Particle Swarm Optimization (PSO), in its present form, has been in existence for roughly a decade, with formative research in related domains (such as social modelling, computer graphics, simulation and animation of natural swarms or flocks) for some years before that; a relatively short time compared with some of the other natural computing paradigms such as artificial neural networks and evolutionary computation. However, in that short period, PSO has gained widespread appeal amongst researchers and has been shown to offer good performance in a variety of application domains, with potential for hybridisation and specialisation, and demonstration of some interesting emergent behaviour. This paper aims to offer a compendious and timely review of the field and the challenges and opportunities offered by this welcome addition to the optimization toolbox. Part I discusses the location of PSO within the broader domain of natural computing, considers the development of the algorithm, and refinements introduced to prevent swarm stagnation and tackle dynamic environments. Part II considers current research in hybridisation, combinatorial problems, multicriteria and constrained optimization, and a range of indicative application areas.

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A review of particle swarm optimization. Part I: background and development

In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). CSO is generated by observing the behaviors of cats, and composed of two sub-models, i.e., tracing mode and seeking mode, which model upon the behaviors of cats. Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).

Cat swarm optimization

Particle swarm optimization-based algorithms for TSP and generalized TSP

Enhancing particle swarm optimization using generalized opposition-based learning

Particle swarm optimization (PSO) is an alternative population-based evolutionary computation technique. It has been shown to be capable of optimizing hard mathematical problems in continuous or binary space. We present here a parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data. The first strategy is designed for solution parameters that are independent or are only loosely correlated, such as the Rosenbrock and Rastrigrin functions. The second communication strategy can be applied to parameters that are more strongly correlated such as the Griewank function. In cases where the properties of the parameters are unknown, a third hybrid communication strategy can be used. Experimental results demonstrate the usefulness of the proposed PPSO algorithm.

A Parallel Particle Swarm Optimization Algorithm with Communication Strategies

Investment portfolio construction is always a popular issue for the investors. In this paper, we utilize Investment Satisfied Capability Index ISCI to sieve out the potential candidate stocks and then use Evolved Bat Algorithm EBA to construct the portfolio for stock investment. Three years historical daily Return on Investment ROI data from 2008 to 2010 is included in the experiment in order to test and verify the performance of the constructed stock portfolio by EBA. The experimental result is compared with the investment results from Taiwan broader market and Taiwan Top 50 Tracker Fund of Taiwan Yuanta Securities. The experimental result indicates that our proposed approach provides an efficient portfolio for the stock investment.

Stock Portfolio Construction Using Evolved Bat Algorithm

Foreign exchange rate forecasting catches many researchers interests in recent years. Problems of the foreign exchange rate forecasting model selection and the improvement on forecasting accuracy are not easy to be solved. In this paper, the forecasting results obtained by conventional time-series models and by the Inter-active Artificial Bee Colony (IABC), which is a young artificial intelligent meth-od, are compared with each other with 4 years historical data. The sliding win-dow strategy is used in the experiment for both the training and the testing phases. In our experiments, we use continuous previous three days data as the training set, and use the training result to forecast the foreign exchange rate on the fourth day. In addition, we evaluate the forecasting accuracy with three criteria, namely, Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The experimental results indicate that feeding macroeco-nomic factors to IABC as the input data is capable to produce higher accurate data in the foreign exchange rate than the conventional time-series models such as EGARCH.

The Comparison between IABC with EGARCH in Foreign Exchange Rate Forecasting

Structuring interactive artificial bee colony forecasting model in foreign exchange rate forecasting with consumer confidence index and conventional microeconomics factors

In this paper, the Consumer Confidence Index (CCI) is led in to be one of the considered factors in foreign exchange rate forecasting. Besides CCI, nine conventional factors, which are used in the existing foreign exchange rate forecasting methods, are also included in the considered factors. The classical time-series method, GARCH model, is employed and six-year historical data in 2006 to 2011 is used to produce the foreign exchange rate forecasting results. The experimental results indicate that including CCI as one of the considered factors for the foreign exchange rate forecasting with GARCH model improves parts of the foreign exchange rate forecasting accuracy than only including the conventional factors.

Jui-Fang Chang

Papers

A Parallel Particle Swarm Optimization Algorithm with Communication Strategies

Stock Portfolio Construction Using Evolved Bat Algorithm

The Comparison between IABC with EGARCH in Foreign Exchange Rate Forecasting

Structuring interactive artificial bee colony forecasting model in foreign exchange rate forecasting with consumer confidence index and conventional microeconomics factors

Using Consumer Confidence Index in the Foreign Exchange Rate Forecasting