Chaotic Time Series with Function Expression Method Based on an Improved Genetic-Simulated Annealing Algorithm

The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy

Optimization of Trading Physics Models of Markets

We describe an end-to-end real-time S&P futures trading system. Inner-shell stochastic nonlinear dynamic models are developed, and Canonical Momenta Indicators (CMI) are derived from a fitted Lagrangian used by outer-shell trading models dependent on these indicators. Recursive and adaptive optimization using Adaptive Simulated Annealing (ASA) is used for fitting parameters shared across these shells of dynamic and trading models

Learning FCMs with multi-local and balanced memetic algorithms for forecasting industrial drying processes

In this paper, we propose a Fuzzy Cognitive Map (FCM) learning approach with a multi-local search in balanced memetic algorithms for forecasting industrial drying processes. The first contribution of this paper is to propose a FCM model by an Evolutionary Algorithm (EA), but the resulted FCM model is improved by a multi-local and balanced local search algorithm. Memetic algorithms can be tuned with different local search strategies (CMA-ES, SW, SSW and Simplex) and the balance of the effort between global and local search. To do this, we applied the proposed approach to the forecasting of moisture loss in industrial drying process. The thermal drying process is a relevant one used in many industrial processes such as food industry, biofuels production, detergents and dyes in powder production, pharmaceutical industry, reprography applications, textile industries, and others. This research also shows that exploration of the search space is more relevant than finding local optima in the FCM models tested

Multi-layer feed forward neural networks for foreign exchange time series forecasting

This dissertation examines the forecasting performance of multi-layer feed forward neural networks in modeling five weekly foreign exchange rates: Australian Dollars/U.S. Dollars (AUS/USD), Euro/U.S. Dollar (EUR/USD), Swiss Franc/U.S. Dollar (CHF/USD), British Pound sterling/U.S. Dollars (GBP/USD), and Japanese Yen/U.S. Dollars (JPY/USD). There are five objectives to accomplish. The first is to determine the key modeling factors that should be considered in topology determination. The second is to compare the performances of Genetic Algorithm (GA) and Modified Tabu Search (TS) in choosing the topology for Neural Networks (NN) implementation. The third is to investigate the suitable learning algorithm for NNs for time series forecasting by comparing Back Propagation (BP) with GAs and TS. The fourth is to conduct computational studies for multi-step ahead forecasting for GBP/USD and EUR/USD, as well as to study other accuracy forecasting issues. The last is to study the implementation of multivariate time series forecasting using NNs.;The results of the experiments performed indicate that one should examine the correct topology, especially the three most important factors (number of input nodes, hidden nodes, learning rate) prior to using NNs for time series forecasting.;The comparison performance of topology suggested using GA, TS, and benchmark led to the conclusion that neither GA nor TS is guaranteed to provide better results, especially in terms of percentage of true directional changes (DIR). However, if there is no prior knowledge of the problem, GA searches for topology determination are favored and provide reasonably good performances. GA is also preferred for NN training. Compared to BP, GA guarantees better performance in terms of Mean of Absolute Percentage Error (MAPE) and, most of the time, performs better in terms of Mean of Square Error (MSE).;Caution should be taken in adopting the results, since the study of time periods indicated that the best topology for forecasting a specific foreign exchange is data specific ; hence the best for a certain period is not always the best to forecast other periods. However, the chosen topology is reasonably useful for up to three steps ahead forecasting.;The trivariate time series, which incorporate interest rates of the two countries involved, did improve the results. Multivariate time series forecasts for monthly JPY/USD, as well as for monthly EUR/USD, produced a higher level of success than the one achieved in the previous experiment. The NNs were programmed using MATLABRTM