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The purpose of this paper is to develop a feasible sequence-oriented new discrete particle swarm optimization (NDPSO) algorithm with novel particles’ updating mechanism for solving simple assembly line balancing problems (SALBPs).

In the NDPSO, a task-oriented representation is adopted to solve type I and type II SALBPs, and a particle directly represents a feasible task sequence (FTS) as a permutation. Then, the particle (permutation) is updated as a whole using the geometric crossover based on the edit distance with swaps for two permutations. Furthermore, the fragment mutation with adaptive mutation probability is incorporated into the NDPSO to improve exploration ability.

Case study illustrates the effectiveness of the NDPSO. Comparative results between the NDPSO and existing real-encoded PSO (CPSO) and direct discrete PSO (DDPSO) against benchmark instances of type I SALBP and type II SALBP show promising higher performance of the proposed NDPSO.

A novel particles’ updating mechanism for FTS-encoded particle is proposed to solve the SALBPs. The comparative results indicate that updating of FTS as a whole seems superior to existing updating of FTS by fragment with respect to exploration ability for solving SALBPs. The novel particles’ updating mechanism is also applicable to generalized assembly line balancing problems.

Two-level support with Level 1 consisting of a set of beams and Level 2 consisting of a tree-like structure is an efficient support structure for extrusion-based additive manufacturing (EBAM). However, the literature for finding a slim two-level support is rare. The purpose of this paper is to design a lightweight two-level support structure for EBAM.

To efficiently solve the problem, the lightweight design problem is split into two subproblems: finding a slim Level 1 support and a slim Level 2 support. To solve these two subproblems, this paper develops three efficient metaheuristic algorithms, i.e. genetic algorithm (GA), genetic programming (GP) and particle swarm optimization (PSO). They are problem-independent and are powerful in global search. For the first subproblem, considering the path direction is a critical factor influencing the layout of Level 1 support, this paper solves it by splitting the overhang region into a set of subregions, and determining the path direction (vertical or horizontal) in each subregion using GA. For the second subproblem, a hybrid of two metaheuristic algorithms is proposed: the GP manipulates the topologies of the tree support, while the PSO optimizes the position of nodes and the diameter of tree branches. In particular, each chromosome is encoded as a single virtual tree for GP to make it easy to manipulate Crossover and Mutation. Furthermore, a local strategy of geometric search is designed to help the hybrid algorithm reach a better result.

Simulation results show that the proposed method is preferred over the existing method: it saves the materials of the two-level support up to 26.34%, the materials of the Level 1 support up to 6.62% and the materials of the Level 2 support up to 37.93%. The proposed local strategy of geometric search can further improve the hybrid algorithm, saving up to 17.88% of Level 2 support materials.

The proposed approach for sliming Level 1 support requires the overhanging region to be a rectilinear polygon and the path direction in a subregion to be vertical or horizontal. This limitation limits the further material savings of the Level 1 support. In future research, the proposed approach can be extended to handle an arbitrary overhang region, each with several choices of path directions.

The details of how to integrate the proposed algorithm into the open-source program CuraEngine 4.13.0 is presented. This is helpful for the designers and manufacturers to practice on their own 3D printers.

The path planning of the overhang is a critical factor influencing the distribution of supporting points and will thus influence the shape of the support structure. Different from existing approaches that use single path directions, the proposed method optimizes the volume of the support structure by planning hybrid paths of the overhangs.

The purpose of this paper is to introduce a novel methodology that has the capability of finding symmetrical and nonsymmetrical solutions in complex design domains without additional tuning when changing the design domain. These go from an academic design domain to a practical one.

Various crossovers operators are applied over the same representation using a genetic algorithm for truss structural optimization cases where literature solutions have a tendency to forced symmetry in order to find an optimal design with fewer iterations. Continuous‐discrete representations were cross‐bred by a uniform‐sbx simultaneous crossover, called zygote crossover. Specialized mutations operations are proposed to generate localized changes to improve the solution according with the design domain.

Design solutions found were lighter and stiffer when comparing against cases reported in current literature and in engineering practice. Also these solutions were found in fewer iterations.

The cases solved herein are complex and are a challenge for any optimization routine however practical design limitations are observed in the sense of out plane stability. Further comparisons cases are required in order to generate a less adjusted design, this is because the greenhouse solution had to be stiffened with out of plane bars to give it enough lateral stability.

Continuous‐discrete representations were cross‐bred by a uniform‐sbx simultaneous crossover, called natural crossover. Specialized mutations operations are proposed to generate localized changes to improve the solution according with the design domain. This scheme along with a less restrictive environment allows a wider exploration of search space.

The purpose of this paper is to optimize the performance of direct methanol fuel cells for portable applications by combining a non‐linear, fully coupled circuit model and a stochastic optimization procedure.

A novel non‐linear equivalent circuit that accounts for electrochemical reactions and charge generation inside catalyst layers, electronic and protonic conduction, methanol crossover through the membrane, mass transport of reactants inside diffusion layers is presented. The discharge dynamic of the fuel cell, depending on the initial methanol concentration and on the load profile, is modelled by using the mass conservation equation. The equivalent circuit is interfaced to a stochastic optimization procedure in order to maximize the battery duration while minimizing fuel crossover.

In the proposed circuit scheme, unlike semi‐empirical models, lumped circuit parameters are derived directly from mass transport and electric equations in order to fully describe the dynamic performance of the fuel cell. Physical and geometrical parameters are optimized in order to improve the system runtime. It is shown that a combined use of fuel cells and lithium batteries can improve the runtime of portable electronic devices compared to traditional supply systems based on lithium batteries only.

The one‐dimensional model of the micro fuel cell does not take into account possible transverse mass and electric charge flows in the fuel cell layers; most of the geometric and physics model parameters cannot be estimated from direct in situ or ex situ measurements.

Direct methanol fuel cells are nowadays a promising technology for replacing or complementing lithium batteries due to their high energy density. Most limiting features of direct methanol fuel cells are the fuel crossover and its slow oxidation kinetics. By using the proposed approach, fuel cell parameters can be optimized in order to enhance the discharge runtime and to reduce the methanol crossover.

The equivalent circuit model with optimized lumped non‐linear parameters can be used when designing power management units for portable electronic devices.

The purpose of this paper is to compare the stability of the three nanocarriers created by DNA origami method with different positions and numbers of crossovers

Nanocarriers are attractive components among a variety of nanostructures created by DNA origami and can have numerous applications in mechanical and medical engineering. For this reason, the current study compares three nanotubes with different positions and numbers of crossovers created by DNA origami method that can be utilized as nanocarriers. To investigate the structures, the DNA nanocarriers are studied at the human body temperature 310 K. Molecular dynamics simulations are used for this study. For a quantitative analysis of DNA nanocarriers, the areas of three hexagons at three different sites in each of the nanotubes are investigated. The results indicate that the number and position of crossovers are among the significant factors in the structure stability of nanocarriers. The analyses also revealed that although adding crossovers in locations with fewer crossovers increase structural stability, the position of crossovers can have different effects on the stability. DNA origami-based nanocarriers can be implemented in drug delivery, allow the nanocargoes to pass various surfaces and act as filters for passing cargoes of different dimensions and chemical structures.

The results indicate that the number and position of crossovers are among the significant factors in the structure stability of nanocarriers

In this paper, the stability of DNA origami nanocarriers with different positions and numbers of crossovers was investigated.

This paper presents a new surface reconstruction method based on complex form functions, genetic algorithms and neural networks. Surfaces can be reconstructed in an analytical representation format. This representation is optimal in the sense of least‐square fitting by predefined subsets of data points. The surface representations are achieved by evolution via repetitive application of crossover and mutation operations together with a back‐propagation algorithm until a termination condition is met. The expression is finally classified into specific combinations of basic functions. The proposed method can be used for CAD model reconstruction of 3D objects and free smooth shape modelling. We have implemented the system demonstration with Visual C++ and MatLab to enable real time surface visualisation in the process of design.

Dimensional quality of sheet metal assemblies is an important factor for the final product. However, the part tolerance is not easily controlled because of the spring back deformation during the stamping process. Selective assembly is a means to decrease assembly tolerance of the assembly from low-precision components. Therefore, the purpose of this paper is to propose a fully efficient method of selective assembly optimization based on an improved genetic algorithm for optimization toolbox (IGAOT) in MATLAB.

The method of influence coefficient is first applied to calculate the assembly variation of sheet metal components since the traditional rigid assembly variation model cannot be used due to welding deformation. Afterwards, the IGAOT is proposed to generate optimal selective groups, which consists of advantages of genetic algorithm for optimization toolbox (GAOT) and simulated annealing.

The cases of two simple planes and the tail lamp bracket assembly are used to illustrate the flowchart of optimizing combinations of selective groups. These cases prove that the proposed IGAOT has better precision than that of GAOT with the same parameters for selective assembly.

The research objective of this paper is to evaluate the changes from rigid bodies to sheet metal parts which are very complex for selective assembly. The method of IGAOT was proposed to the selected groups which has better precision than that of current optimization algorithms.

The purpose of this paper is to present a finite element analysis (FEA) of filament-wound composites, as well as application of these materials.

In this paper, a new finite element method of filament-wound composite is presented. The stress and strain fields in the composite cylinders are analyzed using the ABAQUS software packages for considering the filament undulation and crossover. The paper presented results of buckling load of composite cylinders with different types of filament-winding patterns.

The result of the example shows that the stress distributions are uniform along the cylinder length and around the circumference when the analytical approach is based on the conventional FEA. The stress distributions are not uniform along the cylinder length and around the circumference for considering the filament undulation and crossover. The stress units are arranged in a regular geometric pattern around circumference and along the axis of rotation. The analysis of the effect of filament-winding mosaic patterns on the mechanical characteristics of composite cylindrical is presented in the paper.

The stress and strain fields in the composite cylinders were analyzed for considering the filament undulation and crossover. The buckling load of composite cylinders with different types of filament-winding patterns was presented in this paper.

A new design framework for crossover operator is proposed based on the commonality concept. In the reproduction process the resulting hybrid crossover operator includes a local search scheme aiming to improve the genetic characteristics of the offspring. Commonality suggests that search should be driven in the neighbourhood of parents, and local optimisers can drive this search. The ranking of the offspring candidates is based on a local fitness function using approximations and appropriated heuristics linked to the structural optimisation problem. The goal of this approach is to identify and preserve the common schema of the two parents responsible for their high‐observed fitness. The proposed hybrid crossover operator is embedded into a genetic algorithm supported by an elitist strategy and its performance is compared with the parametrised uniform crossover.

In this paper, a set of safe adaptive genetic algorithms (sGAs) is proposed based on the Splicing/Decomposable encoding scheme and the efficient speed‐up strategies developed by Xu et al.. The proposed algorithms implement the self‐adaptation of the problem representation, selection and recombination operators at the levels of population, individual and component which commendably balance the conflicts between “reliability” and “efficiency”, as well as “exploitation” and “exploration” existed in the evolutionary algorithms. It is shown that the algorithms converge to the optimum solution in probability one. The proposed sGAs are experimentally compared with the classical genetic algorithm (CGA), non‐uniform genetic algorithm (nGA) proposed by Michalewicz, forking genetic algorithm (FGA) proposed by Tsutsui et al. and the classical evolution programming (CEP). The experiments indicate that the new algorithms perform much more efficiently than CGA and FGA do, comparable with the real‐coded GAs — nGA and CEP. All the algorithms are further evaluated through an application to a difficult real‐life application problem: the inverse problem of fractal encoding related to fractal image compression technique. The results for the sGA is better than those of CGA and FGA, and has the same, sometimes better performance compared to those of nGA and CEP.