Sharif Digital Repository

This paper derives an estimated function made by simple Neural Network to find initial state of optimization parameters. It changes a system of differential equations with boundary values to a system of equations with initial values. So a lot of time would be saved to solve it. As a result, the system with differential equations will reach the desired final state. Copyright © 2006 by ASME  

The optimal design of stiffened plates for buckling under in-plane forces and bending moments was presented. The virtual work principle was applied in order to calculate the buckling coefficient and the critical load factor of the stiffened plates. It was observed that the buckling coefficient is a function of the displacement coefficients  

The usage of conventional fossil fuels has aided fast economic growth while also having negative consequences, such as increased global warming and the destruction of the ecosystem. This paper proposes a novel swarm-based metaheuristic method called Chimp Optimization Algorithm (ChOA) to tackle the environmental, economic dispatch issue and reducing the waste nonrenewable materials. In this regard, two objective functions named fuel cost function and emission cost function are proposed. Unique constrained handling also solves the challenge of multi-objective optimization. Standard IEEE 30 bus with six generators and a 10-unit system are used to demonstrate the usefulness of ChOA. The result... 

Integrated treatment planning for cancer patients has high importance in intensity modulated radiation therapy (IMRT). Direct aperture optimization (DAO) is one of the prominent approaches used in recent years to attain this goal. Considering a set of beam directions, DAO is an integrated approach to optimize the intensity and leaf position of apertures in each direction. In this paper, first, a mixed integer-nonlinear mathematical formulation for the DAO problem in IMRT treatment planning is presented. Regarding the complexity of the problem, two well-known metaheuristic algorithms, particle swarm optimization (PSO) and differential evolution (DE), are utilized to solve the model. The... 

This paper aims to develop a deteriorated bridge maintenance strategy that ensures the safe operation of steel structures and minimizes the total risk. Five common failure modes are considered for the deteriorated bridge: flexure, shear, deflection, fatigue failure for girder, and chloride attack for the concrete deck. Time-dependent and system reliability analyses are carried out to find the probability of failure under these failure modes. Risk-cost optimization is then used to determine the maintenance strategy. This method was applied to a working example. It was found that the developed maintenance strategy can predict when, where, and what to maintain for a bridge to ensure its safe... 

Presence of energy hubs in the future vision of energy networks creates a great opportunity for system planners and operators to move towards more efficient systems. The role of energy hubs as the intermediate in multi-carrier energy (MCE) systems calls for a generic framework to study the new upcoming technical as well as economical effects on the system performance. In response, this paper attempts to develop a general optimization and modeling framework for coupled power flow studies on different energy infrastructures. This, as a large-scale nonlinear problem, is approached through a robust optimization technique, i.e., multi-agent genetic algorithm (MAGA). The proposed procedure... 

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... 

In this paper, the problem of Optimal Trajectory Planning for a high speed planing boat under nonlinear equality and inequality path constraints, is addressed. First, a nonlinear mathematical model of the craft's dynamic is constructed. To solve a trajectory optimization problem, we can utilize the indirect or direct methods. In the indirect methods, the maximum principle of Pontryagin is used to transform the optimal control problem into Euler-Lagrange equations, on the other hand, in the direct methods it is necessary to transcribe the optimal control problem into a nonlinear programming problem (NLP) by discretization of states and controls. The resulted NLP can be solved by... 

A major issue required to enhance the autonomy level of unmanned vehicles is real-time motion planning. In this context, optimal trajectories need to be generated online considering the vehicle's dynamic potentials and constraints. However, autonomous air vehicles often need to plan and execute their missions with varying objectives that may even be dictated in flight. Therefore, the current study introduces and focuses on the new concept of variable-objective motion planning. In this regard, a new dynamic multi-objective heuristic optimization algorithm is developed for path and motion planning of autonomous air vehicles in presence of deterministic terrain obstacles as well as random... 

Hydrogen is an important storage medium and can be produced by the water electrolysis. In this research, energy transfer loss between a photovoltaic (PV) unit and electrolyzer is minimized by optimizing the size and operating condition of an electrolyzer directly connected to a PV module. In directly coupled photovoltaic-electrolyzer (PV/EL) systems, there is a mismatch between output PV's maximum power point characteristic and input PEM electrolyzer's characteristic. With proper sizing optimization methods, it is possible to directly couple photovoltaic-electrolyzer systems. The evolutionary optimization algorithms like genetic algorithm (GA), particle swarm optimization (PSO) and... 

Workspace of a mechanism is generally defined as the region of space which end-effector of that mechanism can reach. Determination of workspace is an important task in the design of a mechanism. However, for parallel mechanisms, due to the complexity of solving the forward kinematic equations, determination of workspace is much more complicated than for serial mechanisms. In the literature, time-consuming numerical methods, such as point-by-point searching, are usually employed for this purpose. In this paper, an optimization-based algorithm is introduced for the boundary determination of inclusive and constant orientation workspaces of parallel mechanisms. In the proposed algorithm, thanks... 

In this article, a new algorithm for rather expensive simulation problems is presented, which consists of two phases. In the first phase, as a model-based algorithm, the simulation output is used directly in the optimization stage. In the second phase, the simulation model is replaced by a valid metamodel. In addition, a new optimization algorithm is presented. To evaluate the performance of the proposed algorithm, it is applied to the (s,S) inventory problem as well as to five test functions. Numerical results show that the proposed algorithm leads to better solutions with less computational time than the corresponding metamodel-based algorithm. © 2017 Taylor & Francis Group, LLC  

In this paper, we develop and analyze a novel performance metric, called interference efficiency (IE), that shows the number of transmitted bits per unit of interference energy imposed on the primary users (PUs) in an underlay cognitive radio network (CRN). Specifically, we develop a framework to maximize the IE of a CRN with multiple secondary users (SUs) while satisfying target constraints on the average interference power on PU receiver, total SUs transmit power and minimum ergodic rate for the SUs. In doing so, we formulate a multiobjective optimization problem (MOP), that aims to achieve the maximum ergodic sum rate of multiple SUs and the minimum average interference power on the... 

Optimal trajectory and muscle forces of a human-like musculoskeletal arm are predicted for planar point-to-point movements using optimal control theory. The central nervous system (CNS) is modeled as an optimal controller that performs a reaching motion to final states via minimization of an objective function. For the CNS strategy, a cubic function of muscles stresses is considered as an appropriate objective function that minimizes muscles fatigue. A two-DOF nonlinear musculoskeletal planar arm model with four states and six muscle actuators is used for the evaluation of the proposed optimal strategy. The nonlinear variational formulation of the corresponding optimal control problem is... 

This paper models the cyber-social system as a cyber-network of agents monitoring states of individuals in a social network. The state of each individual is represented by a social node, and the interactions among individuals are represented by a social link. In the cyber-network, each node represents an agent, and the links represent information sharing among agents. The agents make an observation of social states and perform distributed inference. In this direction, the contribution of this paper is threefold: First, a novel distributed inference protocol is proposed that makes no assumption on the rank of the underlying social system. This is significant as most protocols in the... 

The goal of this work is to propose a hybrid algorithm called real-coded self-organizing migrating genetic algorithm by combining real-coded genetic algorithm (RCGA) and self-organizing migrating algorithm (SOMA) for solving bound-constrained nonlinear optimization problems having multimodal continuous functions. In RCGA, exponential ranking selection, whole-arithmetic crossover and non-uniform mutation operations have been used as different operators where as in SOMA, a modification has been done. The performance of the proposed hybrid algorithm has been tested by solving a set of benchmark optimization problems taken from the existing literature. Then, the simulated results have been... 

This paper presents a modified particle swarm optimization (MPSO) algorithm for solving the optimal power flow (OPF) problems. The main distinction of this approach is in using particle's worth experience in stead of the best previous experience. The proposed approach is evaluated on the IEEE 30-bus test system which minimizes the total fuel cost considering operational constraints such as power flow equations, transmission flow limits, bus voltages and reactive power of generators. The results obtained using the proposed approach are compared with results of other optimization methods  

The problem of aerodynamic shape optimization of unguided projectiles has been investigated. Two stochastic optimization methods have been applied to solve the problem. These include a Genetic Algorithm (GA) and the recently developed Continuous Ant Colony System (CACS), which is based on the well-known Ant Colony Optimization meta-heuristic. The objective function is defined as the summation of normal force coefficients over a set of given flight conditions. An engineering code (EC) is used to calculate the normal force coefficients over the flight conditions. The obtained results of CACS+EC are compared with those of GA+EC, as well as the results of a previous work (GA +AeroDesign). The... 

Short-term hydrothermal scheduling (STHTS) is a highly non-linear, multi-model, non-convex, and multi-dimensional optimization problem that has been worked upon for about 5 decades. Many research articles have been published in solving different test cases of STHTS problem, while establishing the superiority of one type of optimization algorithm over the type, in finding the near global best solution of these complex problems. This paper presents the implementation of an improved version of a variant of the Particle Swarm Optimization algorithm (PSO), known as Accelerated Particle Swarm Optimization (APSO) on three benchmark test cases of STHTS problems. The adaptive and variable nature of... 

We propose a time-varying dual accelerated gradient method for minimizing the average of n strongly convex and smooth functions over a time-varying network with n nodes. We prove that the time-varying dual accelerated gradient ascent method converges at an R-linear rate with the time to reach an ϵ-neighborhood of the solution being of O([Formula presented]ln⁡[Formula presented]), where c is a constant depending on the graph and objective function parameters and M is a constant depending on the initial values. We test the proposed method on two classes of problems: L2-regularized least squares and logistic classification problems. For each class, we generate 1000 problems and use the...