Modifications of Particle Swarm Optimization Techniques and Its Application on Stock Market: A Survey <s> INTRODUCTION <s> A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described. <s> BIB001 </s> Modifications of Particle Swarm Optimization Techniques and Its Application on Stock Market: A Survey <s> INTRODUCTION <s> Abstract In this paper, a new method named MSSE-PSO (master–slave swarms shuffling evolution algorithm based on particle swarm optimization) is proposed. Firstly, a population of points is sampled randomly from the feasible space, and then partitioned into several sub-swarms (one master swarm and other slave swarms). Each slave swarm independently executes PSO or its variants, including the update of particles’ position and velocity. For the master swarm, the particles enhance themselves based on the social knowledge of master swarm and that of slave swarms. At periodic stage in the evolution, the master swarm and the whole slave swarms are forced to mix, and points are then reassigned to several sub-swarms to ensure the share of information. The process is repeated until a user-defined stopping criterion is reached. The tests of numerical simulation and the case study on hydrological model show that MSSE-PSO remarkably improves the accuracy of calibration, reduces the time of computation and enhances the performance of stability. Therefore, it is an effective and efficient global optimization method. <s> BIB002
Computational Intelligence (CI) is the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex and changing environments. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Biological Swarm Systems that have inspired computational models include ants, bees, spiders, and bird flocks BIB001 The authors in]. The objective of computational swarm intelligence models is to modeling the simple behaviors of individuals, and the local interactions with the environment and neighboring individuals, in order to obtain more variant behaviors that can be used to solve complex problems, mostly optimization problems. Swarm intelligence (SI) originated from the study of colonies, or swarms of social organisms. Studies of the social behavior of organisms (individuals) in swarms prompted the design of very efficient optimization and clustering algorithms. For example, simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the Particle Swarm Optimization (PSO) algorithm BIB002 . 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. The usage of PSO in stock market and Portfolio Selection is very common today, whereas, the average person's interest in the stock market has grown exponentially. This demand coupled with advances in trading technology has opened up the markets, so that nowadays anybody can own stocks, and use many types of software to perform the aspired profit with minimum risk. Consequently, a lot of attention has been devoted to the analysis and prediction of future values and trends of the financial markets, and due to large applications in different business transactions, stock market prediction has become a hot topic of research. Nowadays, more software such as PSO are used to guide person to manage his portfolio and get successful investment, this motivates researchers to develop these software to give more accuracy and efficiency for successful portfolio management. In this paper we survey the state of the art of the various modifications of the PSO and study whether had been applied over the stock market or not. The rest of the paper is organized as follows. Section 2 gives historical study of particle swarm optimization. Section 3 briefly reviews the stock market. In section 4, the basic particle swarm optimization is presented. Section 5 gives the Variations of Particle Swarm Optimization and explains how it can modified PSO and the terms which we can modify it. Also, it gives the different forms for the Modifications of the original PSO. Finally, in Section 6 we conclusion this paper by the summary of main points.