Stochastic Evolutionary Algorithm Research Articles

Particle Swarm Optimization Algorithm Based on Escape Boundary

The particle swarm optimization (PSO) is a population-based stochastic evolutionary algorithm, noted for its capability of searching for the global optimum of complex problems. Particles flying out of the solution space will lead to invalid solutions. So often in engineering applications, boundary condition is used to confine the particles within the solution space. In this paper, a new boundary is proposed, which is called as escape boundary. The solution space is divided into three sections, that is, the inside，escape boundary and the outside of the boundary. The location of the global solution in the solution space, accordingly has two types, that is, the global optimum around the center of the solution space, and the global optimum close to the escape boundary. The proposed boundary is introduced into the PSO algorithm, and is compared to the damping boundary. The experimental results show that the PSO based on escape boundary has better search ability and faster convergence rate.

Robust design optimization in aeronautics using stochastic analysis and evolutionary algorithms

Uncertainties are a daily issue to deal with in aerospace engineering and applications. Robust optimization methods commonly use a random generation of the inputs and take advantage of multi-point criteria to look for robust solutions accounting with uncertainty definition. From the computational point of view, the application to coupled problems, like computational fluid dynamics (CFD) or fluid–structure interaction (FSI), can be extremely expensive. This study presents a coupling between stochastic analysis techniques and evolutionary optimization algorithms for the definition of a stochastic robust optimization procedure. At first, a stochastic procedure is proposed to be applied into optimization problems. The proposed method has been applied to both CFD and FSI problems for the reduction of drag and flutter, respectively.

Multi-objective cropping pattern in the Vaalharts irrigation scheme

This study presents the multi-objective cropping pattern modeling of a farm in the Vaalharts irrigation scheme (VIS) in South Africa. The cropping pattern model presents three objectives and three constraints. The objectives are to maximize the total net benefit (NB) in monetary terms (South African Rand, ZAR) generated by planting four different crops, maximize total agricultural output (tons) and minimize the irrigation water use (m3). The total farm size is 77.1 ha while the total available water for irrigation is 9,140 m3 per ha/annum. Multi-objective differential evolution algorithm (MDEA) which is a stochastic multi-objective evolutionary algorithm recently developed was used to solve the multi-objective model in this study. The model produced non-dominated solutions that converge to Pareto optimal front. The averages of total net benefit, total agricultural output, total irrigation water and total area planted are ZAR 882 890.63, 3 439 518.75 tons, 702 522.50 m3 and 661 444.06 m2 respectively with corresponding average planting areas of 416 680, 53 030, 87 620 and 212 410 m2 for maize, groundnut, Lucerne and Pecan nuts respectively. It is concluded that MDEA is a good optimizer for multi-objective cropping pattern model for generating maximum agricultural output for the farmers in the area with the constraints of land and water availabilities. Key words: Cropping pattern, irrigation, agricultural output, differential evolution, non-dominated solutions.

Space-filling Latin hypercube designs for computer experiments

In the area of computer simulation, Latin hypercube designs play an important role. In this paper the classes of maximin and Audze-Eglais Latin hypercube designs are considered. Up to now only several two-dimensional designs and a few higher dimensional designs for these classes have been published. Using periodic designs and the Enhanced Stochastic Evolutionary algorithm of Jin et al. (J. Stat. Plan. Interference 134(1):268–687, 2005), we obtain new results which we compare to existing results. We thus construct a database of approximate maximin and Audze-Eglais Latin hypercube designs for up to ten dimensions and for up to 300 design points. All these designs can be downloaded from the website http://www.spacefillingdesigns.nl.

A rank based particle swarm optimization algorithm with dynamic adaptation

The particle swarm optimization (PSO) technique is a powerful stochastic evolutionary algorithm that can be used to find the global optimum solution in a complex search space. This paper presents a variation on the standard PSO algorithm called the rank based particle swarm optimizer, or PSO rank, employing cooperative behavior of the particles to significantly improve the performance of the original algorithm. In this method, in order to efficiently control the local search and convergence to global optimum solution, the γ best particles are taken to contribute to the updating of the position of a candidate particle. The contribution of each particle is proportional to its strength. The strength is a function of three parameters: strivness, immediacy and number of contributed particles. All particles are sorted according to their fitness values, and only the γ best particles will be selected. The value of γ decreases linearly as the iteration increases. A time-varying inertia weight decreasing non-linearly is introduced to improve the performance. PSO rank is tested on a commonly used set of optimization problems and is compared to other variants of the PSO algorithm presented in the literature. As a real application, PSO rank is used for neural network training. The PSO rank strategy outperformed all the methods considered in this investigation for most of the functions. Experimental results show the suitability of the proposed algorithm in terms of effectiveness and robustness.

A Stochastic Approach to Image Retrieval Using Relevance Feedback and Particle Swarm Optimization

Understanding the subjective meaning of a visual query, by converting it into numerical parameters that can be extracted and compared by a computer, is the paramount challenge in the field of intelligent image retrieval, also referred to as the ?semantic gap? problem. In this paper, an innovative approach is proposed that combines a relevance feedback (RF) approach with an evolutionary stochastic algorithm, called particle swarm optimizer (PSO), as a way to grasp user's semantics through optimized iterative learning. The retrieval uses human interaction to achieve a twofold goal: 1) to guide the swarm particles in the exploration of the solution space towards the cluster of relevant images; 2) to dynamically modify the feature space by appropriately weighting the descriptive features according to the users' perception of relevance. Extensive simulations showed that the proposed technique outperforms traditional deterministic RF approaches of the same class, thanks to its stochastic nature, which allows a better exploration of complex, nonlinear, and highly-dimensional solution spaces.

Hybrid evolutionary algorithm for microscrew thread parameter estimation

In this paper we propose a novel approach to the problem of microscrew thread parameter estimation based on a hybrid evolutionary algorithm that combines a stochastic evolutionary algorithm with the deterministic inverse parabolic interpolation. The proposed method uses a machine vision system utilizing a single web camera. The hybrid evolutionary algorithm was tested on a specially created image database of microscrews. Experimental results prove speed and efficiency of the proposed method and its robustness to noise in the images. This method may be used in automated systems of real-time non-destructive quality control of microscrews and has potential for parameter estimation of different types of microparts.

Improved design of a centrifugal pump impeller using CFD and numerical optimisation tools

The possibility of improving the design and increasing the hydraulic efficiency of a centrifugal pump impeller is investigated, with the implementation of advanced numerical tools and experimental data from a laboratory model. Parametric studies are conducted to examine the influence of several design parameters, like the blade length, inlet height, leading edge inclination, cambering, and mean line curvature. The optimum values of the above that maximise the hydraulic efficiency at the BEP are then derived using a stochastic evolutionary algorithm software. The improved impeller design exhibits a considerable increase in hydraulic efficiency compared to the initial design.

Anisotropic diffusion with generalized diffusion coefficient function for defect detection in low-contrast surface images

In this paper, an anisotropic diffusion model with a generalized diffusion coefficient function is presented for defect detection in low-contrast surface images and, especially, aims at material surfaces found in liquid crystal display (LCD) manufacturing. A defect embedded in a low-contrast surface image is extremely difficult to detect, because the intensity difference between the unevenly illuminated background and the defective region is hardly observable and no clear edges are present between the defect and its surroundings. The proposed anisotropic diffusion model provides a generalized diffusion mechanism that can flexibly change the curve of the diffusion coefficient function. It adaptively carries out a smoothing process for faultless areas and performs a sharpening process for defect areas in an image. An entropy criterion is proposed as the performance measure of the diffused image and then a stochastic evolutionary computation algorithm, particle swarm optimization (PSO), is applied to automatically determine the best parameter values of the generalized diffusion coefficient function. Experimental results have shown that the proposed method can effectively and efficiently detect small defects in various low-contrast surface images.

Estimation of parameters of a biochemically based model of photosynthesis using a genetic algorithm

Photosynthesis response to carbon dioxide concentration can provide data on a number of important parameters related to leaf physiology. The genetic algorithm (GA), which is a robust stochastic evolutionary computational algorithm inspired by both natural selection and natural genetics, is proposed to simultaneously estimate the parameters [including maximum carboxylation rate allowed by ribulose 1.5-bisphosphate carboxylase/oxygenase (Rubisco) carboxylation rate (V(cmax)), potential light-saturated electron transport rate (J(max)), triose-phosphate utilization (TPU), leaf dark respiration in the light (R(d)) and mesophyll conductance (g(m))] of the photosynthesis models presented by Farquhar, von Caemmerer and Berry, and Ethier and Livingston. The results show that by properly constraining the parameter bounds the GA-based estimate methods can effectively and efficiently obtain globally (or, at least near globally) optimal solutions, which are as good as or better than those obtained by non-linear curve fitting methods used in previous studies. More complicated problems such as taking the g(m) variation response to CO(2) into account can be easily formulated and solved by using GA. The influence of the crossover probability (P(c)), mutation probability (P(m)), population size and generation on the performance of GA was also investigated.

An algorithm for fast optimal Latin hypercube design of experiments

AbstractThis paper presents the translational propagation algorithm, a new method for obtaining optimal or near optimal Latin hypercube designs (LHDs) without using formal optimization. The procedure requires minimal computational effort with results virtually provided in real time. The algorithm exploits patterns of point locations for optimal LHDs based on the ϕp criterion (a variation of the maximum distance criterion). Small building blocks, consisting of one or more points each, are used to recreate these patterns by simple translation in the hyperspace. Monte Carlo simulations were used to evaluate the performance of the new algorithm for different design configurations where both the dimensionality and the point density were studied. The proposed algorithm was also compared against three formal optimization approaches (namely random search, genetic algorithm, and enhanced stochastic evolutionary algorithm). It was found that (i) the distribution of the ϕp values tends to lower values as the dimensionality is increased and (ii) the proposed translational propagation algorithm represents a computationally attractive strategy to obtain near optimum LHDs up to medium dimensions. Copyright © 2009 John Wiley & Sons, Ltd.

Cross-docking: increasing platform efficiency by sequencing incoming and outgoing semi-trailers

Cross-docking involves use of a transhipment platform to consolidate incoming products for outgoing destinations. Research in cross-docking mostly studies the cross-dock network (platform location) and the platform design (door locations). In this study, we consider a platform's internal operation and we focus on the direct flow of products from receiving to shipping doors. This flow can be improved by the synchronisation of incoming and outgoing semi-trailers. Two methods – dynamic programming and a heuristic approach integrated with a stochastic evolutionary algorithm – are proposed as a resolution approach for this problem.

LINEAR AND CIRCULAR ARRAY OPTIMIZATION: A STUDY USING PARTICLE SWARM INTELLIGENCE

Linear and circular arrays are optimized using the particle swarm optimization (PSO) method. Also, arrays of isotropic and cylindrical dipole elements are considered. The parameters of isotropic arrays are elements excitation amplitude, excitation phase and locations, while for dipole array the optimized parameters are elements excitation amplitude, excitation phase, location, and length. PSO is a high-performance stochastic evolutionary algorithm used to solve N-dimensional problems. The method of PSO is used to determine a set of parameters of antenna elements that provide the goal radiation pattern. The efiectiveness of PSO for the design of antenna arrays is shown by means of numerical results. Comparison with other methods is made whenever possible. The results reveal that design of antenna arrays using the PSO method provides considerable enhancements compared with the uniform array and the synthesis obtained from other optimization techniques.

Optimizing chemistry of bulk metallic glasses for improved thermal stability

Thermo-mechanical-physical properties of bulk metallic glasses (BMGs) depend strongly on the concentrations of each of the chemical elements in a given alloy. The proposed methodology for simultaneously optimizing these multiple properties by accurately determining proper concentrations of each of the alloying elements is based on the use of computational algorithms rather than on traditional experimentation, expert experience and intuition. Specifically, the proposed BMG design method combines an advanced stochastic multi-objective evolutionary optimization algorithm based on self-adapting response surface methodology and an existing database of experimentally evaluated BMG properties. During the iterative computational design procedure, a relatively small number of new BMGs need to be manufactured and experimentally evaluated for their properties in order to continuously verify the accuracy of the entire design methodology. Concentrations of the most important alloying elements can be predicted so that new BMGs have multiple properties optimized in a Pareto sense. This design concept was verified for superalloys using strictly experimental data. Thus, the key innovation here lies in arriving at the BMG compositions which will have the highest glass forming ability by utilizing an advanced multi-objective optimization algorithm while requiring a minimum number of BMGs to be manufactured and tested in order to verify the predicted performance of the predicted BMG compositions.

Optimal sizing of a run-of-river small hydropower plant

The sizing of a small hydropower plant of the run-of-river type is very critical for the cost effectiveness of the investment. In the present work, a numerical method is used for the optimal sizing of such a plant that comprises two hydraulic turbines operating in parallel, which can be of different type and size in order to improve its efficiency. The study and analysis of the plant performance is conducted using a newly developed evaluation algorithm that simulates in detail the plant operation during the year and computes its production results and economic indices. A parametric study is performed first in order to quantify the impact of some important construction and operation factors. Next, a stochastic evolutionary algorithm is implemented for the optimization process. The examined optimization problem uses data of a specific site and is solved in the single and two-objective modes, considering, together with economic, some additional objectives, as maximization of the produced energy and the best exploitation of the water stream potential. Analyzing the results of various optimizations runs, it becomes possible to identify the most advantageous design alternatives to realize the project. It was found that the use of two turbines of different size can enhance sufficiently both the energy production of the plant and the economic results of the investment. Finally, the sensitivity of the plant performance to other external parameters can be easily studied with the present method, and some indicative results are given for different financial or hydrologic conditions.

Design of an extended unknown input observer with stochastic robustness techniques and evolutionary algorithms

The paper deals with the problem of designing an unknown input observer for discrete-time non-linear systems. In particular, with the use of the Lyapunov method, it is shown that the proposed observer is convergent under certain, non-restrictive conditions. Based on the achieved results, a general solution for increasing the convergence rate is proposed and implemented with the use of stochastic robustness techniques. In particular, it is shown that the problem of increasing the convergence rate of the observer can be formulated as a stochastic robustness analysis task that can be transformed into a structure selection and parameter estimation problem of a non-linear function, which can be solved with the B-spline approximation and evolutionary algorithms. The final part of the paper shows an illustrative example based on a two phase induction motor. The presented results clearly exhibit the performance of the proposed observer.

Comparison of Direct Multiobjective Optimization Methods for the Design of Electric Vehicles

“System design oriented methodologies” are discussed in this paper through the comparison of multiobjective optimization methods applied to heterogeneous devices in electrical engineering. Avoiding criteria function derivatives, direct optimization algorithms are used. In particular, deterministic geometric methods such as the Hooke & Jeeves heuristic approach are compared with stochastic evolutionary algorithms (Pareto genetic algorithms). Different issues relative to convergence rapidity and robustness on mixed (continuous/discrete), constrained and multiobjective problems are discussed. A typical electrical engineering heterogeneous and multidisciplinary system is considered as a case study: the motor drive of an electric vehicle. Some results emphasize the capacity of each approach to facilitate system analysis and particularly to display couplings between optimization parameters, constraints, objectives and the driving mission.

Critical Practice: Protocol for a Fused Technology

AbstractSoftware created for architecture often presumes a certain repertoire of design intents; software appropriated from other fields does not make such presumptions but introduces translation difficulties into material logics. Aaron Sprecher, Chandler Ahrens and Eran Neuman describe how their collaborative design group, Open Source Architecture (O‐S‐A), has worked with structural engineers and software developers to create a computational protocol based on a stochastic evolutionary topological optimisation algorithm. This procedure allows architectural designers to work with complex geometries previously limited to the expertise of engineering. In addition to taking the form of a distributed network spanning New York, Los Angeles and Tel Aviv, O‐S‐A opens the possibility for various disciplines (computer science, structural engineering, architectural design) to work with a fluidity and commonality not previously possible. Like the work of RSG (also featured in this issue), here design begins with the construction of a software environment in which a project can occur, and which merges previously distinct areas of expertise. Copyright © 2006 John Wiley & Sons, Ltd.

An independent component analysis-based filter design for defect detection in low-contrast surface images

In this paper, we propose a convolution filtering scheme for detecting small defects in low-contrast uniform surface images and, especially, focus on the applications for backlight panels and glass substrates found in liquid crystal display (LCD) manufacturing. A defect embedded in a low-contrast surface image shows no distinct intensity from its surrounding region, and even worse, the sensed image may present uneven brightness on the surface. All these make the defect detection in low-contrast surface images extremely difficult. In this study, a constrained independent component analysis (ICA) model is proposed to design an optimal filter with the objective that the convolution filter will generate the most representative source intensity of the background surface without noise. The prior constraint incorporated in the ICA model confines the source values of all training image patches of a defect-free image within a small interval of control limits. In the inspection process, the same control parameter used in the constraint is also applied to set up the thresholds that make impulse responses of all pixels in faultless regions within the control limits, and those in defective regions outside the control limits. A stochastic evolutionary computation algorithm, particle swarm optimization (PSO), is applied to solve for the constrained ICA model. Experimental results have shown that the proposed method can effectively detect small defects in low-contrast backlight panels and LCD glass substrate images.

Space-Filling Latin Hypercube Designs for Computer Experiments

In the area of computer simulation, Latin hypercube designs play an important role. In this paper the classes of maximin and Audze-Eglais Latin hypercube designs are considered. Up to now only several two-dimensional designs and a few higher dimensional designs for these classes have been published. Using periodic designs and the Enhanced Stochastic Evolutionary algorithm of Jin et al. (2005), we obtain new results which we compare to existing results. We thus construct a database of approximate maximin and Audze-Eglais Latin hypercube designs for up to ten dimensions and for up to 300 design points.