ZASTOSOWANIE ROZMYTEJ MAPY KOGNITYWNEJ W PROGNOZOWANIU EFEKTYWNOŚCI PRACY WYPOŻYCZALNI ROWEROWYCH

This paper proposes application of fuzzy cognitive map with evolutionary learning algorithms to model a system for prediction of effectiveness of bike sharing systems. Fuzzy cognitive map was constructed based on historical data and next used to forecast the number of cyclists and customers of bike sharing systems on three consecutive days. The learning process was realized with the use of Individually Directional Evolutionary Algorithm IDEA and Real-Coded Genetic Algorithm RCGA. Simulation analysis of the system for prediction of effectiveness of bike sharing systems was carried out with the use of software developed in JAVA.W pracy zaproponowano zastosowanie rozmytej mapy kognitywnej wraz z ewolucyjnymi algorytmami uczenia do modelowania systemu prognozowania efektywności pracy wypożyczalni rowerowych. Na podstawie danych historycznych zbudowano rozmytą mapę kognitywną, którą następnie zastosowano do prognozowania liczby rowerzystów i klientów wypożyczalni w trzech kolejnych dniach. Proces uczenia zrealizowano z zastosowaniem indywidualnego kierunkowego algorytmu ewolucyjnego IDEA oraz algorytmu genetycznego z kodowaniem zmiennoprzecinkowym RCGA. Analizę symulacyjną systemu prognozowania efektywności pracy wypożyczalni rowerowych przeprowadzono przy pomocy oprogramowania opracowanego w technologii JAVA

Application of an Improved Genetic Algorithm for Optimal Design of Planar Steel Frames

Genetic Algorithm (GA) is one of the most widely used optimization algorithms. This algorithm consists of five stages, namely population generation, crossover, mutation, evaluation, and selection. This study presents a modified version of GA called Improved Genetic Algorithm (IGA) for the optimization of steel frame designs. In the IGA, the rate of convergence to the optimal solution is increased by splitting the population generation process to two stages. In the first stage, the initial population is generated by random selection of members from among AISC W-shapes. The generated population is then evaluated in another stage, where the member that does not satisfy the design constraints are replaced with stronger members with larger cross sectional area. This process continues until all design constraints are satisfied. Through this process, the initial population will be improved intelligently so that the design constraints fall within the allowed range. For performance evaluation and comparison, the method was used to design and optimize 10-story and 24-story frames based on the LRFD method as per AISC regulations with the finite element method used for frame analysis. Structural analysis, design, and optimization were performed using a program written with MATLAB programming language. The results show that using the proposed method (IGA) for frame optimization reduces the volume of computations and increases the rate of convergence, thus allowing access to frame designs with near-optimal weights in only a few iterations. Using the IGA also limits the search space to the area of acceptable solutions

Parameter Optimization of Genetic Algorithm Utilizing Taguchi Design for Gliding Trajectory Optimization of Missile

The present study aims to establish a genetic algorithm (GA) method to optimize gliding trajectory of a missile. The trajectory is optimized by discretizing the angle of attack (AOA) and solving optimal control problem to achieve maximum gliding range. GA is employed to resolve the optimal control problem to achieve optimized AOA. A Taguchi’s design of experiments was proposed contrary to full factorial method to ascertain the GA parameters. The experiments have been designed as per Taguchi’s design of experiments using L27 orthogonal array. Systematic reasoning ability of Taguchi method is exploited to obtain better selection, crossover and mutation operations and consequently, enhance the performance of GA for gliding trajectory optimization. The effects of GA parameters on gliding trajectory optimization are studied and analysis of variance (ANOVA) is carried out to evaluate significance factors on the results. Crossover function and population size are observed as highly impacting parameter in missile trajectory optimization accompanied by selection method, crossover fraction, mutation rate and number of generations. Artificial neural network (ANN) method was also applied to predict the significance of GA parameters. The results show that the gliding range is maximized after GA parameter tuning. Simulation results also portrayed that with optimal AOA, gliding distance of missile is improved compared to earlier one. The numerical simulation shows the efficiency of proposed procedure via various test scenarios

Metaheuristics applied to the optimization of continuous functions

Optimization is a field of mathematics which studies and develops mathematical methods with the aim of optimizing a wide range of problems. In physics these methods are central. Essentially all the dynamical equations in physics can be expressed as a series of optimization problems in terms of action integrals. Optimization can better be explained as finding the optima, also known as extremes, of a mathematical object. Such object may be a continuous function, as the case of this thesis. The approaches for solving optimization problems are generally divided into two categories, deterministic optimization and stochastic optimization. The main difference is that the deterministic approach applies calculus and the stochastic approach applies a search technique. For solving complex optimization problems, the stochastic approach has long proven to be most efficient. This thesis focuses on improving the two stochastic search methods: Simulated Annealing and the Genetic Algorithm. This is performed by implementing two newly developed methods. The first method is the Tangent-based Evaluation method, which is better suited to detect abnormalities in continuous functions than the common one-point evaluation method. The other method is the Analytic Swap method for generation of solutions. Solution generation is an important part of any stochastic algorithm. Usually the new solutions generated by a random function, but the Analytic Swap method combines randomness with analytics to generate better solutions

Distributed Generation Allocation For Power Loss Minimization And Voltage Improvement Of Radial Distribution Systems Using Genetic Algorithm

Numerous advantages attained by integrating Distributed Generation (DG) in distribution systems. These advantages include decreasing power losses and improving voltage profiles. Such benefits can be achieved and enhanced if DGs are optimally sized and located in the systems. This theses presents a distribution generation (DG) allocation strategy to improve node voltage and power loss of radial distribution systems using genetic algorithm (GA). The objective is to minimize active power losses while keep the voltage profiles in the network within specified limit. This approach finds optimal DG active power and optimal OLTC position for tap changing transformer. Also uncertainty in load and generation are considered. Thus, in this work, the load demand at each node and the DG power generation at candidate nodes are considered as a possibilistic variable represented by two different triangular fuzzy number. The simulation results shows that reduction of power loss in distribution system is possible and all node voltages variation can be achieved within the required limit if DG are optimally placed in the system. Induction DG placement into the distribution system also give a better performance from capacitor bank placement. In modern load growth scenario uncertainty load and generation model shows that reduction of power loss in distribution system is possible and all node voltages variation can be achieved within the required limit without violating the thermal limit of the system