Sharif Digital Repository

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... 

The objective of this study is to design sub-optimal signature matrices for binary inputs for an overloaded code division multiple access (CDMA) system as developed by this author group. In this study, the authors propose to use the sum capacity, the bit error rate and distance criteria as objective functions for signature matrix optimisation. Three optimisation techniques, the genetic algorithm, the particle swarm optimisation and the conjugate gradient (CG) are exploited in this work. Since the optimisation computational complexity increases by matrix dimensions, it is practically impossible to directly optimise the large signature matrices. In order to address this problem, a method is... 

Optimum highway alignment is among the most substantial, but large and complicated topics in transportation area. Infinite number of feasible solutions, numerous local optima and the constrained feature of the problem, associated with complex and mainly non-linear constraints, has put an extra effort into the problem solving process. This paper focuses on solving highway alignment optimization problem using an integrated model of parallel processing and particle swarm optimization algorithm. To achieve this goal, algorithm parallelization is done in synchronous and asynchronous manner. For assessing parallel performance, corresponding indexes are evaluated. SRTM3 databank is used for solving... 

This paper illustrates how a 3 degrees of freedom, Cartesian robot can be given the task of playing ping-pong against a human player. We present an algorithm based on particle swarm optimization for the robot to calculate when and how to hit an approaching ball. Simulation results are shown to depict the effectiveness of our approach. Although emphasis is placed on sending the ball to a desired point on the ping pong table, it is shown that our method may be adjusted to meet the requirements of a variety of ball hitting strategies  

This paper presents mathematical modeling of joint lot sizing and scheduling problem in a job shop environment under a set of realistic working conditions. One main realistic assumption of the current study is dealing with flexible machines able to change their working speeds, known as process compressibility. The production schedules should be subject to limited available time in every planning period. Also, the model assumes that periodical sequences should be determined in a way that they obey from a fixed global sequence. Another complicating aspect of the problem is about consideration of precedence relationships for the needed processes of an item type over the corresponding machines.... 

In this research, we approach the flowshop sequence-dependent group scheduling problem with minimisation of total weighted earliness and tardiness as the objective for the first time. A mixed integer linear programming model is developed to solve the problem optimally. Since the proposed research problem is proven to be NP-hard, a hybrid meta-heuristic algorithm based on the particle swarm optimisation (PSO) algorithm, enhanced with neighbourhood search is developed to heuristically solve the problem. Since the objective is a non-regular, a timing algorithm is developed to find the best schedule for each sequence provided by the metaheuristic algorithm. A lower bounding method is also... 

A Timetabling problem is usually defined as assigning a set of events to a number of rooms and timeslots such that they satisfy a number of constraints. Particle swarm optimization (PSO) is a stochastic, population-based computer problem-solving algorithm; it is a kind of swarm intelligence that is based on social-psychological principles and provides insights into social behavior, as well as contributing to engineering applications. This paper applies the Particle Swarm Optimization algorithm to the classic Timetabling problem. This is inspired by similar attempts belonging to the evolutionary paradigm in which the metaheuristic involved is tweaked to suit the grouping nature of problems... 

In Particle Swarm Optimization (PSO) algorithm, although taking an active role to guide particles moving toward optimal solution, the most-fit candidate does not have a guide itself and only moves along its velocity vector in every iteration. This may yield a noticeable number of agents converge into local optima if the guide (i.e. the most fit candidate) agent cannot explore the best solution. In this paper, an attempt is made to get the advantage of the Ant Colony Optimization (ACO) methodology to assist the PSO algorithm for choosing a proper guide for each particle. This will strengthen the PSO abilities for not getting involved in local optima. As a result, we present a promising new... 

Simulation optimization is an endeavor to determine the best combination of inputs that result in the best system performance criterion without evaluating all possible combinations. Since simulation optimization applies to many problems, it is extensively studied in the literature with different methods. However, most of these methods ignore the uncertainty of the systems’ parameters, which may lead to a solution that is not robustly optimal in reality. Motivated by this uncertainty, we propose a robust simulation optimization algorithm that follows the well-known Taguchi standpoint but replaces its statistical technique with a minimax method based on the kriging (Gaussian process)... 

Particle Swarm Optimization (PSO) is a well-known swarm intelligence (SI) algorithm employed for solving various optimization problems. This algorithm suffers from premature convergence to local optima. Accordingly, a number of PSO variants have been proposed in the literature. These algorithms exploited different schemes to improve performance. In this paper, we propose a new variant of PSO with an enhanced Learning strategy and Crossover operator (PSOLC). This algorithm applies three strategies, comprising altering the exemplar particles, updating the PSO parameters, and integrating PSO with Genetic Algorithm (GA). In the proposed learning strategy, each particle is guided by the best... 

This paper presents a particle swarm optimization (PSO) based fuzzy stochastic long term approach for determining optimum location and size of distributed energy resources (DERs). The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting. A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduction in peak load level, and reduction in voltage deviation are simultaneously considered as the objective functions. At first these objectives are fuzzified and designed to be comparable with each other, then they are introduced to a PSO...