Multilevel Optimization Techniques Research Articles

Multilevel Thresholding-based Medical Image Segmentation using Hybrid Particle Cuckoo Swarm Optimization

Background: The most important aspect of medical image processing and analysis is image segmentation. Fundamentally, the outcomes of segmentation have an impact on all subsequent image testing methods, including object representation and characterization, measuring of features, and even higher-level procedures. The problem with image segmentation is recognition and perceptual completion while segmenting the image. However, these issues can be resolved by multilevel optimization techniques. However, multilevel thresholding will become more computationally intensive with increasing thresholds. Optimization algorithms can resolve these issues. Therefore, hybrid optimization is used for image segmentation in this research work. Methods: The researchers propose a Multilevel Thresholding-based Segmentation using a Hybrid Optimization approach with an adaptive bilateral filter to resolve the optimization challenges in medical image segmentation. The proposed model utilizes Kapur's entropy as the objective function in the nature-inspired optimization algorithm. Results: The result is evaluated using parameters such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The researchers perform result analysis with variable thresholding levels on KAU-BCMD and mini-MIAS datasets. The highest PSNR, SSIM, and FSIM achieved were 31.9672, 0.9501, and 0.9728 respectively. The results of the hybrid model are compared with state-of-the-art models, demonstrating its efficiency. Conclusion: The research concludes that the proposed Multilevel thresholding-based segmentation using a Hybrid Optimization approach effectively solves optimization challenges in medical image segmentation. The results indicate its efficiency compared to existing models. The research work highlights the potential of the proposed hybrid model for improving image processing and analysis in the medical field.

A multilevel optimization approach for daily scheduling of combined heat and power units with integrated electrical and thermal storage

Renowned for their remarkable overall efficiencies ranging from 70% to 90%, combined heat and power systems stand as a pivotal strategy for optimizing energy consumption by capitalizing on the synergistic relationship between electricity and thermal energy production. However, achieving optimal performance in combined heat and power systems remains a formidable challenge due to the intricate interplay of numerous variables. This paper presents a novel approach to daily scheduling optimization for combined heat and power units, focusing on the integration of electrical and thermal storage systems and meticulous consideration of security constraints. The optimization of combined heat and power unit scheduling introduces a mixed-integer nonlinear programming challenge, replete with deterministic and random variables. Addressing this complexity requires resilient solutions. In this study, we employ a multilevel optimization technique, transforming the problem into a bilevel structure. The initial step involves mapping operating parameters and minimizing costs through a water cycle optimization algorithm, laying a robust foundation for combined heat and power unit operation. Subsequently, we immerse ourselves in the realm of stochastic contingency scenarios, acknowledging the myriad uncertainties inherent in real-world systems. To empirically validate the efficacy of our proposed algorithm, extensive simulations are conducted on IEEE 18-bus and 24-bus test systems that emulate practical power networks. The results unequivocally showcase the power of our approach in navigating the complexities of optimal CHP unit planning. This paper's contributions lie in its innovative multilevel optimization technique, adeptly addressing both deterministic and stochastic aspects, ultimately paving the way for increased energy efficiency and enhanced system reliability in practical applications.

Energy-Efficient Flexible Flow Shop Scheduling With Due Date and Total Flow Time

One of the most significant optimization issues facing a manufacturing company is the flexible flow shop scheduling problem (FFSS). However, FFSS with uncertainty and energy-related elements has received little investigation. Additionally, in order to reduce overall waiting times and earliness/tardiness issues, the topic of flexible flow shop scheduling with shared due dates is researched. Using transmission line loadings and bus voltage magnitude variations, an unique severity function is formulated in this research. Optimize total energy consumption, total agreement index, and make span all at once. Many different meta-heuristics have been presented in the past to find near-optimal answers in an acceptable amount of computation time. To explore the potential for energy saving in shop floor management, a multi-level optimization technique for flexible flow shop scheduling and integrates power models for individual machines with cutting parameters optimisation into energy-efficient scheduling issues is proposed. However, it can be difficult and time-consuming to fine-tune algorithm-specific parameters for solving FFSP.

Artificial Intelligence Model for Software Reusability Prediction System

The most significant invention made in recent years to serve various applications is software. Developing a faultless software system requires the software system design to be resilient. To make the software design more efficient, it is essential to assess the reusability of the components used. This paper proposes a software reusability prediction model named Flexible Random Fit (FRF) based on aging resilience for a Service Net (SN) software system. The reusability prediction model is developed based on a multilevel optimization technique based on software characteristics such as cohesion, coupling, and complexity. Metrics are obtained from the SN software system, which is then subjected to min-max normalization to avoid any saturation during the learning process. The feature extraction process is made more feasible by enriching the data quality via outlier detection. The reusability of the classes is estimated based on a tool called Soft Audit. Software reusability can be predicted more effectively based on the proposed FRF-ANN (Flexible Random Fit - Artificial Neural Network) algorithm. Performance evaluation shows that the proposed algorithm outperforms all the other techniques, thus ensuring the optimization of software reusability based on aging resilient. The model is then tested using constraint-based testing techniques to make sure that it is perfect at optimizing and making predictions.

Linkage factors optimization of multi-outputs of compliant mechanism using response surface

This paper presents a linkage factors synthesis and multi-level optimization technique for bi-stable compliant mechanism. The linkage synthesis problem is modeled as multiple level factors and responses optimization problem with constraints. The bi-stable compliant mechanism is modeled as a crank slider mechanism using pseudo-rigid-body model (PRBM). The model exerts the large deflection of flexible element which explains compliant mechanism’s bi-stable performance. The design concept is applied on variable input parameters subsets. Though the effect of compliant mechanism process factors on Fmax and PRBM deflection angle (Theta-cap Θ1) are contradictory when studied individually as no response gives best process quality. The relationship model between input factors and responses characteristics were generated by ANOVA and optimized by response surface methodology (RSM). ANOVA shown more significant factors are the initial angle of link1 (θ1) and material thickness (t). The Box-Behnken design of RSM is applied with a desirability function approach to determine the optimum set of parameters for minimizing Fmax and maximizing the Theta-cap (Θ1). Thus, this technique shown flexibility based on the product application could be tested and established.

A multi-level optimization technique based on fuel consumption and energy index in early-stage ship design

Reducing lifetime fuel consumption (LFC) and energy efficiency design index (EEDI) are two of the main concerns of shipping industry in recent years. This paper presents a multi-disciplinary and multi-level optimization scheme-based software (HPS-MOP2) to design a hull–propeller system simultaneously from the LFC and EEDI point of view in early-stage ship design. Calculations of the ship resistance and propeller performance are essential to optimize the ship hull–propeller system. Two numerical methods with variable fidelity, non-uniform rational basis spline (NURBS) geometry modelling technique and new version of multi-objective evolutionary algorithm based on decomposition (MOEA/D) are three main parts of the proposed methodology. A bulk carrier propelled by a well-known propeller is used as a base model in three different study cases based on specific fuel oil consumption (SFOC) curves provided by the engine manufacturers Wartsila, MAN and Caterpillar. The presented results illustrate that the employed approach may achieve cost- and energy-efficient designs.

Simultaneous topology optimization of supporting structure and loci of isolators in an active vibration isolation system

We developed a new multi-objective and multi-level optimization method to design an active vibration isolation system. Both the layout of the continuum (i.e. the supporting structure) and the loci of the isolators are designed using topology optimization technique in a unified formulation for the first time. The static, dynamic and vibration-isolation characteristics are taken into account simultaneously in the present model. Due to their different roles in the system it may be appropriate and advantageous to treat the design of the continuum layout and isolator loci as different sub-problems with different objectives in separate stages. The multi-level optimization technique, where the optimization of the supporting structure and the isolator loci are incorporated into a closed-loop, is proposed and implemented so that the interactions between these two sub-problems can be fully taken into account. Numerical results demonstrate the validity of the proposed design cycle. Comparisons show that the overall static, dynamic and vibration-isolation performance of the optimized system outperforms the ones designed by traditional methods.

Improving Direct Search algorithms by multilevel optimization techniques

Direct Search algorithms are classical derivative-free methods for optimization. Though endowed with solid theoretical properties, they are not well suited for large-scale problems due to slow convergence and scaling issues. In this paper, we discuss how, on problems for which a hierarchy of objective functions is available, such limitations can be circumvented by using multilevel schemes which are able to accelerate the computation of a finest level solution. Starting from a previously introduced derivative-free multilevel method, based on Coordinate Search optimization with a sampling strategy of Gauss–Seidel type, we consider also the use of sampling strategies of Jacobi type, and present several algorithmic variations. We justify our choices by performing experiments on two model problems, showing that a performance close to multigrid optimality can be observed in practice.

A Multiscale Design Methodology for Hierarchical Systems With Random Field Uncertainty

A multiscale design methodology is proposed for hierarchical material and product systems with random field uncertainty that propagates across multiple length scales. Using the generalized hierarchical multiscale decomposition pattern in multiscale modeling, a set of computational techniques is developed to manage the system complexity. Design of experiments and metamodeling strategies are proposed to manage the complexity of propagating random field uncertainty through three generalized levels of transformation: the material microstructure random field, the material property random field, and the probabilistic product performance. Multilevel optimization techniques are employed to find optimal design solutions at individual scales. A hierarchical multiscale design problem that involves a two-scale (submicro- and microscales) material design and a macroscale product (bracket) design is used to demonstrate the applicability and benefits of the proposed methodology.

Data-Driven Decision Tree Classification for Product Portfolio Design Optimization

The formulation of a product portfolio requires extensive knowledge about the product market space and also the technical limitations of a company’s engineering design and manufacturing processes. A design methodology is presented that significantly enhances the product portfolio design process by eliminating the need for an exhaustive search of all possible product concepts. This is achieved through a decision tree data mining technique that generates a set of product concepts that are subsequently validated in the engineering design using multilevel optimization techniques. The final optimal product portfolio evaluates products based on the following three criteria: (1) it must satisfy customer price and performance expectations (based on the predictive model) defined here as the feasibility criterion; (2) the feasible set of products/variants validated at the engineering level must generate positive profit that we define as the optimality criterion; (3) the optimal set of products/variants should be a manageable size as defined by the enterprise decision makers and should therefore not exceed the product portfolio limit. The strength of our work is to reveal the tremendous savings in time and resources that exist when decision tree data mining techniques are incorporated into the product portfolio design and selection process. Using data mining tree generation techniques, a customer data set of 40,000 responses with 576 unique attribute combinations (entire set of possible product concepts) is narrowed down to 46 product concepts and then validated through the multilevel engineering design response of feasible products. A cell phone example is presented and an optimal product portfolio solution is achieved that maximizes company profit, without violating customer product performance expectations.

Optimal design of a traction system using Target Cascading

In this paper, traction system design in carried out with a multilevel optimization technique called Target Cascading. This methodology is based on a hierarchical breaking down and allows solving a problem with multiple parallel optimizations. The large-scale system design problem is usually decomposed into several linked optimization subproblems according to project organization structure. Target Cascading gives a new approach to formulate and to solve the complex system design optimization problem in the electrical field. The problem formulation is more suitable to this kind of problem than conventional methods because it is closer to a work organisation of design engineers.

Design optimization of switchable multi-port spiral inductors

In this paper, we develop a design optimization methodology for switchable multi-port spiral inductors in fully integrated wireless systems. The methodology simultaneously maximizes the inductor's performance for multiple inductance values and operating frequencies. We utilize multi-level optimization techniques to efficiently design the geometry of the switchable inductor structure. The methodology can produce designs with significantly larger quality factors than those obtained by optimizing the inductor design for a single inductance value and operating frequency.

Scheduling of manufacturing systems under dual-resource constraints using genetic algorithms

Automated assembly lines are subject to unexpected failures, which can cause costly shutdowns. Generally, the recovery process is done “on-line” by human experts or automated error recovery logic controllers embedded in the system. However, these controller codes are programmed based on anticipated error scenarios and, due to the geometrical features of the assembly lines, there may be error cases that belong to the same anticipated type but are present in different positions, each requiring a different way to recover. Therefore, robustness must be assured in the sense of having a common recovery algorithm for similar cases during the recovery sequence.The proposed approach is based on three-dimensional geometric modeling of the assembly line coupled with the genetic programming and multi-level optimization techniques to generate robust error recovery logic in an “off-line” manner. The approach uses genetic programming's flexibility to generate recovery plans in the robot language itself. An assembly line is modeled and from the given error cases an optimum way of error recovery is investigated using multi-level optimization in a “generate and test” fashion. The obtained results showed that with the improved convergence gained by using multi-level optimization, the infrastructure is capable of finding robust error recovery algorithms. It is expected that this approach will require less time for the generation of robust error recovery logic.

Optimal Design of Cable Structures with Genetic Algorithms

Genetic Algorithm is employed for cable structures subjected to large displacements, stress and diameter constraints. Design solution is governed by nonlinear interaction of cable sag, amount of pretension and stiffness of cable with deformations under transverse loads. Geometrical nonlinear finite element method with total Lagrangian approach is integrated in the present work with genetic algorithm to evaluate optimum values of design parameters, i.e. diameter of cable and amount of prestress. Study is performed on steel and carbon fibre cable stays, cable nets and cable roofs. Different types of crossover operators are applied along with single- and multi-level optimization techniques in order to obtain true optimum in the search space of global optimum.

Automated generation of robust error recovery logic in assembly systems using genetic programming

Abstract Automated assembly lines are subject to unexpected failures, which can cause costly shutdowns. Generally, the recovery process is done “on-line” by human experts or automated error recovery logic controllers embedded in the system. However, these controller codes are programmed based on anticipated error scenarios and, due to the geometrical features of the assembly lines, there may be error cases that belong to the same anticipated type but are present in different positions, each requiring a different way to recover. Therefore, robustness must be assured in the sense of having a common recovery algorithm for similar cases during the recovery sequence. The proposed approach is based on three-dimensional geometric modeling of the assembly line coupled with the genetic programming and multi-level optimization techniques to generate robust error recovery logic in an “off-line” manner. The approach uses genetic programming's flexibility to generate recovery plans in the robot language itself. An assembly line is modeled and from the given error cases an optimum way of error recovery is investigated using multi-level optimization in a “generate and test” fashion. The obtained results showed that with the improved convergence gained by using multi-level optimization, the infrastructure is capable of finding robust error recovery algorithms. It is expected that this approach will require less time for the generation of robust error recovery logic.

Multiobjective and multilevel optimization for steel frames

A new application of multiobjective and multilevel optimization techniques for steel frames is presented. Two levels: system and element, and two objectives: maximization of the total structural strain energy and minimization of the total structural weight are considered. The constraints of frequency, strength, buckling, displacement and variable-bounds are treated. Using the building codes, AISC–LRFD, AISC Seismic Provisions and Chinese GBJ17–88, formal mathematical formulations are made. A demonstration example shear steel frame under gravity and earthquake loads is given.

Multilevel optimization of pipelined caches

This paper formulates and shows how to solve the problem of selecting the cache size and depth of cache pipelining that maximizes the performance of a given instruction-set architecture. The solution combines trace-driven architectural simulations and the timing analysis of the physical implementation of the cache. Increasing cache size tends to improve performance but this improvement is limited because cache access time increases with its size. This trade-off results in an optimization problem we referred to as multilevel optimization, because it requires the simultaneous consideration of two levels of machine abstraction: the architectural level and the physical implementation level. The introduction of pipelining permits the use of larger caches without increasing their apparent access time, however, the bubbles caused by load and branch delays limit this technique. In this paper we also show how multilevel optimization can be applied to pipelined systems if software- and hardware-based strategies are considered for hiding the branch and load delays. The multilevel optimization technique is illustrated with the design of a pipelined cache for a high clock rate MIPS-based architecture. The results of this design exercise show that, because processors with pipelined caches can have shorter CPU cycle times and larger caches, a significant performance advantage is gained by using two or three pipeline stages to fetch data from the cache. Of course, the results are only optimal for the implementation technologies chosen for the design exercise; other choices could result in quite different optimal designs. The exercise is primarily to illustrate the steps in the design of pipelined caches using multilevel optimization; however, it does exemplify the importance of pipelined caches if high clock rate processors are to achieve high performance.

Prediction of high temperature metal matrix structural material failure using a massively parallel computer

Large scale high speed design and analysis of next generation aerospace systems is achieved by focusing on the computational integration and synchronization of probabilistic mathematics, structural/material mechanics, and parallel computing. Design costs have been driven upward by mathematical models that require multiple levels of interactive analysis and utilize time consuming convergence criteria that further drive computing and design costs upward. To reduce CPU time and memory limitations, an effective real time parallelization (RTP) of the solution is introduced. Recursive Internal Partitioning (RIP) is used to partition the entire domain into subdomains with one or more subdomains being assigned to each independent processor. The Alpha STAR multifrontal algorithm (AMF) is implemented to assemble, condense, and solve for the unknowns at all finite element nodes. A multi level optimization technique is utilized to speed up the simulation processing time of the diversified field of specialized analysis techniques and mathematical models. These models require hierarchical multiple levels of interactive analysis utilizing time consuming convergence. The generic high speed civil transport (hsct) model is used to demonstrate the large scale computing capability. Results of multicriteria optimization indicate an order of magnitude reduction in computing time. Numerical solutions as well as physical phenomena are discussed and recommendations are provided for future solutions.

Hierarchical optimization for power system control

This paper presents a practical feedback control strategy for the transient stability problem. The control configuration is hierarchical and is proposed to be installed in addition to existing machine excitation and turbine governor controls. The design proceeds in two steps. First, the local controllers associated with the subsystems are independently designed by fixing the interconnections. Then a higher hierarchical level is designed from the interconnection variables and nonlinear terms. The resulting parameter optimization problem is solved by a decentralized computational structure. The scheme is based on multilevel optimization techniques with multiple objectives given by relevant system disturbances, aggregated by means of a weighted sum. The usefulness of the proposed technique is demonstrated by its application to a power system optimization problem with practical soft constraints on the control variables.

Multilevel optimization of high speed VLSI interconnect networks by decomposition

A multilevel optimization technique is developed for large-scale and hierarchical optimization of high-speed VLSI interconnects modeled by distributed transmission lines. Mathematical programming decomposition is combined with network tearing where the overall network is optimized by a set of parallel suboptimizations. The technique takes advantage of VLSI interconnects in the hierarchy of IC, multichip modules (MCM) and printed circuit board (PCB), and is faster than standard optimization. The convergence property of the technique is derived through Gauss-Seidel relaxation analysis and optimality conditions for the multiple suboptimizations.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>