Sharif Digital Repository

This paper presents a new method to simultaneously optimize components' size, set points of the control system and slope of PV panels of standalone hybrid energy systems (HESs) using the Passive Congregation PSO (PSOPC) approach. New control set points are defined for the HES, and a new operation strategy is presented based on the defined set points. The optimization algorithm determines the optimal values of the set points to efficiently optimize the HES operation. The effectiveness of the proposed control set points is finally verified through some numerical analyses. In this regard, the proposed optimization method is employed to optimize various HES configurations and compared with other... 

Minimum entropy control technique, an approach for controlling chaos without using the dynamical model of the system, can be improved by being combined with a nature-based optimization technique. In this paper, an ACO-based optimization algorithm is employed to minimize the entropy function of the chaotic system. The feedback gain of a delayed feedback controller is adjusted in the ACO algorithm. The effectiveness of the idea is investigated on suppressing chaos in the tapping-mode atomic force microscope equations. Results show a good performance. The PSO-based version of the minimum entropy control technique is also used to control the chaotic behavior of the AFM, and corresponding results... 

Flocking through leader following structures in mobile networks raises attractive control problems. Due to limited sensing radii, leaders locally influence a network of agents. In this paper, we consider the problem of real-time maximization of flocking velocity. By using local information and a Particle-Swarm-Optimization (PSO) algorithm, a Leader Agent (LA) actively motivates flocking at high speed. The LA manages topology of the network in its neighborhood and increases flocking velocity. PSO output quality and calculation costs show that the proposed optimization algorithm is practically feasible. A case-study is also presented  

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... 

The minimum entropy (ME) control is a chaos control technique which causes chaotic behavior to vanish by stabilizing unstable periodic orbits of the system without using mathematical model of the system. In this technique some controller type, normally delayed feedback controller, with an adjustable parameter such as feedback gain is used. The adjustable parameter is determined such that the entropy of the system is minimized. Proposed in this paper is the PSO-based multi-variable ME control. In this technique two or more control parameters are adjusted concurrently either in a single or in multiple control inputs. Thus it is possible to use two or more feedback terms in the delayed feedback... 

Aluminum metal matrix composites (MMCs) reinforced with nanoceramics are ideal materials for the manufacture of lightweight automotive and other commercial parts. Adaptive neuro-fuzzy inference system combined with particle swarm optimization method is implemented in this research study in order to optimize the parameters in processing of aluminum MMCs. In order to solve the problems associated with poor wettability, agglomeration and gravity segregation of nanoparticles in the melt, a mixture of alumina and aluminum particles was used as the reinforcement instead of raw nanoalumina. Microstructural characterization shows dendritic microstructure for the sand cast and non-dendritic... 

Vendor-managed inventory (VMI) is a popular policy in supply chain management (SCM) to decrease bullwhip effect. Since the transportation cost plays an important role in VMI and because the demands are often fuzzy, this paper develops a VMI model in a multi-retailer single-vendor SCM under the consignment stock policy. The aim is to find optimal retailers' order quantities so that the total inventory and transportation cost are minimized while several constraints are satisfied. Because of the NP-hardness of the problem, an algorithm based on particle swarm optimization (PSO) is proposed to find a near optimum solution, where the centroid defuzzification method is employed for... 

In this study, hydrogen generation is maximised by optimising the size and the operating conditions of an electrolyser (EL) directly connected to a photovoltaic (PV) module at different irradiance. Due to the variations of maximum power points of the PV module during a year and the complexity of the system, a nonlinear approach is considered. A mathematical model has been developed to determine the performance of the PV/EL system. The optimisation methodology presented here is based on the particle swarm optimisation algorithm. By this method, for the given number of PV modules, the optimal sizeand operating condition of a PV/EL system areachieved. The approach can be applied for different... 

In this paper, a seasonal multi-product multi-period inventory control problem is modeled in which the inventory costs are obtained under inflation and all-unit discount policy. Furthermore, the products are delivered in boxes of known number of items, and in case of shortage, a fraction of demand is considered backorder and a fraction lost sale. Besides, the total storage space and total available budget are limited. The objective is to find the optimal number of boxes of the products in different periods to minimize the total inventory cost (including ordering, holding, shortage, and purchasing costs). Since the integer nonlinear model of the problem is hard to solve using exact methods, a... 

Optimal well placement is a crucial step in reservoir development process. The key points in such an optimization process are using a fast function evaluation tool and development of an efficient optimization algorithm. This study presents an approach that uses particle swarm optimization algorithm in conjunction with streamline simulation to determine the optimum well locations within a reservoir, regarding a modified net present value as the objective. Performance of this algorithm was investigated through several different examples, and compared to that of genetic algorithm (GA) and simulated annealing (SA) methods. It was observed that particle swarm optimization algorithm outperformed... 

This paper presents a particle swarm optimization (PSO) based approach to solve the multi-stage transmission expansion planning problem in a competitive pool-based electricity market. It is a large-scale non-linear combinatorial problem. We have considered some aspects in our modeling including a multi-year time horizon, a number of scenarios based on the future demands of system, investment and operating costs, the N - 1 reliability criterion, and the continuous non-linear functions of market-driven generator offers and demand bids. Also the optimal expansion plan to maximize the cumulative social welfare among the multi-year horizon is searched. Our proposed PSO based approach, namely... 

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  

The main recognition procedure in modern HMM-based continuous speech recognition systems is Viterbi algorithm. Viterbi algorithm finds out the best acoustic sequence according to input speech in the search space using dynamic programming. In this paper, dynamic programming is replaced by a search method which is based on particle swarm optimization. The major idea is focused on generating initial population of particles as the speech segmentation vectors. The particles try to achieve the best segmentation by an updating method during iterations. In this paper, a new method of particles representation and recognition process is introduced which is consistent with the nature of continuous... 

Due to the important role that distribution systems play in quality of power delivered to the customers, there has always been a great deal of interest in investigating different methods of efficiency enhancement for these networks. Two of these methods are Feeder Reconfiguration (FR) and Capacitor Allocation (CA); both have been widely employed to reduce losses and improve several other operational characteristics in electrical power distribution systems. As in FR process the topology of the network changes, it is necessary to change some previous settings; for instance: the capacity of capacitor banks in service in each bus, after each reconfiguration process. This is due to the fact that... 

Smartening of contemporaneous power delivery systems in conjunction with increased penetration of vehicle to grid (V2G) technology, changes the way market participants play their role in the market operation to maximize their profit. In V2G technology, plug-in electric vehicles (PEV) have bidirectional power flows i.e. they can either inject power to the grid or draw power from it. In recent years, the V2G technology has found a world wild attention due to its important advantages such as the peak load reduction and providing system reserve, to name a few. The unit commitment (UC) is a power system operation problem which is used to find the optimal operation schedule of generation units.... 

Purpose: The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) to classify the operating faults of a hydraulic system. The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits. Design/methodology/approach: In the proposed methodology, first, the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms, i.e. a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy (CMA-ES) and... 

This paper presents a new approach to better utilize international air-corridors for formation flights. Using Particle Swarm Optimization, we show how different long-range flights can be brought together to form a formation. The idea serves both to increase international air-corridor capacities together and to decrease fuel consumption up to 8-10%. Case studies show that there are many places which such an approach could effectively be implemented to help cope with increase in fuel price as well as the demand for aerial transport of passengers and goods  

A novel and robust approach is proposed to find the optimum shape of concrete arch dams located on any unsymmetrical shape of a valley. The approach is capable of finding the optimum shape for any given valley type in suitable time, based on abutment stability analysis, against thrust forces from an arch dam. The behavior and stability of a concrete arch dam is strongly dependent on the bedrock on which the dam rests. The stability of the abutment is considered a constraint in the proposed approach. In addition, a new objective function is introduced to decrease the final volume of the arch dam. Furthermore, a computer program was developed, which takes the effect of the dam foundation... 

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... 

In this paper, a multiproduct multi-chance constraint stochastic inventory control problem is considered, in which the time-periods between two replenishments are assumed independent and identically distributed random variables. For the problem at hand, the decision variables are of integer-type, the service-level is a chance constraint for each product, and the space limitation is another constraint of the problem. Furthermore, shortages are allowed in the forms of fuzzy random quantities of lost sale that are backordered. The developed mathematical formulation of the problem is shown to be a fuzzy random integer-nonlinear programming model. The aim is to determine the maximum level of...