Modifications of Particle Swarm Optimization Techniques and Its Application on Stock Market: A Survey
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paper_title: Particle swarm optimization

paper_content:

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.
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paper_title: Improved particle swarm algorithm for hydrological parameter optimization

paper_content:

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.
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paper_title: Multi-swarm particle swarm optimization based on mixed search behavior

paper_content:

The paper develops a Multi-swarm particle swarm optimization (MPSO) to overcome the premature convergence problem. MPSO takes advantage of multiple sub-swarms with mixed search behavior to maintain the swarm diversity, and introduces cooperative mechanism to prompt the information exchange among sub-swarms. Moreover, MPSO adopts an adaptive reinitializing strategy guided by swarm diversity, which can contribute to the global convergence of the algorithm. Through the mixed local search behavior modes, the cooperative search and the reinitializing strategy guided by swarm diversity, MPSO can maintain appropriate diversity and keep the balance of local search and global search validly. The proposed MPSO was applied to some well-known benchmarks. The experimental results show MPSO is a robust global optimization technique for the complex multimodal functions.
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paper_title: Multi- Swarm and Multi- Best particle swarm optimization algorithm

paper_content:

This paper proposes a novel particle swarm optimization algorithm: Multi-Swarm and Multi-Best particle swarm optimization algorithm. The novel algorithm divides initialized particles into several populations randomly. After calculating certain generations respectively, every population is combined into one population and continues to calculate until the stop condition is satisfied. At the same time, the novel algorithm updates particlespsila velocities and positions by following multi-gbest and multi-pbest instead of single gbest and single pbest. The novel algorithm is not only a generalization of the basic particle swarm optimization, but can improve the searching efficiency, help the algorithm fly out of local optimum and increase the possibility of finding the real global best solution greatly. Finally one example is simulated to show the novel algorithmpsilas superiority.
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paper_title: A Modified Particle Swarm Optimization Algorithm for Reliability Redundancy Optimization Problem

paper_content:

In this paper, a modified particle swarm optimization (MPSO) algorithm is proposed to solve the reliability redundancy optimization problem. This algorithm modifies the strategy of generating new position of particles. For each generation solution, the flight velocity of particles is removed. Whereas the new position of each particle is generated by using difference strategy. Moreover, an adaptive parameter is used to ensure diversity of feasible solutions. Experimental results on four benchmark problems demonstrate that the proposed MPSO has better robustness, effectiveness and efficiency than other algorithms reported in literatures for solving the reliability redundancy optimization problem.
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paper_title: Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques

paper_content:

The present paper introduces the use of BFO and ABFO techniques to develop an efficient forecasting model for prediction of various stock indices. The structure used in these forecasting models is a simple linear combiner. The connecting weights of the adaptive linear combiner based models are optimized using ABFO and BFO by minimizing its mean square error (MSE). The short and long term prediction performance of these models are evaluated with test data and the results obtained are compared with those obtained from the genetic algorithm (GA) and particle swarm optimization (PSO) based models. It is in general observed that the new models are computationally more efficient, prediction wise more accurate and show faster convergence compared to other evolutionary computing models such as GA and PSO based models.
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paper_title: Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques

paper_content:

The present paper introduces the use of BFO and ABFO techniques to develop an efficient forecasting model for prediction of various stock indices. The structure used in these forecasting models is a simple linear combiner. The connecting weights of the adaptive linear combiner based models are optimized using ABFO and BFO by minimizing its mean square error (MSE). The short and long term prediction performance of these models are evaluated with test data and the results obtained are compared with those obtained from the genetic algorithm (GA) and particle swarm optimization (PSO) based models. It is in general observed that the new models are computationally more efficient, prediction wise more accurate and show faster convergence compared to other evolutionary computing models such as GA and PSO based models.
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paper_title: Multi- Swarm and Multi- Best particle swarm optimization algorithm

paper_content:

This paper proposes a novel particle swarm optimization algorithm: Multi-Swarm and Multi-Best particle swarm optimization algorithm. The novel algorithm divides initialized particles into several populations randomly. After calculating certain generations respectively, every population is combined into one population and continues to calculate until the stop condition is satisfied. At the same time, the novel algorithm updates particlespsila velocities and positions by following multi-gbest and multi-pbest instead of single gbest and single pbest. The novel algorithm is not only a generalization of the basic particle swarm optimization, but can improve the searching efficiency, help the algorithm fly out of local optimum and increase the possibility of finding the real global best solution greatly. Finally one example is simulated to show the novel algorithmpsilas superiority.
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paper_title: Parameter Selection in Particle Swarm Optimization

paper_content:

This paper first analyzes the impact that inertia weight and maximum velocity have on the performance of the particle swarm optimizer, and then provides guidelines for selecting these two parameters. Analysis of experiments demonstrates the validity of these guidelines.
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paper_title: Heterogeneous particle swarm optimization

paper_content:

Particles in the standard particle swarm optimization (PSO) algorithms, and most of its modifications, follow the same behaviours. That is, particles implement the same velocity and position update rules. This means that particles exhibit the same search characteristics. A heterogeneous PSO (HPSO) is proposed in this paper, where particles are allowed to follow different search behaviours selected from a behaviour pool, thereby efficiently addressing the exploration-exploitation trade-off problem. A preliminary empirical analysis is provided to show that much can be gained by using heterogeneous swarms.
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paper_title: A Modified Particle Swarm Optimization Algorithm for Reliability Redundancy Optimization Problem

paper_content:

In this paper, a modified particle swarm optimization (MPSO) algorithm is proposed to solve the reliability redundancy optimization problem. This algorithm modifies the strategy of generating new position of particles. For each generation solution, the flight velocity of particles is removed. Whereas the new position of each particle is generated by using difference strategy. Moreover, an adaptive parameter is used to ensure diversity of feasible solutions. Experimental results on four benchmark problems demonstrate that the proposed MPSO has better robustness, effectiveness and efficiency than other algorithms reported in literatures for solving the reliability redundancy optimization problem.
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paper_title: Generation and reserve dispatch in a competitive market using constrained particle swarm optimization

paper_content:

Competitive bidding for energy and ancillary services is increasingly recognized as an important part of electricity markets. In addition, the transmission capacity limits should be considered to optimize the total market cost. In this paper, a new approach based on constrained particle swarm optimization (CPSO) is developed to deal with the multi-product (energy and reserve) and multi-area electricity market dispatch problem. Constraint handling is based on particle ranking and uniform distribution. CPSO method offers a new solution for optimizing the total market cost in a multi-area competitive electricity market considering the system constraints. The proposed technique shows promising performance for smooth and non smooth cost function as well. Three different systems are examined to demonstrate the effectiveness and the accuracy of the proposed algorithm.
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paper_title: Center particle swarm optimization

paper_content:

Center particle swarm optimization algorithm (CenterPSO) is proposed where a center particle is incorporated into linearly decreasing weight particle swarm optimization (LDWPSO). Unlike other ordinary particles in LDWPSO, the center particle has no explicit velocity, and is set to the center of the swarm at every iteration. Other aspects of the center particle are the same as that of the ordinary particle, such as fitness evaluation and competition for the best particle of the swarm. Because the center of the swarm is a promising position, the center particle generally gets good fitness value. More importantly, due to frequent appearance as the best particle of swarm, it often attracts other particles and guides the search direction of the whole swarm. CenterPSO and LDWPSO are extensively compared on three well-known benchmark functions with 10, 20, 30 dimensions. Experimental results show that CenterPSO achieves not only better solutions but also faster convergence. Furthermore, CenterPSO and LDWPSO are compared as neural network training algorithms. The results show that CenterPSO achieves better performance than LDWPSO.
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paper_title: Center particle swarm optimization

paper_content:

Center particle swarm optimization algorithm (CenterPSO) is proposed where a center particle is incorporated into linearly decreasing weight particle swarm optimization (LDWPSO). Unlike other ordinary particles in LDWPSO, the center particle has no explicit velocity, and is set to the center of the swarm at every iteration. Other aspects of the center particle are the same as that of the ordinary particle, such as fitness evaluation and competition for the best particle of the swarm. Because the center of the swarm is a promising position, the center particle generally gets good fitness value. More importantly, due to frequent appearance as the best particle of swarm, it often attracts other particles and guides the search direction of the whole swarm. CenterPSO and LDWPSO are extensively compared on three well-known benchmark functions with 10, 20, 30 dimensions. Experimental results show that CenterPSO achieves not only better solutions but also faster convergence. Furthermore, CenterPSO and LDWPSO are compared as neural network training algorithms. The results show that CenterPSO achieves better performance than LDWPSO.
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paper_title: Predicting the Stock Market

paper_content:

This paper presents a tuturial introduction to predictions of stock time series. The various approaches of technical and fundamental analysis is presented and the prediction problem is formulated as a special case of inductive learning. The problems with performance evaluation of near-random-walk processes are illustrated with examples together with guidelines for avoiding the risk of data-snooping. The connections to concepts like "the bias/variance dilemma", overtraining and model complexity are further covered. Existing benchmarks and testing metrics are surveyed and some new measures are introduced.
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paper_title: Heterogeneous particle swarm optimization

paper_content:

Particles in the standard particle swarm optimization (PSO) algorithms, and most of its modifications, follow the same behaviours. That is, particles implement the same velocity and position update rules. This means that particles exhibit the same search characteristics. A heterogeneous PSO (HPSO) is proposed in this paper, where particles are allowed to follow different search behaviours selected from a behaviour pool, thereby efficiently addressing the exploration-exploitation trade-off problem. A preliminary empirical analysis is provided to show that much can be gained by using heterogeneous swarms.
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paper_title: Computational Intelligence PC Tools

paper_content:

Computational intelligence is an emerging field in computer science which combines fuzzy logic, neural networks, and genetic algorithms for a flexible yet powerful approach to scientific computing. Because computational intelligence combines three interrelated, mathematically-based tools, it has a wide variety of applications, from engineering and process control to experts systems. This book takes a hands-on, desktop-applications approach to the topic, featuring examples of specific real-world implementations and detailed case studies, with all pertinent code and software included on a floppy disk packaged with the book. Features: * Concise introduction to the concepts of fuzzy logic, neural networks, and genetic algorithms, and how they relate to one another within the context of computational intelligence. * Computational intellignece applications, including self-organizing feature maps, fuzzy calculator, evolutionary programming, and fuzzy neural networks. * Detailed case studies from engineering (F-16 flight system), systems control (mass transit scheduling), and medicine (appendicitis diagnosis). * Windows floppy disk with both source code and executable, self-contained programs for desktop implementation of all of the book's applications.
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paper_title: Particle swarm optimization

paper_content:

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.
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paper_title: A particle swarm optimizer with passive congregation.

paper_content:

This paper presents a particle swarm optimizer (PSO) with passive congregation to improve the performance of standard PSO (SPSO). Passive congregation is an important biological force preserving swarm integrity. By introducing passive congregation to PSO, information can be transferred among individuals of the swarm. A particle swarm optimizer with passive congregation (PSOPC) is tested with a set of 10 benchmark functions with 30 dimensions and compared to a global version of SPSO (GSPSO), a local version of SPSO (LSPSO), and PSO with a constriction factor (CPSO), respectively. Experimental results indicate that the PSO with passive congregation improves the search performance on the benchmark functions significantly.
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paper_title: A Guaranteed Global Convergence Particle Swarm Optimizer

paper_content:

The standard Particle Swarm Optimizer may prematurely converge on suboptimal solutions that are not even guaranteed to be local extrema. A new particle swarm optimizer, called stochastic PSO, which is guaranteed to convergence to the global optimization solution with probability one, is presented based on the analysis of the standard PSO. And the global convergence analysis is made using the F.Solis and R.Wets’ research results. Finally, several examples are simulated to show that SPSO is more efficient than the standard PSO.
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paper_title: An improved particle swarm optimization algorithm with disturbance term

paper_content:

The standard particle swarm optimization (PSO) algorithm, existing improvements and their influence to the performance of standard PSO are introduced. The framework of PSO basic formula is analyzed. Implied by its three-term structure, the inherent shortcoming that trends to local optima is indicated. Then a modified velocity updating formula of particle swarm optimization algorithm is declared. The addition of the disturbance term based on existing structure effectively mends the defects. The convergence of the improved algorithm is analyzed. Simulation results demonstrated that the improved algorithm have a better performance than the standard one.
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paper_title: A modified particle swarm optimization algorithm and its application to the multiobjective FET modeling problem

paper_content:

This paper introduces a modified particle swarm algorithm to handle multiobjective optimization problems. In multiobjective PSO algorithms, the determination of Pareto optimal solutions depends directly on the strategy of assigning a best local guide to each particle. In this work, the PSO algorithm is modified to assign a best local guide to each particle by using minimum angular distance information. This algorithm is implemented to determine field-effect transistor (FET) model elements subject to the Pareto domination between the scattering parameters and operation bandwidth. Furthermore, the results are compared with those obtained by the nondominated sorting genetic algorithm-II. FET models are also built for the 3 points sampled from the different locations of the Pareto front, and a discussion is presented for the Pareto relation between the scattering parameter performances and the operation bandwidth for each model.
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paper_title: An improved particle swarm optimization algorithm with disturbance term

paper_content:

The standard particle swarm optimization (PSO) algorithm, existing improvements and their influence to the performance of standard PSO are introduced. The framework of PSO basic formula is analyzed. Implied by its three-term structure, the inherent shortcoming that trends to local optima is indicated. Then a modified velocity updating formula of particle swarm optimization algorithm is declared. The addition of the disturbance term based on existing structure effectively mends the defects. The convergence of the improved algorithm is analyzed. Simulation results demonstrated that the improved algorithm have a better performance than the standard one.
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paper_title: A fusion model of HMM, ANN and GA for stock market forecasting

paper_content:

In this paper we propose and implement a fusion model by combining the Hidden Markov Model (HMM), Artificial Neural Networks (ANN) and Genetic Algorithms (GA) to forecast financial market behaviour. The developed tool can be used for in depth analysis of the stock market. Using ANN, the daily stock prices are transformed to independent sets of values that become input to HMM. We draw on GA to optimize the initial parameters of HMM. The trained HMM is used to identify and locate similar patterns in the historical data. The price differences between the matched days and the respective next day are calculated. Finally, a weighted average of the price differences of similar patterns is obtained to prepare a forecast for the required next day. Forecasts are obtained for a number of securities in the IT sector and are compared with a conventional forecast method.
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paper_title: A modified particle swarm optimizer with dynamic adaptation

paper_content:

This paper proposes a modified particle swarm optimization algorithm with dynamic adaptation. In this algorithm, a modified velocity updating formula of the particle is used, where the randomness in the course of updating particle velocity is relatively decreased and the inertia weight of each particle is different. Moreover, this algorithm introduces two parameter describing the evolving state of the algorithm, the evolution speed factor and aggregation degree factor. By analyzing the influence of two parameters on the PSO search ability, a new strategy is presented that the inertia weight dynamically changes based on the run and evolution state. In the strategy the inertia weight is given by a function of evolution speed factor and aggregation degree factor, and the value of inertia weight is dynamically adjusted according to the evolution speed and aggregation degree. The feature of the proposed algorithm is analyzed and several testing functions are performed in simulation study. Experimental results show that, the proposed algorithm remarkably improves the ability of PSO to jump out of the local optima and significantly enhance the convergence precision.
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paper_title: AN IMPROVED FUZZY PARTICLE SWARM OPTIMIZATION FOR NUMERICAL OPTIMIZATION

paper_content:

Fuzzy particle swarm optimization (FPSO) is a new variant of particle swarm optimization (PSO). Compared to PSO, each particle in FPSO is attracted by its previous best particle and other particles (not the global best particle) selected by a fuzzy mechanis- m. Although FPSO effectively slows down the attraction of the previous best particle and the global best particle, it shows slow convergence rate when solving complex optimization problems. To enhance the performance of FPSO, this paper proposes an improved FPSO algorithm (IFPSO) which employs two strategies including generalized opposition-based learning (GOBL) and Levy mutation. In order to verify the performance of our approach, thirteen well-known benchmark functions and a real-world optimization problem are used in the experiments. Simulation results show that our approach can significantly improve the performance of FPSO and outperforms several other state-of-the-art PSO algorithms.
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paper_title: Issues and Challenges of Intelligent Systems and Computational Intelligence

paper_content:

This carefully edited book contains contributions of prominent and active researchers and scholars in the broadly perceived area of intelligent systems. The book is unique both with respect to the width of coverage of tools and techniques, and to the variety of problems that could be solved by the tools and techniques presented. The editors have been able to gather a very good collection of relevant and original papers by prominent representatives of many areas, relevant both to the theory and practice of intelligent systems, artificial intelligence, computational intelligence, soft computing, and the like. The contributions have been divided into 7 parts presenting first more fundamental and theoretical contributions, and then applications in relevant areas.
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paper_title: Particle swarm optimization

paper_content:

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.
---
paper_title: A Guaranteed Global Convergence Particle Swarm Optimizer

paper_content:

The standard Particle Swarm Optimizer may prematurely converge on suboptimal solutions that are not even guaranteed to be local extrema. A new particle swarm optimizer, called stochastic PSO, which is guaranteed to convergence to the global optimization solution with probability one, is presented based on the analysis of the standard PSO. And the global convergence analysis is made using the F.Solis and R.Wets’ research results. Finally, several examples are simulated to show that SPSO is more efficient than the standard PSO.
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paper_title: AN IMPROVED FUZZY PARTICLE SWARM OPTIMIZATION FOR NUMERICAL OPTIMIZATION

paper_content:

Fuzzy particle swarm optimization (FPSO) is a new variant of particle swarm optimization (PSO). Compared to PSO, each particle in FPSO is attracted by its previous best particle and other particles (not the global best particle) selected by a fuzzy mechanis- m. Although FPSO effectively slows down the attraction of the previous best particle and the global best particle, it shows slow convergence rate when solving complex optimization problems. To enhance the performance of FPSO, this paper proposes an improved FPSO algorithm (IFPSO) which employs two strategies including generalized opposition-based learning (GOBL) and Levy mutation. In order to verify the performance of our approach, thirteen well-known benchmark functions and a real-world optimization problem are used in the experiments. Simulation results show that our approach can significantly improve the performance of FPSO and outperforms several other state-of-the-art PSO algorithms.
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paper_title: Chaotic catfish particle swarm optimization for solving global numerical optimization problems

paper_content:

Abstract Chaotic catfish particle swarm optimization (C-CatfishPSO) is a novel optimization algorithm proposed in this paper. C-CatfishPSO introduces chaotic maps into catfish particle swarm optimization (CatfishPSO), which increase the search capability of CatfishPSO via the chaos approach. Simple CatfishPSO relies on the incorporation of catfish particles into particle swarm optimization (PSO). The introduced catfish particles improve the performance of PSO considerably. Unlike other ordinary particles, the catfish particles initialize a new search from extreme points of the search space when the gbest fitness value (global optimum at each iteration) has not changed for a certain number of consecutive iterations. This results in further opportunities of finding better solutions for the swarm by guiding the entire swarm to promising new regions of the search space and accelerating the search. The introduced chaotic maps strengthen the solution quality of PSO and CatfishPSO significantly. The resulting improved PSO and CatfishPSO are called chaotic PSO (C-PSO) and chaotic CatfishPSO (C-CatfishPSO), respectively. PSO, C-PSO, CatfishPSO, C-CatfishPSO, as well as other advanced PSO procedures from the literature were extensively compared on several benchmark test functions. Statistical analysis of the experimental results indicate that the performance of C-CatfishPSO is better than the performance of PSO, C-PSO, CatfishPSO and that C-CatfishPSO is also superior to advanced PSO methods from the literature.
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paper_title: Chaotic catfish particle swarm optimization for solving global numerical optimization problems

paper_content:

Abstract Chaotic catfish particle swarm optimization (C-CatfishPSO) is a novel optimization algorithm proposed in this paper. C-CatfishPSO introduces chaotic maps into catfish particle swarm optimization (CatfishPSO), which increase the search capability of CatfishPSO via the chaos approach. Simple CatfishPSO relies on the incorporation of catfish particles into particle swarm optimization (PSO). The introduced catfish particles improve the performance of PSO considerably. Unlike other ordinary particles, the catfish particles initialize a new search from extreme points of the search space when the gbest fitness value (global optimum at each iteration) has not changed for a certain number of consecutive iterations. This results in further opportunities of finding better solutions for the swarm by guiding the entire swarm to promising new regions of the search space and accelerating the search. The introduced chaotic maps strengthen the solution quality of PSO and CatfishPSO significantly. The resulting improved PSO and CatfishPSO are called chaotic PSO (C-PSO) and chaotic CatfishPSO (C-CatfishPSO), respectively. PSO, C-PSO, CatfishPSO, C-CatfishPSO, as well as other advanced PSO procedures from the literature were extensively compared on several benchmark test functions. Statistical analysis of the experimental results indicate that the performance of C-CatfishPSO is better than the performance of PSO, C-PSO, CatfishPSO and that C-CatfishPSO is also superior to advanced PSO methods from the literature.
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paper_title: A Modified Particle Swarm Optimization with Nonlinear Decreasing Inertia Weight Based PID Controller for Ultrasonic Motor

paper_content:

Ultrasonic motor (USM) exhibits non-linearity that relates the input and output. It also causes serious characteristic changes during operation. PID controller has been widely used as the control scheme for USM. However, it is difficult for the fixed-gain type PID controller to compensate such characteristic changes and non-linearity of USM. The present paper proposes a modified PSO with nonlinear decreasing inertia weight (PSO-NDW) for optimal self tuning of PID controller in positioning control of USM. A modified PSO employs the strategy that nonlinearly decreases the value of inertia weight from a large value to a small value. This strategy is to improve the performance of the standard PSO algorithm in global search and fine-tuning of the solutions. The performance of PSO-NDW based PID controller has been evaluated on the USM servo system. The results demonstrate that the proposed modified PSO can improve the accuracy of USM.
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Title: Modifications of Particle Swarm Optimization Techniques and Its Application on Stock Market: A Survey

Section 1: INTRODUCTION
Description 1: This section introduces the concepts of Computational Intelligence, Swarm Intelligence, and the motivation behind using Particle Swarm Optimization (PSO) for stock market applications.

Section 2: HISTORICAL BACKGROUND OF PARTICLE SWARM OPTIMIZATION
Description 2: This section provides a historical overview of Particle Swarm Optimization and its development since its inception.

Section 3: STOCK MARKET REVIEW
Description 3: This section reviews the stock market, its significance, and the importance of accurate market prediction.

Section 4: BASIC PARTICLE SWARM OPTIMIZATION
Description 4: This section explains the fundamental principles and mechanisms of the Particle Swarm Optimization algorithm.

Section 5: MODIFICATIONS OF THE ORIGINAL PSO
Description 5: This section discusses various modifications of the PSO, categorized into external and internal modifications, aimed at enhancing its performance.

Section 6: CONCLUSION
Description 6: This section summarizes the main points covered in the survey and suggests future research directions based on the findings.