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Recurrent neural network has been widely used as auto-regressive model for time series. The most commonly used training method for recurrent neural network is back propagation. However, recurrent neural networks trained with back propagation can get trapped at local minima and saddle points. In these cases, auto-regressive models cannot effectively model time series patterns. In order to address these...
This paper has as a start point the metaheuristic Particle Swarm Optimization (PSO), which has very good abilities to solve many types of optimization problems. As a main contribution, this work proposes an intelligent algorithm derived from PSO. This algorithm has two main characteristics. The first one consists in the use of an improved version of PSO, namely Hybrid Topology Particle Swarm Optimization...
The dynamic characteristics of a hydraulic turbine governing system is determined by the parameters of the hydraulic turbine governor. There are several drawbacks of the conventional particle swarm algorithm in parameter optimization, such as low speed of convergence, low accuracy and being inclined to result in partial optimization during the process of optimization. This paper introduced concave...
In this paper, to increase the accuracy and the rate of convergence of the algorithm, by employing orthogonal experiment and mutation operation to the traditional Quantum-behaved Particle Swarm Optimization (QPSO), an improved QPSO (IQPSO) has been presented. The optimization criterion, the ITAE (Integral of Time and Absolute Error) tested that the efficiency of IQPSO is superior to the traditional...
Particle swarm optimization (PSO) is a state-of-the-art algorithm in meta-heuristic optimization study area. It is a swarm based algorithm that mimic fish or bird's behaviors in the nature. Success rate of convergence in an optimization algorithm depends on control balancing between exploration and exploitation. Inertia weight coefficient parameter controls convergence rate of PSO algorithm. In this...
Partial shading condition (PSC) is the most serious concern of a photovoltaic (PV) system as it pointedly decreases the proficiency of PV system. The PV system generates numerous peaks in its output power characteristics curve under PSC. To track a global maximum power point (GMPP) with-in a suitable time limit, a consistent method is thus required. For achieving high performance, enhanced convergence...
Particle Swarm Optimization (PSO) is an evolutionary computing algorithm and is successfully used to solve complex real world optimization problems. Due to the complex nature of optimization problems, PSO endures the problems like premature convergence or being trapped in local minima, to avoid such situation the role of swarm initialization is very important. In this research we propose a new method...
In swarm robotics, the self-organization of multiagent systems which consists of a number of comparatively simple agents is an approach inspired from natural swarms. In this paper, we solve the findpath problem of n ε N agents using the principle of swarming. A Lagrangian swarm model which could navigate in a cluttered configuration space is developed. A Lyapunov like function is constructed from...
In primate life, there are a number of various social behavior, such as communication among members in a group, and food sharing, which are vital to maintain their survival. Similar to those of Swarm Intelligence, such as ant colony optimization, the behavior of primates motivates us to develop an algorithm with the aim of solving continuous problems. Our algorithm is inspired by the behavior of the...
In the case of particle swarm optimization, this paper mainly analyzes and discusses the mathematical model and the analysis on searching for the global optimum region. Firstly, the global optimum region Θ is defined and calculated in the convergence step and the divergence step. Furthermore, the rate μ of locating into the global optimum region is mathematically related to the number of particles,...
This paper proposes an individual representation for optimizing allocation of static var compensators (SVCs). Generally, the individuals indicate the capacities of the devices in the optimal allocation of SVCs. In the proposed representation, capacities and installation places of SVCs are expressed in separate variables. The effectiveness of the proposed representation is verified by computer simulation...
The tracking is an application of computer vision has grown remarkably over the last two decades. By tracking, exist a set of algorithms that provide detecting and tracking objects in a video sequence. It then touches several areas, such as video surveillance biometric or biomedical imaging, the human-machine interaction, traffic control and intelligent vehicles, etc. The multiplicity of problems...
Advanced correlation filters are an effective tool for target detection within a particular class. Most correlation filters are derived from a complex filter equation leading to a closed form filter solution. The response of the correlation filter depends upon the selected values of the optimal trade-off (OT) parameters. In this paper, the OT parameters are optimized using particle swarm optimization...
In this research, quantum particle swarm optimization (QPSO) is utilized to solve multiobjective combined economic emission dispatch (CEED) problem formulated using cubic criterion function considering a uni wise max/max price penalty factor. QPSO is implemented on a 6-unit power generation system and compared with Lagrangian relaxation, particle swarm optimization (PSO) and simulated annealing (SA)...
It is widely known that particle swarm optimization (PSO) has some drawbacks, especially it loses diversity easily. In order to solve this problem, some improved PSOs were proposed which update velocity according to diversity. However, some important information about particles is still not sufficiently utilized such as fitness values. As a gradient descent method, backpropagation (BP) algorithm is...
Spider Monkey Optimization is a well known meta-heuristic in the arena of nature inspired algorithms. It is basically known for its stagnation removal power in its original design. To balance the meta-heuristics mechanisms while preserving premature convergence, a new variant is developed which is named as Modified spider monkey optimization. In this paper, metropolis principle is used from simulated...
Recently, Search Based Software Testing (SBST) research has gained much attention in producing the optimal solution for the optimization problem by automating the test data generation for the branch coverage criterion. Particle Swarm Optimization (PSO) has been emerged for obtaining optimal solution for the test data generation problem because of its easy implementation, fast convergence and few parameters...
These days, most of the real-world problems have become multi-criteria in nature and the demand for an effective multi-objective optimization algorithm has been significantly increased. This paper presents a new Multi-Objective Self-Regulating Particle Swarm Optimization (MOSRPSO) algorithm whereby the SRPSO algorithm originally developed for single objective problems has been modified to tackle with...
In time difference of arrival (TDOA) location system, the cooperative localization algorithm based on Particle swarm optimization (PSO) and Taylor series not only solves the problem that particle swarm is easy to fall into local optimum, but also avoids the inappropriate initial value which leads to non convergence of Newton iterative algorithm. In practical applications, the coordinate information...
In this study, we propose a novel intelligent particle swarm optimization(PSO). In case of PSO, when particles approach to optimum point, particles roam near global optimum. Therefore, PSO takes unnecessary function call and long convergence time. In order to solve these problems, we propose distance based intelligent particle swarm optimization(DbIPSO). DbIPSO calculate distance of every particle...
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