Sharif Digital Repository

The problem of early convergence in the Particle Swarm Optimization (PSO) algorithm often causes the search process to be trapped in a local optimum. This problem often occurs when the diversity of the swarm decreases and the swarm cannot escape from a local optimal. In this paper, a novel dynamic diversity enhancement particle swarm optimization (DDEPSO) algorithm is introduced. In this variant of PSO, we periodically replace some of the swarm's particles by artificial ones, which are generated based on the history of the search process, in order to enhance the diversity of the swarm and promote the exploration ability of the algorithm. Afterwards, we update the velocity of the artificial... 

This paper studies the control of a brachiating robot imitating the locomotion of a long armed ape. The robot has two revolute joints, but only one of them is actuated. In this paper, after deriving dynamic model of the robot, the Controlled Lagrangians (CL) method is used to design a controller for point to point locomotion. The CL method involves satisfying a number of equations called matching conditions. The matching conditions are derived using the extended λ-method in the form of a set of partial differential equations (PDEs). Solving the PDEs, a class of controllers is found that satisfies the matching conditions. The fittest controller in the class of controllers is then chosen by... 

Owing to wide-ranging searches (there are various alignments between two points) as well as complex and nonlinear cost functions and a variety of geometric constraints, the problem of optimal railway alignment is classified as a complex problem. Thus, choosing an alignment between two points is usually done based on a limited number of alignments designed by experts. In recent years, the study of railway alignment optimization has shown the importance of optimization and the introduction of various algorithms and their usefulness in solving different problems. It is expected that applying meta-heuristic optimization algorithms such as methods based on swarm intelligence can lead to better... 

Deep excavation in urban areas can cause instability problems due to significant settlement at the ground surface and large movements at the excavation facing walls. One of the most popular methods used to stabilise these excavations is utilising soil-nailing method. This method has also been widely used to stabilise natural slopes and earth retaining structures. Because of the complexity involved in the mechanism of this stabilising system due to interacting effects of the soil, nails, grout and shotcrete, numerical modelling with high accuracy should be used to analyse the behaviour of the soil-nailed walls. Considering all aspects of soil-structure interaction in the present research, a... 

Deep excavation in urban areas can cause instability problems due to significant settlement at the ground surface and large movements at the excavation facing walls. One of the most popular methods used to stabilise these excavations is utilising soil-nailing method. This method has also been widely used to stabilise natural slopes and earth retaining structures. Because of the complexity involved in the mechanism of this stabilising system due to interacting effects of the soil, nails, grout and shotcrete, numerical modelling with high accuracy should be used to analyse the behaviour of the soil-nailed walls. Considering all aspects of soil-structure interaction in the present research, a... 

One of the recently developed approaches for control of chaos is the minimum entropy (ME) control technique. In this method an entropy function based on the Shannon definition, is defined for a chaotic system. The control action is designed such that the entropy as a cost function is minimized which results in more regular pattern of motion for the system trajectories. In this paper an online optimization technique using particle swarm optimization (PSO) method is developed to calculate the control action based on ME strategy. The method is examined on some standard chaotic maps with error feedback and delayed feedback forms. Considering the fact that the optimization is online, simulation... 

ABSTRACT Compressed sensing is a new theory that samples a signal below the Nyquist rate. While Gaussian and Bernoulli random measurements perform quite well on the average, structured matrices such as Toeplitz are mostly used in practice due to their simplicity. However, the signal compression performance may not be acceptable. In this paper, we propose to optimize the Toeplitz matrices to improve its compression performance to recover sparse signals. We establish the optimization on minimizing the coherence of the measurement matrix by an intelligent optimization method called Particle Swarm Optimization. Our simulation results show that the optimized Toeplitz matrix outperforms the... 

This paper presents a new method to determine the optimal number and locations of autorecloser and sectionalizer switches (AR/S) in distribution networks. The costs of AR/S investment, switch maintenance, and undistributed energy as well as reliability coefficients are considered in the objective function. Reliability parameters such as SAIFI, SADI, MAIFI, and ENS are evaluated in the case study system. As a new method, the weights of the reliability parameters are obtained by decision-makers using the analytical hierarchy process (AHP). The optimal size, type, and location of automatic switches are determined by minimizing the objective function using the particle swarm optimization (PSO)... 

This paper studies a brachiation robot that is a long armed locomotion similar to apes. The robot has 2 revolute joints but only one of them is actuated. In this paper, after deriving dynamic model of the robot the Controlled Lagrangian (CL) method is used for stabilization. The matching conditions satisfied for the controller are derived and the extended λ-method is used to solve PDE's involved in the method of controlled lagrangian. Satisfactory controller's gains are chosen by PSO algorithm. Finally, feasibility of the developed controller is investigated by numerical simulations  

One of the recently developed approaches for control of chaos is the minimum entropy (ME) control technique. In this method an entropy function based on the Shannon definition, is defined for a chaotic system. The control action is designed such that the entropy as a cost function is minimized which results in more regular pattern of motion for the system trajectories. In this paper an online optimization technique using particle swarm optimization (PSO) method is developed to calculate the control action based on ME strategy. The method is examined on some standard chaotic maps with error feedback and delayed feedback forms. Considering the fact that the optimization is online, simulation... 

Automatic switching equipment and protection devices (AS/PDs) play a fundamental role in radial power distribution networks. In this paper, a new hybrid method is presented to determine the optimal number, types, and locations of AS/PDs based on protective coordination. When a short circuit fault occurs in a medium voltage distribution network (MV feeder), AS/PDs such as relays, fuses, auto-reclosers and sectionalizers can be employed to disconnect the faulty part from the healthy area. The use of automatic switches at proper locations can significantly decrease system interruptions. The costs of investment, maintenance, and unsupplied energy are considered in the cost function. This... 

Anchored wall system is one of the common methods used for deep excavation stabilization adjacent to sensitive structures in urban areas. A key aspect of the stability analysis of deep excavations is the amount of deformations occurring on the facing wall and the adjacent structures. In this research, a large number of parametric studies considering all aspects of soil-structure interaction is carried out for different excavation geometries to find the optimal design, and the outcome is shown in the form of design tables and charts. Also, by a GA-PSO algorithm and using the large database obtained from the numerical simulations, a simple equation is developed that can predict the deflections... 

Motion planning is a key factor in enhancing the autonomy level of unmanned flying vehicles. A new dynamic hybrid algorithm is developed to solve the motion planning problem in real-time using a heuristic optimization approach. The proposed algorithm effectively combines desired features such as rapid convergence to an optimal path with reduced computational effort. In addition to the terrain obstacles, the proposed algorithm is able to avoid random threats that may arise sporadically in the terrain. Using the maneuver automaton concept, nonlinear dynamic model and performance constraints are also considered in the process of motion planning to further ensure feasible trajectories.... 

A Particle Swarm Optimization (PSO) algorithm for a Flow Shop Sequence Dependent Group Scheduling (FSDGS) problem, with minimization of total flow time as the criterion (Fmmls, Spuc, prmu 52 Q), is proposed in this research. An encoding scheme based on Ranked Order Value (ROV) is developed, which converts the continuous position value of particles in PSO to job and group permutations. A neighborhood search strategy, called Individual Enhancement (IE), is fused to enhance the search and to balance the exploration and exploitation. The performance of the algorithm is compared with the best available meta-heuristic algorithm in literature, i.e. the Ant Colony Optimization (ACO) algorithm, based... 

This paper presents evolutionary methods for optimization in dynamic mobile robot path planning. In dynamic mobile path planning, the goal is to find an optimal feasible path from starting point to target point with various obstacles, as well as smoothness and safety in the proposed path. Pattern search (PS) algorithm, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to find an optimal path for mobile robots to reach to target point with obstacle avoidance. For showing the success of the proposed method, first they are applied to two different paths with a dynamic environment in obstacles. The first results show that the PSO algorithms are converged and minimizethe... 

The trend of gold price in the market is the most important consideration for the investors of the gold, and serves as the basis of gaining profit, so there are scholars who try to forecast the gold price. Forecasting accuracy is one of the most important factors involved in selecting a forecasting method. Besides, nowadays artificial intelligence (AI) techniques are becoming more and more widespread because of their accuracy, symbolic reasoning, flexibility and explanation capabilities. Among these techniques, particle swarm optimization (PSO) is one of the best AI techniques for optimization and parameter estimation. In this study a PSO-based time series model for the gold price... 

Color quantization is a technique for processing and reduction colors in image. The purposes of color quantization are displaying images on limited hardware, reduction use of storage media and accelerating image sending time. In this paper a hybrid algorithm of GA and Particle Swarm Optimization algorithms with FCM algorithm is proposed. Finally, some of color quantization algorithms are reviewed and compared with proposed algorithm. The results demonstrate Superior performance of proposed algorithm in comparison with other color quantization algorithms