Nesting Arbitrary Shapes Using Geometric Mating

The quantitative evaluation of producer and injector well interference based on well operation data (profiles of flow rates/injectivities and bottomhole/reservoir pressures) with the help of CRM (Capacitance-Resistive Models) is an optimization problem with large set of variables and constraints. The analytical solution cannot be found because of the complex form of the objective function for this problem. Attempts to find the solution with stochastic algorithms take unacceptable time and the result may be far from the optimal solution. Besides, the use of universal (commercial) optimizers hides the details of step by step solution from the user, for example — the ambiguity of the solution as the result of data inaccuracy.<br> The present article concerns two variants of CRM problem. The authors present a new algorithm of solving the problems with the help of “General Quadratic Programming Algorithm”. The main advantage of the new algorithm is the greater performance in comparison with the other known algorithms. Its other advantage is the possibility of an ambiguity analysis. This article studies the conditions which guarantee that the first variant of problem has a unique solution, which can be found with the presented algorithm. Another algorithm for finding the approximate solution for the second variant of the problem is also considered. The method of visualization of approximate solutions set is presented. The results of experiments comparing the new algorithm with some previously known are given.

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Design Optimization Method for MR-Brake Actuators

Volume 7: Dynamic Systems and Control; Mechatronics and Intelligent Machines, Parts A and B ◽

10.1115/imece2011-65014 ◽

2011 ◽

Cited By ~ 2

Author(s):

Ozan G. Erol ◽

Hakan Gurocak ◽

Berk Gonenc

Keyword(s):

Optimal Solution ◽

Volume Ratio ◽

Optimization Methods ◽

Search Space ◽

Optimization Method ◽

Friction Torque ◽

Element Analysis ◽

Taguchi Design Of Experiments ◽

Variable Friction ◽

Electronically Controllable

MR-brakes work by varying viscosity of a magnetorheological (MR) fluid inside the brake. This electronically controllable viscosity leads to variable friction torque generated by the actuator. A properly designed MR-brake can have a high torque-to-volume ratio which is quite desirable for an actuator. However, designing an MR-brake is a complex process as there are many parameters involved in the design which can affect the size and torque output significantly. The contribution of this study is a new design approach that combines the Taguchi design of experiments method with parameterized finite element analysis for optimization. Unlike the typical multivariate optimization methods, this approach can identify the dominant parameters of the design and allows the designer to only explore their interactions during the optimization process. This unique feature reduces the size of the search space and the time it takes to find an optimal solution. It normally takes about a week to design an MR-brake manually. Our interactive method allows the designer to finish the design in about two minutes. In this paper, we first present the details of the MR-brake design problem. This is followed by the details of our new approach. Next, we show how to design an MR-brake using this method. Prototype of a new brake was fabricated. Results of experiments with the prototype brake are very encouraging and are in close agreement with the theoretical performance predictions.

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A two-stage evolutionary optimization approach for an irrigation system design

Journal of Hydroinformatics ◽

10.2166/hydro.2016.032 ◽

2016 ◽

Vol 19 (1) ◽

pp. 115-122 ◽

Cited By ~ 6

Author(s):

Milan Cisty ◽

Zbynek Bajtek ◽

Lubomir Celar

Keyword(s):

Water Distribution ◽

Irrigation System ◽

Optimal Solution ◽

Water Distribution Network ◽

Search Space ◽

Optimization Method ◽

Global Optimum ◽

Optimization Approach ◽

Nsga Ii ◽

Irrigation Network

In this work, an optimal design of a water distribution network is proposed for large irrigation networks. The proposed approach is built upon an existing optimization method (NSGA-II), but the authors are proposing its effective application in a new two-step optimization process. The aim of the paper is to demonstrate that not only is the choice of method important for obtaining good optimization results, but also how that method is applied. The proposed methodology utilizes as its most important feature the ensemble approach, in which more optimization runs cooperate and are used together. The authors assume that the main problem in finding the optimal solution for a water distribution optimization problem is the very large size of the search space in which the optimal solution should be found. In the proposed method, a reduction of the search space is suggested, so the final solution is thus easier to find and offers greater guarantees of accuracy (closeness to the global optimum). The method has been successfully tested on a large benchmark irrigation network.

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Performance Evaluation of Population Seeding Techniques of Permutation-Coded GA Traveling Salesman Problems Based Assessment

Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms ◽

10.4018/978-1-7998-8048-6.ch053 ◽

2021 ◽

pp. 1074-1115

Author(s):

Victer Paul ◽

Ganeshkumar C ◽

Jayakumar L

Keyword(s):

Genetic Algorithms ◽

Nearest Neighbor ◽

Optimization Technique ◽

Optimal Solution ◽

Computation Time ◽

Search Space ◽

Population Based ◽

Vital Role ◽

Traveling Salesman ◽

Performance Criteria

Genetic algorithms (GAs) are a population-based meta-heuristic global optimization technique for dealing with complex problems with a very large search space. The population initialization is a crucial task in GAs because it plays a vital role in the convergence speed, problem search space exploration, and also the quality of the final optimal solution. Though the importance of deciding problem-specific population initialization in GA is widely recognized, it is hardly addressed in the literature. In this article, different population seeding techniques for permutation-coded genetic algorithms such as random, nearest neighbor (NN), gene bank (GB), sorted population (SP), and selective initialization (SI), along with three newly proposed ordered-distance-vector-based initialization techniques have been extensively studied. The ability of each population seeding technique has been examined in terms of a set of performance criteria, such as computation time, convergence rate, error rate, average convergence, convergence diversity, nearest-neighbor ratio, average distinct solutions and distribution of individuals. One of the famous combinatorial hard problems of the traveling salesman problem (TSP) is being chosen as the testbed and the experiments are performed on large-sized benchmark TSP instances obtained from standard TSPLIB. The scope of the experiments in this article is limited to the initialization phase of the GA and this restricted scope helps to assess the performance of the population seeding techniques in their intended phase alone. The experimentation analyses are carried out using statistical tools to claim the unique performance characteristic of each population seeding techniques and best performing techniques are identified based on the assessment criteria defined and the nature of the application.

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Synthesis of Controllers for MIMO Systems with Time Response Specifications

International Journal of System Dynamics Applications ◽

10.4018/ijsda.2014070102 ◽

2014 ◽

Vol 3 (3) ◽

pp. 25-52 ◽

Cited By ~ 5

Author(s):

Maher Ben Hariz ◽

Wassila Chagra ◽

Faouzi Bouani

Keyword(s):

Global Optimization ◽

Optimization Problem ◽

Closed Loop ◽

Controller Design ◽

Mimo Systems ◽

Optimal Solution ◽

Optimization Method ◽

Time Response ◽

Step Response ◽

Global Optimization Method

This paper proposes the design of fixed low order controllers for Multi Input Multi Output (MIMO) decoupled systems. The simplified decoupling is used as a decoupling system technique due to its advantages compared to other decoupling methods. The main objective of the proposed controllers is to satisfy some desired closed loop step response performances such as the settling time and the overshoot. The controller design is formulated as an optimization problem which is non convex and it takes in account the desired closed loop performances. Therefore, classical methods used to solve the non convex optimization problem can generate a local solution and the resulting control law is not optimal. Thus, the thought is to use a global optimization method in order to obtain an optimal solution which will guarantee the desired time response specifications. In this work the Generalized Geometric Programming (GGP) is exploited as a global optimization method. The key idea of this method consists in transforming an optimization problem, initially, non convex to a convex one by some mathematical transformations. Simulation results and a comparison study between the presented approach and a Proportional Integral (PI) controller are given in order to shed light the efficiency of the proposed controllers.

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Performance Evaluation of Population Seeding Techniques of Permutation-Coded GA Traveling Salesman Problems Based Assessment

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2019040103 ◽

2019 ◽

Vol 10 (2) ◽

pp. 55-92 ◽

Cited By ~ 1

Author(s):

Victer Paul ◽

Ganeshkumar C ◽

Jayakumar L

Keyword(s):

Genetic Algorithms ◽

Nearest Neighbor ◽

Optimization Technique ◽

Optimal Solution ◽

Computation Time ◽

Search Space ◽

Population Based ◽

Vital Role ◽

Traveling Salesman ◽

Performance Criteria

Genetic algorithms (GAs) are a population-based meta-heuristic global optimization technique for dealing with complex problems with a very large search space. The population initialization is a crucial task in GAs because it plays a vital role in the convergence speed, problem search space exploration, and also the quality of the final optimal solution. Though the importance of deciding problem-specific population initialization in GA is widely recognized, it is hardly addressed in the literature. In this article, different population seeding techniques for permutation-coded genetic algorithms such as random, nearest neighbor (NN), gene bank (GB), sorted population (SP), and selective initialization (SI), along with three newly proposed ordered-distance-vector-based initialization techniques have been extensively studied. The ability of each population seeding technique has been examined in terms of a set of performance criteria, such as computation time, convergence rate, error rate, average convergence, convergence diversity, nearest-neighbor ratio, average distinct solutions and distribution of individuals. One of the famous combinatorial hard problems of the traveling salesman problem (TSP) is being chosen as the testbed and the experiments are performed on large-sized benchmark TSP instances obtained from standard TSPLIB. The scope of the experiments in this article is limited to the initialization phase of the GA and this restricted scope helps to assess the performance of the population seeding techniques in their intended phase alone. The experimentation analyses are carried out using statistical tools to claim the unique performance characteristic of each population seeding techniques and best performing techniques are identified based on the assessment criteria defined and the nature of the application.

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Gradient-based optimization method for interference suppression of linear arrays by the amplitude-only and phase-only control

International Journal of Microwave and Wireless Technologies ◽

10.1017/s1759078721001355 ◽

2021 ◽

pp. 1-7

Author(s):

Renjing Gao ◽

Yi Tang ◽

Qi Wang ◽

Shutian Liu

Keyword(s):

Optimization Algorithm ◽

Optimization Problem ◽

Optimization Problems ◽

Search Algorithm ◽

Interference Suppression ◽

Computation Time ◽

Optimization Method ◽

Main Beam ◽

Linear Arrays ◽

Gradient Based

Abstract This paper presents a gradient-based optimization method for interference suppression of linear arrays by controlling the electrical parameters of each array element, including the amplitude-only and phase-only. Gradient-based optimization algorithm (GOA), as an efficient optimization algorithm, is applied to the optimization problem of the anti-interference arrays that is generally solved by the evolutionary algorithms. The goal of this method is to maximize the main beam gain while minimizing the peak sidelobe level (PSLL) together with the null constraint. To control the nulls precisely and synthesize the radiation pattern accurately, the full-wave method of moments is used to consider the mutual coupling among the array elements rigorously. The searching efficiency is improved greatly because the gradient (sensitivity) information is used in the algorithm for solving the optimization problem. The sensitivities of the design objective and the constraint function with respect to the design variables are analytically derived and the optimization problems are solved by using GOA. The results of the GOA can produce the desired null at the specific positions, minimize the PSLL, and greatly shorten the computation time compared with the often-used non-gradient method such as genetic algorithm and cuckoo search algorithm.

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An Optimal Soft Landing Control for Moon Lander by Means of Measures

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.52-54.1861 ◽

2011 ◽

Vol 52-54 ◽

pp. 1861-1867

Author(s):

J.A. Fakharzadeh ◽

Sajad Salehi

Keyword(s):

Dynamical System ◽

Control Strategy ◽

Measure Space ◽

Optimization Problem ◽

Optimal Solution ◽

The Other ◽

Solution Path ◽

Soft Landing ◽

Variational Form ◽

Landing Control

Designing a new control strategy for a moon lander to achieve an optimal soft landing, is the main purpose of this paper. For the dynamical system of the propeller to achieve the lowest level of fuel consumption, the problem of soft landing is presented as an optimal control one. Representing this into a variational form, transferring to an optimization problem on a measure space and then determining the optimal solution via a linear programming problem is the new solution path. This method has some important advantages in compare with the other, which are explain within a numerical simulation.

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Optimal Design of a Mechanism Used for Opening and Shutting a Ship’s Hatch Cover

Journal of Mechanisms Transmissions and Automation in Design ◽

10.1115/1.3258602 ◽

1984 ◽

Vol 106 (4) ◽

pp. 503-509

Author(s):

Koichi Ito ◽

Tadashi Kuroiwa ◽

Shinsuke Akagi

Keyword(s):

Optimal Design ◽

Nonlinear Optimization ◽

Optimization Problem ◽

Numerical Study ◽

Optimal Solution ◽

Optimization Method ◽

Gradient Algorithm ◽

Linkage Mechanism ◽

Reduced Gradient ◽

Generalized Reduced Gradient

A nonlinear optimization method is proposed to design a linkage mechanism used for opening and shutting a ship’s hatch cover. Considering the maximum force of the oil cylinder necessary to move the hatch cover as the objective function to be minimized, the design problem to determine the optimal configuration of linkage mechanism is formulated as a nonlinear optimization problem of minimax type. It it shown that the optimal solution can be derived by adopting the generalized reduced gradient algorithm together with a linkage statical simulation model, and the effectiveness of the algorithm is ascertained through a numerical study.

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Hybrid Approach for Containment Problems

Volume 2: 28th Design Automation Conference ◽

10.1115/detc2002/dac-34124 ◽

2002 ◽

Author(s):

Chan Yu ◽

Souran Manoochehri

Keyword(s):

Hybrid Method ◽

Hybrid Approach ◽

The Other ◽

Geometric Features ◽

Mating Pair ◽

Packaging Process ◽

Common Features ◽

Mating Conditions ◽

Quasi Newton ◽

Object Relative

A hybrid method combining a genetic algorithms based containment algorithm with a complex mating algorithm is presented. The approach uses mating between a pair of objects as means to accelerate the packaging process. In this study, mating between two objects has been defined as positioning one object relative to others by merging common features that are assigned through mating conditions between them. A constrained move set is derived from the mating condition that allows the transformation of a component in each mating pair to be fully or partially constrained with respect to the other. By using mating in the packaging, the number of components to be placed can be reduced significantly and overall speed of the packaging process can also be improved. The hybrid method uses a genetic algorithm to search mating pairs and global positions of selected objects. The mating pair is mated first by a simple mating condition which is derived from geometric features of mating objects. If a proper mating is not obtained, the complex mating algorithm finds an optimal mating condition using Quasi-Newton method.

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