Interconnect reuse based resource allocation with GA approach

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

Last-mile delivery is an important part of a logistics activity in the city. Usually, delivery workers are required to manually unload goods at customer locations. These manual tasks induce physiological fatigue in the workers and increase delivery time. This paper discusses a genetic algorithm (GA) approach to the open vehicle routing problem with split delivery (SDOVRP), where manual unloading is addressed. The workers are pre-assigned to vehicle and split delivery is allowed. Its objective is to minimize the total cost of total fixed cost of vehicles and delivery workers and total transportation cost. For safety, the total physical workload imposed on each worker must not exceed the daily limit. Since an optimization approach cannot find the optimal solution within reasonable computation time especially when solving large size problem. A GA with heuristic for pre-determine split delivery is developed to solve the problem. The computational experiment results show that the GA approach is efficient and can obtain near-optimal SDOVRP solutions.

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

Shipyards have large departments or facilities. It is essential to make an effective topological layout plan since the initial investment cost of these departments is high. Topological layout is an optimization problem and Genetic Algorithm (GA) is generally used in the literature. The selection of effective genetic algorithm approaches and operators are very important to improve the performance of the optimization. This study investigates an effective solution to the shipyard topological layout using a Quadratic Assignment Problem (QAP) model with classic and elitist GA approaches. Besides, genetic operators that have significant effects on exploitation and exploration capabilities are analyzed. Therefore, 126 experiments were run with 13 different operators. The results obtained from the classic and elitist GA approach were evaluated individually and compared with each other. It was observed that the elitist GA approach has a superior performance compared to the classic GA approach. This study is the most comprehensive and practical study on the performance of the GA for topological layout of the shipyard in the literature.

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.

Wireless sensor network (WSN) has key constraint on energy of sensor nodes i.e. each nodes have finite amount of energy. Seeing energy as key constraint WSN must has robust energy optimized routing algorithm. Mostly routing approach use shortest path and minimal energy depletion model. Network operate on these routing scheme early partitioned due to unbalanced energy consumption of sensor nodes i.e. a single shortest route may used most of the time leads to faster consumption of energy of sensor nodes coming in route path. In current paper attempts has made to develop energy efficient routing scheme based on optimization techniques "particle swarm optimization (PSO) and genetic algorithm (GA)". We implemented all four routing scheme i.e. shortest path approach, PSO approach, GA approach and hybrid PSO-GA approach for small size as well as large size sensor network. Simulation results depicts hybrid PSO-GA routing scheme raised 12% to 23%, 8% to 15% and 5% to 13% lifetime compared to shortest path approach, PSO approach and GA approach respectively for large scale network. For large scale network hybrid PSO-GA raised 9% to 16%, 6% to 11% and 5% to 9% packet delivery ratio compared to shortest path approach, PSO approach and GA approach respectively.

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.

Walking robot dynamic stabilization is a growing field of research as such robots are being constructed for various applications in human environment. The dynamic stabilization systems for such robots are often highly non-linear with the additional demand for rapid change of model mass parameters to meet the design requirements. The article centres round the problem of mechanical modelling of a walking robot and identifying the robot dynamic stabilization system parameters using the genetic algorithm approach. The stabilization system supposed in the paper is based on inverted pendulum model with paired gyroscopic units with limited kinetic momenta, that shapes out the optimization problem. The proposed control system includes a cascade of two PID regulators, the optimal parameters of which are identified using the genetic algorithm approach. The authors give the short description of the approach and suggest the fitness function suitable for this application. The computational experiment is then conducted, and the optimal parameters set is obtained. The disadvantages of the genetic algorithm solver in Simulink environment are pointed out with the approaches to their partial elimination.