Search

Abstract: Interactive optimization algorithms use real–time interaction to include decision maker preferences based on the subjective quality of evolving solutions. In water resources management problems where numerous qualitative criteria exist, use of such interactive optimization methods can facilitate in the search for comprehensive and meaningful solutions for the decision maker. The decision makers using such a system are, however, likely to go through their own learning process as they view new solutions and gain knowledge about the design space. This leads to temporal changes (nonstationarity) in their preferences that can impair the performance of interactive optimization algorithms. This paper proposes a new interactive optimization algorithm – Case-Based Micro Interactive Genetic Algorithm – that uses a case-based memory and case-based reasoning to manage the effects of nonstationarity in decision maker’s preferences within the search process without impairing the performance of the search algorithm. This paper focuses on exploring the advantages of such an approach within the domain of groundwater monitoring design, though it is applicable to many other problems. The methodology is tested under non-stationary preference conditions using simulated and real human decision makers, and it is also compared with a non-interactive genetic algorithm and a previous version of the interactive genetic algorithm. [Copyright &y& Elsevier]

Abstract: This paper presents a new procedure for the optimization of the mooring design of floating platforms, in which an automatic design sequence is also established. Regarding the optimization philosophy, the following aspects are dealt with: [•] The optimization of the platform heading and its mooring pattern, taking into account the environmental force spreading; [•] optimum line length or line tension for each mooring line, associated to the optimization of the mooring line materials and sizes. Basically, the main goal of this paper is to introduce a new method, which will provide the quickest way to find the best mooring system, defined here as that which minimizes platform responses. A genetic algorithm (GA) is applied in this contribution, and this paper describes exactly the procedure of developing a GA code directed toward the solution of mooring design optimization problems. In order to prove the efficiency and the vast potential of the proposed algorithm as a design tool, sample moorings are analyzed for different environmental conditions and the final results, including the time required to run them, are presented. [Copyright &y& Elsevier]

Abstract: Grid computing system is different from conventional distributed computing systems by its focus on large-scale resource sharing and open architecture for services. The global grid technologies and the Globus Toolkit in particular, are evolving toward an open grid service architecture (OGSA) with which a grid system provides an extensible infrastructure so that various organizations can offer their own services and integrate their resources. Hence, this paper aims at solving the problem of optimally allocating services on the grid to maximize the grid service reliability. Since no existing study has analyzed the grid service reliability, this paper develops initial modeling and evaluation algorithms to evaluate the grid service reliability. Based on the grid service reliability evaluation, we present an optimization model for the grid service allocation problem and develop a genetic algorithm (GA) to effectively solve it. A numerical example is given to show the modeling procedures and efficiency of the GAs. [Copyright &y& Elsevier]

• Mathematical modeling of the torsional-bar and gas-spring based trunk lid mechanisms for sedan type of vehicles. • An automated structural design approach for two different trunk lid mechanisms. • Minimizing the differences between desired and calculated driving forces. • Comparing the performance of population-based PSO, GA and DE optimization algorithms. This paper proposes a parametric synthesis of two different trunk lid mechanisms for sedan vehicles, incorporating three different population-based optimization techniques: Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and the Differential Evolution (DE). For this purpose, the kinematic equations of the mechanisms are derived and implemented in the quasi-static analysis to determine the necessary driving forces. The lengths and initial angles of four-bar linkage are optimized in order to minimize the sum of the differences between the target and calculated hand force values. The optimization results of the design variables and the performances of the optimization methods are presented. The outcomes indicate that the proposed design procedure is able to provide a trunk lid mechanism in which the target value of driving force in every moving angle is achieved. Furthermore, it is observed that the optimization techniques show different performances due to the fact that they provide better and faster optimal solutions than each other for different cases. The results of this paper are of utmost importance for the manufacturer to obtain an automated design process for the trunk lid mechanisms. [ABSTRACT FROM AUTHOR]

Abstract: The ability to track the optimum of dynamic environments is important in many practical applications. In this paper, the capability of a hybrid genetic algorithm (HGA) to track the optimum in some dynamic environments is investigated for different functional dimensions, update frequencies, and displacement strengths in different types of dynamic environments. Experimental results are reported by using the HGA and some other existing evolutionary algorithms in the literature. The results show that the HGA has better capability to track the dynamic optimum than some other existing algorithms. [Copyright &y& Elsevier]

Abstract: Medical data mining has recently become one of the most popular topics in the data mining community. This is due to the societal importance of the field and also the particular computational challenges posed in this domain of data mining. However, current medical data mining approaches oftentimes use identical costs or just ignore them for the different cases of classification errors. Thus, their outcome may be unexpected. This paper applies a new meta-heuristic approach, called the Homogeneity-Based Algorithm (or HBA), for optimizing the classification accuracy when analyzing some medical datasets. The HBA first expresses the objective as an optimization problem in terms of the error rates and the associated penalty costs. These costs may be dramatically different in medical applications as the implications of having a false-positive and a false-negative case may be tremendously different. When the HBA is combined with traditional classification algorithms, it enhances their prediction accuracy. It does so by using the concept of homogenous sets. Five medical datasets, obtained from the machine learning data repository at the University of California, Irvine (UCI), USA, were tested. Some computational results indicate that the HBA, when it is combined with traditional methods, can significantly outperform current stand-alone data mining approaches. [Copyright &y& Elsevier]

Abstract: Automatic greenhouse production is quite new in China. For the development of our modern agriculture it is a significant issue to accurately formulate the simulation growth models of greenhouse plants in different environments. The objective of our study was to develop an approach to calibrate the growth model of greenhouse crop. In this paper, an adaptive genetic algorithm (GA) is proposed and evaluated for this issue. This new algorithm is composed of two GAs. The primary one is utilized to parameterize the growth model and the secondary is to determine the algorithmic parameters of the primary GA. The superior performance of this new procedure is demonstrated through its applications to three test functions and the greenhouse optimization problems compared with other two GAs. This presented technique may be a fine framework for the development of similar application for complex biological models that require parameterization when a new set of environmental conditions arises or there is a need to account for differences among subspecies or varieties. [Copyright &y& Elsevier]

Abstract: In this paper we propose a new approach in genetic algorithm called distributed hierarchical genetic algorithm (DHGA) for optimization and pattern matching. It is eventually a hybrid technique combining the advantages of both distributed and hierarchical processes in exploring the search space. The search is initially distributed over the space and then in each subspace the algorithm works in a hierarchical way. The entire space is essentially partitioned into a number of subspaces depending on the dimensionality of the space. This is done in order to spread the search process more evenly over the whole space. In each subspace the genetic algorithm is employed for searching and the search process advances from one hypercube to a neighboring hypercube hierarchically depending on the convergence status of the population and the solution obtained so far. The dimension of the hypercube and the resolution of the search space are altered with iterations. Thus the search process passes through variable resolution (coarse-to-fine) search space. Both analytical and empirical studies have been carried out to evaluate the performance between DHGA and distributed conventional GA (DCGA) for different function optimization problems. Further, the performance of the algorithms is demonstrated on problems like pattern matching and object matching with edge map. [Copyright &y& Elsevier]

A nonlinear predictive control technique is developed to determine the optimal drying profile for a drying process. A complete nonlinear model of the baker's yeast drying process is used for predicting the future control actions. To minimize the difference between the model predictions and the desired trajectory throughout finite horizon, an objective function is described. The optimization problem is solved using a genetic algorithm due to the successful overconventional optimization techniques in the applications of the complex optimization problems. The control scheme comprises a drying process, a nonlinear prediction model, .an optimizer, and a genetic search block. The nonlinear predictive control method proposed in this paper is applied to the baker's yeast drying process. The results show significant enhancement of the manufacturing quality, considerable decrease of the energy consumption and drying time, obtained by the proposed nonlinear predictive control. [ABSTRACT FROM AUTHOR]

Abstract: Evolutionary algorithms (EAs) are stochastic search methods that mimic the natural biological evolution and/or the social behavior of species. Such algorithms have been developed to arrive at near-optimum solutions to large-scale optimization problems, for which traditional mathematical techniques may fail. This paper compares the formulation and results of five recent evolutionary-based algorithms: genetic algorithms, memetic algorithms, particle swarm, ant-colony systems, and shuffled frog leaping. A brief description of each algorithm is presented along with a pseudocode to facilitate the implementation and use of such algorithms by researchers and practitioners. Benchmark comparisons among the algorithms are presented for both continuous and discrete optimization problems, in terms of processing time, convergence speed, and quality of the results. Based on this comparative analysis, the performance of EAs is discussed along with some guidelines for determining the best operators for each algorithm. The study presents sophisticated ideas in a simplified form that should be beneficial to both practitioners and researchers involved in solving optimization problems. [Copyright &y& Elsevier]

Risk Informed In-Service Inspection (RI-ISI) aims at prioritising the components for inspection within the permissible risk level thereby avoiding unnecessary inspections. Various constraints such as risk level, radiation exposure to the workers and cost of inspections are encountered, while planning the inspection programme. This problem has been attempted to solve using genetic algorithms, which has already established its suitability in optimizing Surveillance and Maintenance activities in Nuclear Power Plants. The paper describes the application of genetic algorithm in optimizing the ISI of feeders, which are large in number and also fall in the same inspection category. [Copyright &y& Elsevier]

Measurements of voltage flicker levels and its frequency is of great concern to the utility in order to prevent unacceptable voltage fluctuation in the supplying system. This paper introduces a new digital approach for the measurements of voltage flicker and its frequency using the Genetic Algorithm (GAs) optimization technique. The algorithm is tested using simulated data. Effects of number of samples, sampling frequency and the sample window size are studied. Effects of GAs parameters and operators, such as population size, crossover, mutation probabilities, niching and fitness functions are also studied. Results are reported and discussed. [Copyright &y& Elsevier]