Optimization of formworks shoring location as a continuous optimization problem

Improved random selection strategy, self-adaptive crossover and mutation operators, population co-evolution algorithm are adopted in this paper to set up a new genetic algorithm(GA), guaranteeing the universality of population, the thoroughness of solution and the execution efficiency of algorithm in solving multi-process, process crossover, work-piece flow and other condition based scheduling problem of flexible manufacturing system(FMS). The problem of GA in solving FMS scheduling is summarized in this paper, reasons and countermeasures for GA's pre-maturing are studied, longitudinal and transverse comprehensive state-based correlative self-adaptive crossover and mutation operators are raised according to the analysis on GA to avoid local optimization and pre-maturity, the co-evolutionary GA is designed to solve the scheduling model for FMS, and the algorithm is validated for the scheduling of box-type parts for precise machine tools.

We explore a general-purpose heuristic algorithm for finding high-quality solutions to continuous optimization problems. The method, called continuous extremal optimization (CEO), can be considered as an extension of extremal optimization and consists of two components, one which is responsible for global searching and the other which is responsible for local searching. The CEO's performance proves competitive with some more elaborate stochastic optimization procedures such as simulated annealing, genetic algorithms, and so on. We demonstrate it on a well-known continuous optimization problem: the Lennard-Jones cluster optimization problem.

Multi-objective optimization problems can be divided into continuous multi-objective optimization problems and discrete multi-objective optimization problems, and discrete multi-objective optimization is not universal. In practical applications, there are many discrete multi-objective optimization problems. The solution of different problems needs to design different evolutionary multi-objective algorithms according to their specific conditions. The threshold in the traditional multi-objective optimization of wireless network is a preset constant. It has relatively poor performance in the synthesis of images with different noise levels. An adaptive wireless network multi-objective optimization algorithm based on image synthesis is proposed. Based on the maximum inter class variance and maximum peak signal to noise ratio (SNR), an adaptive wireless network multi-objective optimization algorithm is established. The accuracy and noise immunity of image synthesis are also considered. In order to avoid the effect of threshold increase on algorithm efficiency, multi-objective optimization algorithm is introduced into the algorithm. Experiments show that the method proposed in this paper is accurate and robust and has good universality for the synthesis of different noise images.

Population-based approaches have given us new search strategies and ideas in order to solve optimization problems. Usually, these methods are based on the performance carried out by a finite number of agents, which by the interaction between them they evolve and work all over the search space. Also, it is well-known that the correct employment of parameter values in this kind of method can positively impact their performance and behavior. In this context, the present work focuses on the design of a hybrid architecture which smartly balances the population size on run-time. In order to smartly balance and control the population size, a modular approach, named Linear Modular Population Balancer (LMPB), is proposed. The main ideas behind the designed architecture include the solving strategy behind a population-based metaheuristic, the influence of learning components based on multiple statistical modeling methods which transform the dynamic data generated into knowledge, and the possibilities to tackle both discrete and continuous optimization problems. In this regard, three modules are proposed for LMPB, which concern tasks such as the management of the population-based algorithm, parameter setting, probabilities, learning methods, and selection mechanism for the population size to employ. In order to test the viability and effectiveness of our proposed approach, we solve a set of well-known benchmark functions and the multidimensional knapsack problem (MKP). Additionally, we illustrate promising solving results, compare them against state-of-the-art methods which have proved to be good options for solving optimization problems, and give solid arguments for future work in the necessity to keep evolving this type of proposed architecture.

Optimization problems with continuous variables are very often in real-life scenarios. This is why several studies have focused on the use of metaheuristics to solve these problems, known as continuous optimization problems. This chapter focuses on the robust population-based metaheuristic Variable Mesh Optimization (VMO). We review the fundamental aspects of VMO for continuous optimization. Moreover, we analyze the variants of VMO that have been proposed to solve multimodal problems, i.e. those with multiple optima at several search subspaces. The chapter uses a homogeneous algorithmic framework to describe VMO and its variants with niching strategies for multimodal problems.KeywordsMetaheuristicsVariable mesh optimizationContinuous optimization problemsMultimodal optimization problemsNiching

Causal discovery by continuous optimization with weighted superstructure.

In this paper we present two recent metaheuristics, particle swarm optimization and differential evolution algorithms, to solve the single machine total weighted tardiness problem, which is a typical discrete combinatorial optimization problem. Most of the literature on both algorithms is concerned with continuous optimization problems, while a few deal with discrete combinatorial optimization problems. A heuristic rule, the smallest position value (SPV) rule, borrowed from the random key representation in genetic algorithms, is developed to enable the continuous particle swarm optimization and differential evolution algorithms to be applied to all permutation types of discrete combinatorial optimization problems. The performance of these two recent population based algorithms is evaluated on widely used benchmarks from the OR library. The computational results show that both algorithms show promise in solving permutation problems. In addition, a simple but very efficient local search method based on the variable neighbourhood search (VNS) is embedded in both algorithms to improve the solution quality and the computational efficiency. Ultimately, all the best known or optimal solutions of instances are found by the VNS version of both algorithms.

Objective To improve the current quality of the upper cervical spine finite element models in China by developing and validating an anatomically detailed three-dimensional hexahedral finite element model of the upper cervical spine for use in clinically relevant biomechanical research.Methods The finite element model was developed with detailed anatomy from 16-slice computed tomographic images of the occiput to the C3 with 0.5 mm thickness from a healthy male subject.The image data were saved in Dicom format,and then imported into the Mimics 10.01 software for reconstructing three-dimensional geometric model of the upper cervical spine.By using the ICEM software,the C0-C3 three-dimensional hexahedral finite element model was reconstructed,with the thickness of the endplates set to 0.2 mm,the gap of facet joints set to 0.5 mm and defined as sliding contact with the friction coefficient being 0.1.Then,the Hypermesh V10.0 software was employed to adjust the mesh quality,load the ligament,and preliminarily develop a three-dimensional hexahedral finite element model of the upper cervical spine (C0-C3).Subsequently,the movements of bending forward,dorsal flexion,rotation,lateroversion were generated through the assignment of materials,the constraint of boundary and the stimulation of models.The resultant data were imported into the ABAQUS 6.11 software for the analysis of the three-dimensional motion of each vertebral level.Finally,the stress nephogram of the model's three-dimensional range of motion (ROM) and stress distribution were compared with in vitro experiments and other published data for efficacy validation.Results An anatomically detailed three-dimensional hexahedral finite element model of the upper cervical spine was developed,which consisted of 30 550 nodes and 41 909 elements.The ROM and stress distribution coincided with the data from published literatures.Conclusion The finite element model of the upper cervical spine developed in this study appears to show high authenticity for use in clinically relevant biomechanic studies. Key words: Cervical vertebrae ; Biomechanics ; Finite element analysis

To get individualized facial three-dimensional finite element (FE) model from transformation of a generic one to assist orthodontic analysis and prediction of treatment-related morphological change of facial soft tissue. A generic three-dimensional FE model of craniofacial soft and hard tissue was constructed based on a volunteer's spiral CT data. Seven pairs of main peri-oral muscles were constructed based on a combination of CT image and anatomical method. Individualized model could be obtained through transformation of the generic model based on selection of corresponding anatomical landmarks and radial basis functions (RBF) method. Validation was analyzed through superimposition of the transformed model and cone-beam CT (CBCT) reconstruction data. Pre- and post-treatment CBCT data of two patients were collected, which were superimposed to gain the amount of anterior teeth retraction and anterior alveolar surface remodeling that could be used as boundary condition. Different values of Poisson ratio ν and Young's modulus E were tested during simulation. Average deviation was 0.47 mm and 0.75 mm in the soft and hard tissue respectively. It could be decreased to a range of +0.29 mm and -0.21 mm after a second transformation at the lip-mouth region. The best correspondence between simulation and post-treatment result was found with elastic properties of soft tissues defined as follows. Poisson ratio ν for skin, muscle and fat being set as 0.45 while Young's modulus being set as 90.0 kPa, 6.2 kPa and 2.0 kPa respectively. Individualized three-dimensional facial FE model could be obtained through mathematical model transformation. With boundary condition defined according to treatment plan such FE model could be used to analyze the effect of orthodontic treatment on facial soft tissue.

Particle swarm optimisation (PSO) was developed by Kennedy and Eberhart in 1995 (Kennedy & Eberhart, 1995) inspired by the collective behaviour of natural birds or fish. PSO is a stochastic optimisation technique that uses a behaviour of population composed by many search points called particle. In spite of easy implementation in computer algorithms, it is well known as a powerful numerical optimizer. In the typical PSO algorithms, a set of particles searches the optimal solution in the problem space efficiently, by sharing the common attractor called global best. There are many modified versions of PSO by improving convergence property to a certain problem. While, a standard PSO is defined by Bratton and Kennedy (Bratton & Kennedy, 2007) to give a real standard for PSO studies. PSO seems as one of the evolutionary computations (ECs), and it has been shown that PSO is comparable to a genetic algorithm (Angeline, 1998). Thus, a lot of studies have demonstrated the effectiveness of PSO family in optimizing various continuous and discrete optimization problems. And a plenty of applications of PSO, such as the neural network training, PID controller tuning, electric system optimisation have been studied and achieved well results (Kennedy, 1997). However, PSO is often failed in searching the global optimal solution in the case of the objective function has a large number of dimensions. The reason of this phenomenon is not only existence of the local optimal solutions, the velocities of the particles sometimes lapsed into the degeneracy, so that the successive range is restricted in the sub-plain of the whole search hyper-plain. The sub-plane that is defined by finite number of particle velocities is a partial space in the whole search space. The issue of local optima in PSO has been studied and proposed several modifications on the basic particle driven equation (Parsopoulos et al., 2001; Hendtlass, 2005; Liang et al., 2006). There used a kind of adaptation technique or randomized method (e.g. mutation in evolutionary computations) to keep particles velocities or to accelerate them. Although such improvements work well and have ability to avoid fall in the local optima, the problem of early convergence by the degeneracy of some dimensions is still remaining, even if there are no local optima. Hence the PSO algorithm does not always work well for the high-dimensional function.

Objective To evaluate the effects of femoral offset (FO) on the stress level of bone cement total hip arthroplasty (THA) in the elder patients by the three-dimensional finite element analysis. Methods The normal bilateral hip joints in two cases was determined with CT imaging,with parameters including FO,neck shaft angle and neck length.The three-dimensional finite element model of THA was built so as to make Von Mises stress analysis of the changes of different neck lengths,neck shaft angles and FO. Results Stress levels in the prosthesis and bone cement reduced monotonically with the increase of neck shaft angles.In the meantime,the stress levels were lowered when neck length was in a range of 35-44 mm,but beyond the range they showed monotonous increase. Conclusions The reduction of stress levels of prosthesis and bone cement,promotion of femur stress and extension of range of motion of hip joints are closely related to FO.FO reconstruction benefits the restoration of abductor force arm and biomechanical function of normal hip joints. Key words: Femur; Arthroplasty,replacement,hip; Finite element analysis; Offset

Multiobjective Optimization Problems (MOP) can be found in many issues of scientific research, engineering, and in everyday social life. A MOP problem has several objectives that conflict with one another which must be optimized simultaneously. This paper presents a quantum-inspired evolutionary algorithm (QEA) to solve continuous multiobjective optimization problem (MOP). The proposed method employs Fast Nondominated Sorting and Crowding Distance from NSGA-II and implements all common operators of genetic algorithms (GA), such as crossover and mutations with additional Quantum Gate quantum operators. The proposed method is then run in a distributed manner and is proven to be able to significantly outperform the hypervolume and MOEA/D metrics and have hypervolumes that are comparable to NSGA-II while maintaining a better average Δ’ in all testing problems. From this result, it is concluded that using quantum-inspired individual genetic algorithms to solve continuous MOP can produce hypervolume and Δ’ metrics that are good in all specified test problems.

A hybrid global optimization method: The multi-dimensional case

GENET and its extended model EGENET are artificial neural networks to efficiently solve finite constraint satisfaction problems such as the car-sequencing problems. Both models use the min-conflict heuristic to update every finite-domain variable for finding local minima, and then apply heuristic learning rule(s) to escape the local minima not representing solution(s). Since continuous and finite domains are completely different, researchers seldom considered to apply the EGENET approach to solve continuous constrained optimization problems. We consider an interesting proposal to modify the original EGENET model with the minimal effort for solving continuous constrained optimization problems. Our proposal immediately opens up new directions for studying many possible ways to approximate continuous domains using modified finite-domain solvers. Moreover, the preliminary benchmarks of our prototypes on some graph layout problems as practical examples demonstrated some advantages of our proposal which prompts for further investigation.

Finite element analysis of the lumbar spine in adolescent idiopathic scoliosis subjected to different loads