scholarly journals Improving Bees Algorithm Using Gradual Search Space Reduction

2021 ◽  
Author(s):  
Turki Binbakir
Keyword(s):  
Numerical Optimization ◽  
Optimization Methods ◽  
Search Space ◽  
Optimization Method ◽  
New Technique ◽  
Bees Algorithm ◽  
Elimination Method ◽  
Space Reduction ◽  
Engineering Problems ◽  
A New Technique

Abstract The aim of this research is to propose a new technique to improve Bees Algorithm. Bees Algorithm is one of the well-known metaheuristic optimization method which have been subject to several attempts to improve it by overcoming some of the weaknesses. The suggested method is derived from the numerical optimization methods, namely bracketing and region elimination methods. It employs an adaption of the regional elimination method to achieve abandonment and reduction of search space within the Bees Algorithm. The utilization of the exhaustive search involves exploring the whole search space to find the optimum at equally located intervals. To assess performance, the proposed method was evaluated on twenty-four benchmark functions and two engineering problems. The acquired result indicated a statistically significant improvement.

Nonsmooth Optimization Method for Image Restoration

1995 ◽  
Author(s):  
Kazufumi Ito ◽  
Karl Kunisch
Keyword(s):  
Image Restoration ◽  
Nonsmooth Optimization ◽  
Numerical Optimization ◽  
Augmented Lagrangian ◽  
Energy Criterion ◽  
Optimization Methods ◽  
Optimization Method ◽  
Augmented Lagrangian Methods ◽  
Active Set ◽  
First Order

Abstract In this paper we discuss applications of the numerical optimization methods for nonsmooth optimization, developed in [IK1] for the variational formulation of image restoration problems involving bounded variation type energy criterion. The Uzawa’s algorithm, first order augmented Lagrangian methods and Newton-like update using the active set strategy are described.

Design Optimization Method for MR-Brake Actuators

2011 ◽  
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.

Model-Based Actuator Trajectories Optimization for a Diesel Engine Using a Direct Method

2010 ◽  
Vol 133 (3) ◽  
Author(s):  
Michael Benz ◽  
Markus Hehn ◽  
Christopher H. Onder ◽  
Lino Guzzella
Keyword(s):  
Numerical Optimization ◽  
Fuel Injection ◽  
Direct Method ◽  
Optimization Methods ◽  
Optimization Method ◽  
Mean Value ◽  
Pollutant Emissions ◽  
Pollutant Emission ◽  
Transient Operation ◽  
Engine Model

This paper proposes a novel optimization method that allows a reduction in the pollutant emission of diesel engines during transient operation. The key idea is to synthesize optimal actuator commands using reliable models of the engine system and powerful numerical optimization methods. The engine model includes a mean-value engine model for the dynamics of the gas paths, including the turbocharger of the fuel injection, and of the torque generation. The pollutant formation is modeled using an extended quasi-static modeling approach. The optimization substantially changes the input signals, such that the engine model is enabled to extrapolate all relevant outputs beyond the regular operating area. A feedforward controller for the injected fuel mass is used to eliminate the nonlinear path constraints during the optimization. The model is validated using experimental data obtained on a transient engine test bench. A direct single shooting method is found to be most effective for the numerical optimization. The results show a significant potential for reducing the pollutant emissions during transient operation of the engine. The optimized input trajectories derived assist the design of sophisticated engine control systems.

scholarly journals A Novel Approach to Improve the Performance of Evolutionary Methods for Nonlinear Constrained Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Alireza Rowhanimanesh ◽  
Sohrab Efati
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Search Space ◽  
Global Optimum ◽  
Constrained Optimization Problems ◽  
Nonlinear Constrained Optimization ◽  
Evolutionary Methods ◽  
Space Reduction ◽  
Reduced Space

Evolutionary methods are well-known techniques for solving nonlinear constrained optimization problems. Due to the exploration power of evolution-based optimizers, population usually converges to a region around global optimum after several generations. Although this convergence can be efficiently used to reduce search space, in most of the existing optimization methods, search is still continued over original space and considerable time is wasted for searching ineffective regions. This paper proposes a simple and general approach based on search space reduction to improve the exploitation power of the existing evolutionary methods without adding any significant computational complexity. After a number of generations when enough exploration is performed, search space is reduced to a small subspace around the best individual, and then search is continued over this reduced space. If the space reduction parameters (red_gen and red_factor) are adjusted properly, reduced space will include global optimum. The proposed scheme can help the existing evolutionary methods to find better near-optimal solutions in a shorter time. To demonstrate the power of the new approach, it is applied to a set of benchmark constrained optimization problems and the results are compared with a previous work in the literature.

FINDING k FARTHEST PAIRS AND k CLOSEST/FARTHEST BICHROMATIC PAIRS FOR POINTS IN THE PLANE

1995 ◽  
Vol 05 (01n02) ◽  
pp. 37-51 ◽  
Author(s):  
NAOKI KATOH ◽  
KAZUO IWANO
Keyword(s):  
Voronoi Diagrams ◽  
Search Space ◽  
Higher Order ◽  
Efficient Algorithms ◽  
New Technique ◽  
Enumeration Problems ◽  
A New Technique ◽  
Geometric Enumeration

We study the problem of enumerating k farthest pairs for n points in the plane and the problem of enumerating k closest/farthest bichromatic pairs of n red and n blue points in the plane. We propose a new technique for geometric enumeration problems which iteratively reduces the search space by a half and provides efficient algorithms. As applications of this technique, we develop algorithms, using higher order Voronoi diagrams, for the above problems, which run in O(min{n2, n log n+k4/3log n/log1/3 k}) time and O(n+k4/3/log1/3 k+k log n) space for general Lp metric with p≠2, and O(min{n2, n log n+k4/3}) time and O(n+k4/3+k log n) space for L2 metric. For the problem of enumerating k closest/farthest bichromatic pairs, we shall also discuss the case where we have different numbers of red and blue points. To the authors’ knowledge, no nontrivial algorithms have been known for these problems and our algorithms are faster than trivial ones when k=o(n3/2).

scholarly journals Lifecycle-Based Swarm Optimization Method for Numerical Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Hai Shen ◽  
Yunlong Zhu ◽  
Xiaodan Liang
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Individual Organism ◽  
Engineering Problems ◽  
Mechanical Design Problem

Bioinspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biological lifecycle, this paper presents a novel optimization algorithm called lifecycle-based swarm optimization (LSO). Biological lifecycle includes four stages: birth, growth, reproduction, and death. With this process, even though individual organism died, the species will not perish. Furthermore, species will have stronger ability of adaptation to the environment and achieve perfect evolution. LSO simulates Biological lifecycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection, and mutation. In addition, the spatial distribution of initialization population meets clumped distribution. Experiments were conducted on unconstrained benchmark optimization problems and mechanical design optimization problems. Unconstrained benchmark problems include both unimodal and multimodal cases the demonstration of the optimal performance and stability, and the mechanical design problem was tested for algorithm practicability. The results demonstrate remarkable performance of the LSO algorithm on all chosen benchmark functions when compared to several successful optimization techniques.

A New Global Optimization Method for Simultaneous Computation on Expensive Black-Box Functions

2003 ◽  
Author(s):  
Liqun Wang ◽  
Songqing Shan ◽  
G. Gary Wang
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Search Space ◽  
Optimization Method ◽  
Global Optimum ◽  
Black Box ◽  
Efficient Global Optimization ◽  
Global Optimization Method ◽  
Global Optimization Methods

The presence of black-box functions in engineering design, which are usually computation-intensive, demands efficient global optimization methods. This work proposes a new global optimization method for black-box functions. The global optimization method is based on a novel mode-pursuing sampling (MPS) method which systematically generates more sample points in the neighborhood of the function mode while statistically covers the entire search space. Quadratic regression is performed to detect the region containing the global optimum. The sampling and detection process iterates until the global optimum is obtained. Through intensive testing, this method is found to be effective, efficient, robust, and applicable to both continuous and discontinuous functions. It supports simultaneous computation and applies to both unconstrained and constrained optimization problems. Because it does not call any existing global optimization tool, it can be used as a standalone global optimization method for inexpensive problems as well. Limitation of the method is also identified and discussed.

Development of a Data-Partition Technique for Gradient-Based Optimization Methods in History Matching

SPE Journal ◽  
2011 ◽  
Vol 16 (03) ◽  
pp. 582-593 ◽  
Author(s):  
D.Y.. Y. Ding
Keyword(s):  
Objective Function ◽  
History Matching ◽  
Optimization Methods ◽  
New Technique ◽  
Large Field ◽  
Data Partition ◽  
Geostatistical Models ◽  
Gradient Based ◽  
A New Technique ◽  
Influential Parameters

Summary Assisted history matching is now widely used to constrain reservoir models. However, history matching is a complex inverse problem, and it is always a big challenge to history match large fields with a large number of parameters. In this paper, we present a new technique for the gradient-based optimization methods to improve history matching for large fields. This new technique is based on data partition for the gradient calculations. In history matching, the objective function can be split into local components, and a local component generally depends on fewer influential parameters. On the basis of this decomposition, we can propose a perturbation design, which allows us to calculate all derivatives of the objective function with only a few perturbations. This method is particularly interesting for regional and well-level history matching, and it is also suitable to match geostatistical models by introducing numerous local parameters. This new technique makes history matching with a large number of parameters (large field) tractable.

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