A Genetic Algorithm for Split Delivery Open Vehicle Routing Problem with Physical Workload Consideration

The application of genetic algorithms in behavioural ecology, illustrated with a model of anti-predator vigilance

Constant increase in urban road traffic is forcing traffic authorities to confront unavoidable congestion problems these days. This creates new challenges for city planners to generate traffic routes with better designs and improve the existing roads. In order to analyze and improve traffic routes, there is a need to understand movement patterns of major vehicular traffic inside the city. Origin Destination(OD) estimation is one such method for understanding the movement patterns. The objective is to find an optimal OD matrix for city traffic, which is a subtle process for classic algorithms. The solution to this problem is Genetic Algorithm (GA) approach which is a search heuristics to find optimal solution from a set of random solutions. A Genetic Algorithm approach has been developed to find an optimal OD matrix, showing the actual travel patterns of significant number of vehicles inside the city. Once the best OD matrix is obtained, it yields a big opportunity for traffic planners to analyze and improve traffic scenario. Genetic Algorithm can play a major role in solving complex problems. In this paper, one such problem of estimating OD matrix of a city is discussed and implemented. In conjunction to genetic algorithm, Mean Absolute Percentage Error(MAPE) has been used as a fitness criteria for finding the optimal OD matrix.

This study deals with estimation of the total and industrial sector electricity consumption based on genetic algorithm (GA) approach, and then proposes two scenarios to project future consumptions. Total electricity consumption is estimated based on gross national product (GNP), population, import and export figures of Turkey. Industrial sector electricity is calculated based on the GNP, import and export figures. Three forms of the genetic algorithm electricity demand (GAED) models for the total and two forms for the industrial electricity consumption are developed. The best-fit GAED model in terms of total minimum relative average errors between observed and estimated values is selected for future demand estimation. ‘High- and low-growth scenarios’ are proposed for predicting the future electricity consumption. Results showed that the GAED estimates the electricity demand in comparison with the other electricity demand projections. The GAED model plans electricity demand of Turkey until 2020. Copyright © 2005 John Wiley & Sons, Ltd.

Design and optimization of rudder bulb based on CFD and genetic algorithm

In this paper, an effective genetic algorithm (GA) approach is proposed for tuning the parameters of membership functions based on input-output pairs. By minimizing a quadratic measure of the error in the least-squares sense, the real-valued chromosomes of a population are evolved to get the best coefficients. Comparison to the well-known back-propagation algorithm for fuzzy logic system shows that both are powerful training algorithms, but much better performance is obtained with the proposed technique. Several numerical design examples are presented to demonstrate the efficiency and effectiveness of this proposed approach.

Design of two-dimensional FIR digital filters for sampling structure conversion by genetic algorithm approach

Clinching is a convenient and efficient cold forming process that can join two sheets without any additional part. This study establishes an intelligent system for optimizing the clinched joint. Firstly, a mathematical model which introduces the ductile damage constraint to prevent cracking during clinching process is proposed. Meanwhile, an optimization methodology and its corresponding computer program are developed by integrated finite element model (FEM) and genetic algorithm (GA) approach. Secondly, Al6061-T4 alloy sheets with a thickness of 1.4mm are used to verify this optimization system. The optimization program automatically acquires the largest axial strength which is approximately equal to 872N. Finally, sensitivity analysis is implemented, in which the influence of geometrical parameters of clinching tools on final joint strength is analyzed. The sensitivity analysis indicates the main parameters to influence joint strength, which is essential from an industrial point of view.

Multiple sourcing under supplier failure risk and quantity discount: A genetic algorithm approach

Sonophotocatalytic treatment of AB113 dye and real textile wastewater using ZnO/persulfate: Modeling by response surface methodology and artificial neural network

With rapid development of technology, interconnect will occupy much chip area and gradually dominate circuit performance, interconnect optimization in high level design become more and more important. In this paper, we formulate an interconnect reuse based high level synthesis algorithm with GA (Genetic Algorithm) approach. The main contribution is that we put forward a novel coding method and design corresponding genetic operator for avoidance of generation of infeasible solutions. Experiments will show the efficiency of the algorithm.

This paper deals with a robust track-following system design problem with multiple constraints using genetic algorithm and LMI approach. A robust H ∞ constraint is introduced to attenuate vibration disturbance within error limits and a robust loop gain constraint is considered to minimize the variations of the tracking loop characteristics against actuator uncertainties. The robust H ∞ constraint can be expressed by a matrix inequality and the robust loop gain constraint can be treated by an objective function of a genetic algorithm. Accordingly, a robust tracking controller can be obtained by integrating a genetic algorithm and LMI approach. The proposed track-following system design method is applied to the track-following system of a DVD recording drive and is evaluated through experimental results.

Investment is one of the economic growth factors of countries, especially in Indonesia. Stocks is a form of investment, which is liquid. In determining the stock investment decisions which need to be considered by investors is to choose stocks that can generate maximum returns with a minimum risk level. Therefore, we need to know how to allocate the capital which may give the optimal benefit. This study discusses the issue of stock investment based on CAPM which is estimated using covariance and Genetic Algorithm approach. It is assumed that the stocks analyzed follow the CAPM model. To do the estimation of beta parameter on CAPM equation is done by two approach, first is to be represented by covariance approach, and second with genetic algorithm optimization. As a numerical illustration, in this paper analyzed ten stocks traded on the capital market in Indonesia. The results of the analysis show that estimation of beta parameters using covariance and genetic algorithm approach, give the same decision, that is, six underpriced stocks with buying decision, and four overpriced stocks with a sales decision. Based on the analysis, it can be concluded that the results can be used as a consideration for investors buying six under-priced stocks, and selling four overpriced stocks.

This paper proposes an innovative genetic algorithm (GA) approach to solve the thermal unit commitment (UC) problem in power generation industry through a constraint satisfaction technique. Due to a large variety of constraints to be satisfied, the solution space of the UC problem is highly nonconvex, and therefore the UC problem can not be solved efficiently by the standard GA. To effectively deal with the constraints of the problem and greatly reduce the search space of the GA, the minimum up- and down-time constraints are embedded in the binary strings that are coded to represent the on-off states of the generating units. The violations of the other constraints are handled by integrating penalty factors into the cost function. Numerical results on the practical Taiwan Power (Taipower) system of 38 thermal units over a 24-hour period show that the features of easy implementation, fast convergence, and highly near-optimal solution in solving the UC problem can be achieved by the proposed GA approach. >

Variations in Earth's orbit and axial tilt induce climatic changes on its surface, which are recorded in sedimentary deposits. These cyclical variations are driven by the main secular frequencies of the solar system. Analyzing these cycles in sedimentary records can help establish an astronomical time scale for the geological record by correlating geological proxies with computed variations in insolation on the Earth's surface, in accordance with the laws of celestial mechanics. A critical aspect of this analysis is the estimation of the sedimentary deposition rate, which determines the time-depth transfer function, relating geological depth to relative or absolute time.We propose a novel approach for constructing astronomical time scales for geological stratigraphic records. The AstroGeoFit method establishes a time-depth transfer function throughout the record, accommodating variable sedimentation rates, and extracts the primary astronomical signal from the geological sequence. This is achieved using a genetic algorithm that adapts to a wide range of sedimentation rate variations. This statistical analysis enables the reconstruction of an astronomical signal (e.g., eccentricity and/or precession) purely from the stratigraphic sequence with minimal personal bias. When this template is correlated with an astronomical solution, an absolute time scale is obtained for the entire record. In addition, we show that quantitative uncertainties can be estimated at each stage of the AstroGeoFit process with the Bayesian approach.The publication of the AstroGeoFit approach will be associated to an open source python package that fully implement the AstroGeoFit algorithm.Ref: J. Laskar, N. Hoang, N. Hara, Y. Wu, A.Sultanov, M. Sinnesael, T. Westerhold, P. Bujons, AstroGeoFit. A Genetic Algorithm and Bayesian approach for  the Astronomical Calibration of the Geological Timescale, 2025, in revision. 

In this work, the genetic algorithm (GA) and input estimation approach (IE) are proposed to design a sliding mode controller (SMC) that hold ability of disturbance torque estimation and the robust control performance. The IE approach is an on-line recursive inverse estimation method based on the Kalman filter (KF) and recursive least square estimator method (RLSE), which estimates the disturbance torque without additional torque sensor. The sliding mode control theory has the characteristics of low sensitivity with variable system parameters. Furthermore, the genetic algorithm is proposed to search the optimal controller design parameters for SMC that it can promote the control performance.