Multi-level Genetic Algorithm Research Articles

Multilevel Genetic Algorithm–Based Acoustic–Elastodynamic Imaging of Coupled Fluid–Solid Media to Detect an Underground Cavity

Multilevel Genetic Algorithm–Based Acoustic–Elastodynamic Imaging of Coupled Fluid–Solid Media to Detect an Underground Cavity

Integrated Production and Transportation Scheduling in E-Commerce Supply Chain with Carbon Emission Constraints

Carbon emission constraints and trading policies in e-commerce environments have brought huge challenges to the operation of supply chain enterprises. In order to ensure the good operation of the e-commerce supply chain in a low-carbon environment, a supply chain scheduling optimization method based on integration of production and transportation with carbon emission constraints is proposed; we use it to analyze the impact of centralized decision-making mode and decentralized decision-making mode on supply chain scheduling and establish a scheduling optimization model that aims at optimal carbon emissions and costs. A multilevel genetic algorithm was designed according to the characteristics of the model, and numerical examples are used to verify the effectiveness of the model and algorithm. The results show that the centralized decision-making mode plays the role of the carbon emission constraints to the greatest extent; the carbon emissions and the cost are smallest in the centralized decision-making mode. The decentralized decision-making mode leads to the overall cost preference of the supply chain due to separate decisions made by enterprises, and the carbon emissions in the supply chain are greater. Transportation experts, business managers and government departments are interesting for integrated production and transportation scheduling in e-commerce supply chain with carbon emission constraints. Further research should address integrated production and transportation scheduling in dual-channel low supply chains.

A multilevel genetic algorithm for the clustering problem

Data mining is concerned with the discovery of interesting patterns and knowledge in data repositories. Cluster analysis which belongs to the core methods of data mining is the process of discovering homogeneous groups called clusters. Given a dataset and some measure of similarity between data objects, the goal in most clustering algorithms is maximising both the homogeneity within each cluster and the heterogeneity between different clusters. The multilevel paradigm suggests a hierarchical optimisation process going through different levels evolving from a coarse grain to fine grain strategy. The clustering problem is solved by first reducing the problem level by level to a coarser problem where an initial clustering is computed. The clustering of the coarser problem is mapped back level-by-level to obtain a better clustering of the original problem by refining the intermediate different clustering obtained at various levels. In this paper, a multilevel genetic algorithm and a multilevel K-means algorithm are introduced for solving the clustering problem. A benchmark using a number of datasets collected from a variety of domains is used to compare the effectiveness of the hierarchical approach against its single-level counterpart.

Multi-level genetic algorithm for the resource-constrained re-entrant scheduling problem in the flow shop

The re-entrant flow shop scheduling problem (RFSP) is regarded as a NP-hard problem and attracted the attention of both researchers and industry. Current approach attempts to minimize the makespan of RFSP without considering the interdependency between the resource constraints and the re-entrant probability. This paper proposed Multi-level genetic algorithm (GA) by including the co-related re-entrant possibility and production mode in multi-level chromosome encoding. Repair operator is incorporated in the Multi-level genetic algorithm so as to revise the infeasible solution by resolving the resource conflict. With the objective of minimizing the makespan, Multi-level genetic algorithm (GA) is proposed and ANOVA is used to fine tune the parameter setting of GA. The experiment shows that the proposed approach is more effective to find the near-optimal schedule than the simulated annealing algorithm for both small-size problem and large-size problem.

CHALLENGES OF THE PROJECT PLANNING METHODS IN THE 21ST CENTURY

There are a lot of project planning (like Gantt chart (Gant, 1910)) and network-based scheduling methods (like CPM, PDM, GERT (see i.e. Kelley-Walker, 1959, Pritsker 1966)), they were developed for handling traditional (e.g. construction) projects. While these methods are appropriate for the operation level - logic planning, scheduling, cost and resource allocation - of traditional project managemet, these methods can hardly be used for agile and extreme project management. Network-based methods focus on operation level, while for strategic decisions other methods should be used. Matrix-based methods can be used for planning agile methods (see Kosztyán-Kiss 2010-2013), however these methods also focused on operation level. This paper introduces an improved matrix-based method, the extended Multilevel Project Expert Matrix (xMPEM) method. This method can be used not only for operation, but strategic level of project management, where typical strategic questions arise e.g. which subprojects/tasks should be completed, how to treat priorities of completion in case of defining logic planning, how to support not only traditional but agile project management approaches. In this paper a multilevel genetic algorithm (MLGA) will be specified in order to determine possible project scenarios and possible project structures. The introduced xMPEM and MLGA methods can serve as the connection between operation and the strategic level of the project management. Key words: Project Expert System, matrix-based project planning methods, multilevel project planning.

Robust Multilevel Optimization of PMSM Using Design for Six Sigma

In this paper, a robust optimization method based on design for six sigma (DFSS) is combined to the optimization of a surface mounted permanent synchronous machine (PMSM) by using multilevel genetic algorithm (MLGA). First, MLGA and DFSS are introduced in the robust optimization. Second, by taking into account the tolerances of the motor products, important input parameters could be varied with six sigma distribution and Monte Carlo simulation (MCS) method is used to reduce the calculation cost. Third, to verify the new algorithm, the presented algorithm is applied to the optimization of a PMSM. The results compared with those of traditional GA and MLGA and the discussion of the robust optimization combined with MLGA are presented.

Dynamic Multilevel Optimization of Machine Design and Control Parameters Based on Correlation Analysis

In this paper, a multilevel optimization method is proposed for a motor drive system including a surface-mounted permanent magnet synchronous machine (SPMSM), the converter/inverter, and the control schemes. First, the multilevel optimization is described by using the problem matrix which may be used to allocate the design variables on different levels. The parameters in the problem matrix are deduced by using correlation analysis. Second, the architecture and implementation of multilevel genetic algorithm (MLGA) are carried out. As one of the advantages of MLGA, the dynamic adjustment strategy of GA operators is utilized to improve the optimal performance. The algorithm is then applied to a three-level optimization problem in which the optimization of SPMSM design and the control parameters of drive are considered in different levels. Finally, some results and discussions about the application of the proposed algorithm are presented.

A multi-level genetic algorithm for a multi-stage space allocation problem

A new case of space allocation problem is considered. The study is based on a real-world multi-stage hostel space allocation for university students. A multi-level application of genetic algorithm metaheuristic with promising results is presented. Based on the case study, we examined the sensitivity analysis of various genetic algorithm operators in order to establish the baseline for practical deployment. The feasibility rate of the solutions obtained were also determined and presented.

Multilevel Optimization for Surface Mounted PM Machine Incorporating With FEM

In this paper, multilevel genetic algorithm (MLGA) is presented to solve the optimization of surface mounted permanent magnet synchronous machine (SPMSM), which has features of mixed continuous and discrete design variables, multi-modal objective functions, etc. Firstly, the multilevel optimization problem is described by using the problem matrix. The values in the problem matrix are deduced by correlation analysis. Secondly, the architecture and implementation of MLGA are carried out. Thirdly, the new algorithm is applied to a bilevel optimization of SPMSM to verify this multilevel optimization. The results compared with those of traditional genetic algorithm (GA) and discussions of the multilevel optimization are presented.

A Multilevel Genetic Algorithm for the Traveling Salesman Problem

Abstract In this paper, we introduce a multilevel genetic algorithm for the traveling salesman problem. The resulting new algorithm starts by coarsening the original problem into a sequence of smaller problems using a coarsening scheme. Thereafter an initial feasible population is generated at the smallest problem and this population is projected back to the original problem by going through a refinement phase at each intermediate level using a simple genetic algorithm. Experimental results indicate that the combination of the genetic algorithm with the multilevel paradigm produces tours of better quality than those produced by the standard genetic algorithm.

A multilevel genetic algorithm for optimization of geometrically nonlinear stiffened composite structures

A multilevel genetic algorithm aiming the global optimization of beam reinforced composite structures with nonlinear geometric behaviour is proposed. A unified approach based on load-displacement control for buckling and first ply failure analysis is adopted. The Newton-Raphson iterative scheme and the arc-length method are used for tracing the equilibrium path and later for updating the critical values. The proposed genetic algorithm performs several sequences of two optimization levels resulting from the decomposition of the original optimization problem. Independent genetic searches are implemented for each level where different fitness functions and sub-populations are considered. The genetic operators selection and crossover supported by an elitist strategy are used while the diversity of the sub-populations is guaranteed based on implicit mutation. A genetic material exchange between levels is performed using clones and so the offspring of matured sub-populations is guaranteed. To improve the efficiency of the multilevel genetic optimization a niche of population is induced after the first stage at both levels.

A multilevel genetic algorithm for the optimum design of structural control systems

AbstractA multilevel genetic algorithm (MLGA) is proposed in this paper for solving the kind of optimization problems which are multilevel structures in nature and have features of mixed‐discrete design variables, multi‐modal and non‐continuous objective functions, etc. Firstly, the formulation of the mixed‐discrete multilevel optimization problems is presented. Secondly, the architecture and implementation of MLGA are described. Thirdly, the algorithm is applied to two multilevel optimization problems. The first one is a three‐level optimization problem in which the optimization of the number of actuators, the positions of actuators and the control parameters are considered in different levels. An actively controlled tall building subjected to strong wind action is considered to investigate the effectiveness of the proposed algorithm. The second application considers a combinatorial optimization problem in which the number and configuration of actuators are optimized simultaneously, an actively controlled building under earthquake excitations is adopted for this case study. Finally, some results and discussions about the application of the proposed algorithm are presented. Copyright © 2002 John Wiley & Sons, Ltd.

Multi‐level design model and genetic algorithm for structural control system optimization

AbstractThe integrated optimum problem of structures subjected to strong earthquakes and wind excitations, optimizing the number of actuators, the configuration of actuators and the control algorithms simultaneously, is studied. Two control algorithms, optimal control and acceleration feedback control, are used as the control algorithms. A multi‐level optimization model is proposed with respect to the solution procedure of the optimum problem. The characteristics of the model are analysed, and the formulation of each suboptimization problem at each level is presented. To solve the multi‐level optimization problem, a multi‐level genetic algorithm (MLGA) is proposed. The proposed model and MLGA are used to solve two multi‐level optimization problems in which the optimization of the number of actuators, the positions of actuators and the control algorithm are considered in different levels. In problem 1, an example structure is excited by strong wind, and in problem 2, an example structure is subjected to strong earthquake excitation. Copyright © 2001 John Wiley & Sons, Ltd.

Multi-level optimal design of buildings with active control under winds using genetic algorithms

The goal of this paper is to study the complicated optimal design problem of integrating the number of actuators, the configuration of the actuators and the active control algorithms in buildings excited by strong wind force. To do this, the following sequential studies are carried out: (1) Two control algorithms, linear quadratic regulator (LQR) and acceleration feedback control algorithm, are analyzed and used as the control algorithms. (2) The characteristics of the optimal design problem are analyzed in detail by a simulation study. (3) A multi-level optimization model is proposed, and the formulation of sub-optimization problems in each level is presented. (4) To solve the multi-level optimization problem, a multi-level genetic algorithm (MLGA) is proposed. The properties and implementation of MLGA are given and analyzed in detail. Finally, the optimal design model and the corresponding solving algorithm are tested by numerical simulation. The results show that: (1) in the design of actively controlled structures subjected to strong wind excitation, the problem of considering the number of actuators, the position of actuators and the control algorithm simultaneously is of a multi-level design optimization with the properties of non-linearity, discreteness and so on. (2) This kind of optimization problem should be described naturally by the multi-level design model. (3) The multi-level genetic algorithm can solve this complicated problem effectively. (4) The optimal locations of actuators depend on the control approaches.

Design of a wide envelope controller for a STOVL gas turbine engine

ABSTRACT This paper describes a novel approach to the design of a control system for a short take-off, vertical landing (STOVL) aircraft gas turbine engine. A multi-level multiobjective genetic algorithm (MOGA) is employed to design controllers at both individual operating points, using system linearisations and small signal response characteristics, and over the full-flight envelope, using a nonlinear model and large signalresponses. The proposed approach should allow the selection of smoother controller parameters over the flight envelope and ensure that more consistent control demands are made at off-design operating points.