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This paper proposed particle swarm optimizations of proportional-integral-derivative (PID) gains used in the disturbance observer based control of Proton Exchange Membrane Fuel Cell (PEMFC) generation system. The proposed approach is easy to implement and have good computational efficiency. In order to get satisfactory dynamic performance, a new performance index is introduced. Comparisons between...
A military decision maker is typically confronted by the task of determining optimal course of action under some constraints in a complex uncertain situation. Thus, a new class of Combinational Constraint Optimization Problem (CCOP) is formalized. The object of CCOP is modeled by Influence net, and the constraints of CCOP relate to resource and collaboration. CCOP comprising Pseudo-Boolean and Boolean...
An unsuitable representation will make the task of mining class association rules very hard for a traditional genetic algorithm (GA). But for a given dataset, it is difficult to decide which one is the best representation used in the mining progress. In this paper, we analyses the effects of different representations for a traditional GA and proposed a growing evolutionary algorithm which was robust...
In view of the problem in the practical application of radial basis function (RBF) network, such as the number of nodes in the hidden layer and the parameters (w, c, and σ) are hard to determine, an improved particle swarm optimization (PSO) algorithm which makes use of the advantages of PSO algorithm and genetic algorithm (GA) is proposed, and then optimize the RBF network model with the new algorithm...
Support Vector Machines (SVM), one of the new techniques for text classification, have been widely used in many application areas. SVM try to find an optimal hyperplane within the input space so as to correctly classify the binary classification problem. We present a novel heuristic text classification approach based on genetic algorithm (GA) and SVM. Simulation results demonstrate that GA and SVM...
The software of auto-tuning parameters of PID control is developed for industrial controller. The output response curve of control system is recognized, and the scopes of the controller parameters are given to meet expected control index by fuzzy tuning rules based on pattern recognition. The PID parameters are optimized by means of genetic algorithm based on the optimization function of over-shoot,...
Computer models are widely used to simulate real processes. Within the computer model, there always exist some parameters which are unobservable in the real process but need to be specified in the model. The procedure to adjust these unknown parameters in order to fit the model to observed data and improve its predictive capability is known as calibration. In this paper, we propose an effective and...
We present an application of a genetic algorithm (GA) based method to the design of hide-unloading job, an asymmetric lifting task in shoe industry of Agra, India. In India, which has the second largest shoe industry in the world, it is labor intensive and concentrated in the small and cottage industry sector with Agra being a major production hub. Due to awkward postures and high load handling in...
Multiple Attribute Decision Making (MADM) is an important part of decision science which helps us to select a preferred alternative among many alternatives which are compared with conflicting criteria. So, many solution approaches have been introduced such as permutation method; Interactive Simple Additive Weighting Method (ISAW) an etc. The time of the solution is sensitive to the size of the problem...
Queuing research and its applications have been studied extensively by concentrating mainly on design, performance and running of the service facility under study. In this paper we show how a simple behavioral queuing system can be modeled using a Cellular Automata; and then we show how a Genetic Algorithm can be used to optimize the behavioral properties of this agent based model.
This paper examines an integrated model to determine strategic capacitated facility locations based on the view of distribution center management in the upstream supply chain. The model incorporates the sub-problems of service flag allocation and vehicle dispatching allocation. This paper proposes a heuristic nested genetic algorithm which minimizes the total cost while incorporating consideration...
In order to overcome the shortcomings of the nonlinear, time-delay and time-varying problems in the sewage treatment system based on biological fluidized bed (BFB), a modified T-S model fuzzy adaptive control system based on genetic algorithm (GA) is proposed. In the system, firstly using GA to optimize the membership functions, then reducing the dimension of fuzzy controller and simplifying the rules...
A matrix-coded genetic algorithm is proposed for solving the flexible job shop scheduling problem (FJSP). Novel strategies have been incorporated into crossover operator and mutation operator to assist the matrix-coded genetic algorithm to perform well. The matrix-coded genetic algorithm is a direct-viewing and easy to operate, a set of benchmark taken from the literature are tested. The computation...
Following the construction completion of the new infrastructures of sewerage, sewerage rehabilitation planning is the major work. This paper presents a systematic sewerage rehabilitation planning consisting of sewer inspection, diagnosis of pipe defects, grading of sewerage structural conditions, and determination of cost-effectiveness rehabilitation methods and substitution pipe materials. The main...
Bi-level linear programming is a technique for modeling decentralized decision. It consists of the upper level and lower level objectives. Thus, this paper intends to apply bi-level linear programming to supply chain management and develops an efficient method based on hybrid of genetic algorithm and particle swarm optimization. The performance of the proposed method is ascertained by comparing the...
In this paper, stochastic version of p-hub covering center problem (we call it Sp-HCCP) has been presented that optimizes the location of the hubs and allocation of non-hub nodes to hub nodes. The goal of our model is to maximize the minimum service-level that can achieved for a given maximum path length (delivery time on the path). We have formulated this problem using the chance constraints with...
The work involves developing a simulation model and a heuristic for a distribution problem with fixed charge in a two stage supply chain. The work is extended to include an integrated supply chain taking into account both the capacity constrained production environment and two stage transportation distribution. In the first phase the paper aims at optimizing the transportation distribution problem...
We discuss a scheduling problem for a two-machine robotic flow-shop with a bounded intermediate station and robots which is realistic in FMCs (flexible manufacturing cells). The problem asks to minimize the total weighted completion time. It is NP-hard. In this paper, we propose a heuristic algorithm based on GA (Genetic Algorithm) which is applicable to the problem, and which allows not only permutation,...
Function optimization is always being one of the important problems of scientific field. Over the past a few decades, many artificial intelligent optimizing algorithms have been invented, such as genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and so on. Artificial fish school algorithm (AFSA) is a novel optimizing method. In this paper, AFSA was applied to...
The rendezvous of an orbiting vehicle with a target body in another orbit can be accomplished in a specified time by two or more impulses. Aiming to solve the minimum fuel, multiple-impulse and time-fixed rendezvous problem, the genetic algorithm method is used in this paper. The genetic algorithm is a direct method for solving this constrained optimization problem that is based on natural selection,...
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