的新的类电磁机制算法,针对约束优化问题,利用惩罚函数法,构造了新的适应度函数,将约束问题转化为无约束问题求解,结合类电磁机制算法的寻优特点,设计了适合于问题特点的局部搜索过程,并重新定义了粒子的电荷和力的计算公式,以使在算法实现过程中,引导不可行点逐步向可行点转化,并最终找到问题的最优解。数值模拟结果验证了算法的有效性。     

一种基于可变多簇结构的动态概率粒子群优化算法

针对传统粒子群优化算法中全连接型拓扑和环形拓扑的特点,引入了一种粒子群信息共享方式——多簇结构,进而基于多簇结构提出了动态可变拓扑策略以协调动态概率粒子群优化算法的勘探和开采能力,并从理论上分析了最优信息在各种拓扑中的传播,同时从图论角度分析了几种经典拓扑以及动态可变多簇结构的统计特性.通过典型的Benchmark函数优化问题测试并比较了几种经典拓扑以及可变拓扑在高斯动态粒子群优化算法中的性能.实验结果表明,基于多簇结构的可变拓扑策略在求解复杂优化问题时优势明显,可以有效地避免算法陷入局部最优,在保证收敛速度的同时增强了算法的全局搜索能力.     

A heuristic procedure for the integrated facility layout design and flow assignment problem

We present an efficient iterative heuristic procedure for solving the integrated layout design and product flow assignment problem. The layout design decisions involve planar location of unequal-area machines with duplicates. The product flows are assigned to machines according to the product processing routes. The integrated decision problem is a nonlinear mixed integer model which cannot be efficiently solved using classical methods for large problems. We propose a novel integrated heuristic procedure based on the alternating heuristic, a perturbation algorithm and sequential location heuristic. Since the alternating heuristic between facility layout design and product-machine assignment sub-problems terminates with local optima, we developed a perturbation algorithm based on assignment decisions. The results of an experimental study show that proposed procedure is both efficient and effective in identifying quality solutions for small to very large-sized problems.     

基于改进人工蜂群算法的体声波滤波器自动布局

针对体声波（BAW）滤波器布局中芯片面积利用率低、设计周期长、制造成本高等问题,提出了一种基于改进人工蜂群算法的BAW滤波器自动布局方法。首先构建了自动布局的评价模型,通过结合利用圆形容器的顺序布局和基于旋转角度编码的聚拢布局来简化自动布局的难度;同时采用了一种改进人工蜂群算法去搜寻BAW滤波器的最优顺序布局和聚拢布局方案;最后,结合多组BAW滤波器的设计案例验证自动布局算法的有效性,其布局有效面积占比提升约17%,整体布局呈方形,且完成滤波器的自动布局仅需30 min。结果表明在保证BAW滤波器性能的前提下,算法可有效提升芯片面积利用率、大幅缩减滤波器的设计周期。     

多策略粒子群优化算法

为了克服粒子群优化算法易早熟、局部搜索能力弱的问题,提出了一种改进的粒子群优化算法--多策略粒子群优化算法。在群体寻优过程中,各粒子根据搜索到的最优位置的变动情况,从几种备选的策略中抉择出当代的最优搜索策略。其中,最优粒子有最速下降策略、矫正下降策略和随机移动策略可以选择,非最优粒子有聚集策略和扩散策略可以选择。四个典型测试函数的数值实验结果表明,新提出的算法比标准粒子群优化算法具有更强和更稳定的全局搜索能力。     

A simulated annealing algorithm for dynamic layout problem

Increased level of volatility in today's manufacturing world demanded new approaches for modelling and solving many of its well-known problems like the facility layout problem. Over a decade ago Rosenblatt published a key paper on modelling and solving dynamic version of the facility layout problems. Since then, various other researchers proposed new and improved models and algorithms to solve the problem. Balakrishnan and Cheng have recently published a comprehensive review of the literature about this subject. The problem was defined as a complex combinatorial optimisation problem. The efficiency of SA in solving combinatorial optimisation problems is very well known. However, it has recently not been applied to DLP based on the review of the available literature. In this research paper a SA-based procedure for DLP is developed and results for test problems are reported.

Scope and purpose

One of the characteristic of today's manufacturing environments is volatility. Under a volatile environment (or dynamic manufacturing environment) demand is not stable. To operate efficiently under such environments facilities must be adaptive to changing demand conditions. This requires solution of the dynamic layout problem (DLP). DLP is a complex combinatorial optimisation problem for which optimal solutions can be found for small size problems. This research paper makes use of a SA algorithm to solve the DLP. Simulated annealing (SA) is a well-established stochastic neighbourhood search technique. It has a potential to solve complex combinatorial optimisation problems. The paper presents in detail how to apply SA to solve DLP and an extensive computational study. The computational study shows that SA is quite effective in solving dynamic layout problems.     

Discrete particle swarm optimization method for the large-scale discrete time–cost trade-off problem

Despite many research studies have concentrated on designing heuristic and meta-heuristic methods for the discrete time–cost trade-off problem (DTCTP), very little success has been achieved in solving large-scale instances. This paper presents a discrete particle swarm optimization (DPSO) to achieve an effective method for the large-scale DTCTP. The proposed DPSO is based on the novel principles for representation, initialization and position-updating of the particles, and brings several benefits for solving the DTCTP, such as an adequate representation of the discrete search space, and enhanced optimization capabilities due to improved quality of the initial swarm. The computational experiment results reveal that the new method outperforms the state-of-the-art methods, both in terms of the solution quality and computation time, especially for medium and large-scale problems. High quality solutions with minor deviations from the global optima are achieved within seconds, for the first time for instances including up to 630 activities. The main contribution of the proposed particle swarm optimization method is that it provides high quality solutions for the time–cost optimization of large size projects within seconds, and enables optimal planning of real-life-size projects.     

An interval space reducing method for constrained problems with particle swarm optimization

In this paper, we propose a method for solving constrained optimization problems using interval analysis combined with particle swarm optimization. A set inverter via interval analysis algorithm is used to handle constraints in order to reduce constrained optimization to quasi unconstrained one. The algorithm is useful in the detection of empty search spaces, preventing useless executions of the optimization process. To improve computational efficiency, a space cleaning algorithm is used to remove solutions that are certainly not optimal. As a result, the search space becomes smaller at each step of the optimization procedure. After completing pre-processing, a modified particle swarm optimization algorithm is applied to the reduced search space to find the global optimum. The efficiency of the proposed approach is demonstrated through comprehensive experimentation involving 100 000 runs on a set of well-known benchmark constrained engineering design problems. The computational efficiency of the new method is quantified by comparing its results with other PSO variants found in the literature.     

一种多基元类的布局迁移自适应算法及在闸机设计中的应用

布局问题研究物体的布局先后或布局定位以满足设计要求,布局迁移设计是在已有布局基础上高效设计新布局的方法。在轨道交通自动控制系统中,闸机表面传感器的布局对人与物的识别有重要的影响。为了实现闸机在不同地域环境中的快速设计,首先以闸机布局中的传感器作为研究对象,进行基元划分,提出了多种基元类型;并分析了基于拓扑结构的基元迁移变换方法,研究了人群特征因素、机械结构约束的数学表达;然后提出基于包围圆搜索的基元运动与干涉分析算法,其参数能够根据求解精度进行自适应调整;并利用多目标归一化函数对各基元的解进行择优,以获取最终布局。最后以18对传感器的闸机布局设计为例进行实例分析,应用此方法并借助于Visual Basic可视化编译平台,实现了闸机在不同地域环境中的传感器布局快速设计。     

带极值抖动的变尺度粒子群优化算法

为克服粒子群优化算法容易陷入局部最优解的问题,提出一种带极值抖动的变尺度粒子群优化算法,该算法在粒子进化过程中动态调整学习因子,改善粒子的搜索性能,利用极值抖动方法帮助粒子逃离局部最优解,采用变尺度方法逐步缩小算法的优化范围,提高算法搜索密度。实验表明,该算法对9个具有代表性的基准测试函数,其优化效率及优化精度均优于以往提出的典型粒子群优化改进算法。     

Applying simulated annealing for designing cellular manufacturing systems using MDmTSP

The design of cellular manufacturing systems involves many structural and operational issues. One of the important design steps is the formation of part families and machine cells (cell formation). Despite a large number of papers on cell formation published worldwide, only a handful incorporates operation sequence in layout design (intra-cell move calculations). We propose a solution to solve the part-family and machine-cell formation problem considering the within-cell layout problem, simultaneously. In this paper, the cellular manufacturing system is formulated as a multiple departures single destination multiple travelling salesman problem (MDmTSP) and a solution methodology based on simulated annealing is proposed to solve the formulated model. Numerical examples show that the proposed method is efficient and effective in finding optimal solutions. The results also indicate that the proposed approach performs well compared to some well-known cell formation methods.     

A parallel boundary search particle swarm optimization algorithm for constrained optimization problems

During the past decade, considerable research has been conducted on constrained optimization problems (COPs) which are frequently encountered in practical engineering applications. By introducing resource limitations as constraints, the optimal solutions in COPs are generally located on boundaries of feasible design space, which leads to search difficulties when applying conventional optimization algorithms, especially for complex constraint problems. Even though penalty function method has been frequently used for handling the constraints, the adjustment of control parameters is often complicated and involves a trial-and-error approach. To overcome these difficulties, a modified particle swarm optimization (PSO) algorithm named parallel boundary search particle swarm optimization (PBSPSO) algorithm is proposed in this paper. Modified constrained PSO algorithm is adopted to conduct global search in one branch while Subset Constrained Boundary Narrower (SCBN) function and sequential quadratic programming (SQP) are applied to perform local boundary search in another branch. A cooperative mechanism of the two branches has been built in which locations of the particles near boundaries of constraints are selected as initial positions of local boundary search and the solutions of local boundary search will lead the global search direction to boundaries of active constraints. The cooperation behavior of the two branches effectively reinforces the optimization capability of the PSO algorithm. The optimization performance of PBSPSO algorithm is illustrated through 13 CEC06 test functions and 5 common engineering problems. The results are compared with other state-of-the-art algorithms and it is shown that the proposed algorithm possesses a competitive global search capability and is effective for constrained optimization problems in engineering applications.     

自适应变异粒子群算法

为了解决粒子群种群多样性低、容易陷入局部最优的缺点,结合最优粒子和其他粒子在种群中的不同作用,给出了一种自适应变异粒子群算法。算法中最优粒子根据种群进化程度,自适应调整自身搜索邻域大小,增强种群的局部搜索能力;对非最优粒子的位置进行小概率的随机初始化,当其速度为零时,速度自适应变化,以便增强种群多样性和全局搜索能力。仿真实验中,将算法应用于6个典型复杂函数优化问题,并与其他变异粒子群算法比较,结果表明,增强种群多样性的同时提高了局部搜索能力。     

Solving a multi-floor layout design model of a dynamic cellular manufacturing system by an efficient genetic algorithm

This paper presents a mixed-integer programming model for a multi-floor layout design of cellular manufacturing systems (CMSs) in a dynamic environment. A novel aspect of this model is to concurrently determine the cell formation (CF) and group layout (GL) as the interrelated decisions involved in the design of a CMS in order to achieve an optimal (or near-optimal) design solution for a multi-floor factory in a multi-period planning horizon. Other design aspects are to design a multi-floor layout to form cells in different floors, a multi-rows layout of equal area facilities in each cell, flexible reconfigurations of cells during successive periods, distance-based material handling cost, and machine depot keeping idle machines. This model incorporates with an extensive coverage of important manufacturing features used in the design of CMSs. The objective is to minimize the total costs of intra-cell, inter-cell, and inter-floor material handling, purchasing machines, machine processing, machine overhead, and machine relocation. Two numerical examples are solved by the CPLEX software to verify the performance of the presented model and illustrate the model features. Since this model belongs to NP-hard class, an efficient genetic algorithm (GA) with a matrix-based chromosome structure is proposed to derive near-optimal solutions. To verify its computational efficiency in comparison to the CPLEX software, several test problems with different sizes and settings are implemented. The efficiency of the proposed GA in terms of the objective function value and computational time is proved by the obtained results.     

Sequential approximation optimization assisted particle swarm optimization for expensive problems

In this paper, an efficient sequential approximation optimization assisted particle swarm optimization algorithm is proposed for optimization of expensive problems. This algorithm makes a good balance between the search ability of particle swarm optimization and sequential approximation optimization. Specifically, the proposed algorithm uses the optima obtained by sequential approximation optimization in local regions to replace the personal historical best particles and then runs the basic particle swarm optimization procedures. Compared with particle swarm optimization, the proposed algorithm is more efficient because the optima provided by sequential approximation optimization can direct swarm particles to search in a more accurate way. In addition, a space partition strategy is proposed to constraint sequential approximation optimization in local regions. This strategy can enhance the swarm diversity and prevent the preconvergence of the proposed algorithm. In order to validate the proposed algorithm, a lot of numerical benchmark problems are tested. An overall comparison between the proposed algorithm and several other optimization algorithms has been made. Finally, the proposed algorithm is applied to an optimal design of bearings in an all-direction propeller. The results show that the proposed algorithm is efficient and promising for optimization of the expensive problems.     

基于种群分区的多策略自适应多目标粒子群算法

在多目标粒子群优化算法中,平衡算法收敛性和多样性是获得良好分布和高精度Pareto前沿的关键,多数已提出的方法仅依靠一种策略引导粒子搜索,在解决复杂问题时算法收敛性和多样性不足.为解决这一问题,提出一种基于种群分区的多策略自适应多目标粒子群优化算法.采用粒子收敛性贡献对算法环境进行检测,自适应调整粒子的探索和开发过程;为准确制定不同性能的粒子的搜索策略,提出一种多策略的全局最优粒子选取方法和多策略的变异方法,根据粒子的收敛性评价指标,将种群划分为3个区域,将粒子性能与算法寻优过程结合,提升种群中各个粒子的搜索效率;为解决因选取的个体最优粒子不能有效指导粒子飞行方向,使算法停滞,陷入局部最优的问题,提出一种带有记忆区间的个体最优粒子选取方法,提升个体最优粒子选取的可靠性并加快粒子收敛过程;采用包含双性能测度的融合指标维护外部存档,避免仅根据粒子密度对外部存档维护时,删除收敛性较好的粒子,导致种群产生退化,影响粒子开发能力.仿真实验结果表明,与其他几种多目标优化算法相比,该算法具有良好的收敛性和多样性.