Sharif Digital Repository

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... 

The growing environmental concerns, governmental regulations, and significant cost savings are the primary motivations for companies to consider the reuse and recovery of products in their inventory system. The previous research ignored several realistic features of reusable items inventory systems, such as the presence of multiple products and operational constraints. For the first time, this paper presents a new multiproduct economic order quantity inventory model for an inventory system of reusable products. The goal of the model is to determine the optimal replenishment quantity and reuse quantity of each item so that the system's total cost is minimized. Several operational constraints... 

Intensity-modulated radiation therapy is a well-known technique for treating cancer patients worldwide. A treatment plan in this technique requires decision-making for three main problems: selection of beam angles, intensity map calculation, and leaf sequencing. Previous works investigated these problems sequentially. We present a new integrated framework for simultaneous decision-making of directions, intensities, and aperture shape, called direct angle and aperture optimization, and develop a mixed-integer nonlinear mathematical model for the problem. Due to the nonlinearity and the dimension of the problem, three efficient metaheuristics based on differential evolution (DE) called classic... 

Chemical fertilizers are widely applied in agriculture to achieve high yield, enhance produce quality and build resistance to diseases; in our case the plant being tomato (Solanum lycopersicum L. var. Royal). However, the acidity, size and taste of tomato fruits could change with excess nitrogen (N) application. The present study aims at the early detection of nitrogen-rich tomato leaves using hyperspectral imaging techniques in the visible and near infrared (Vis-NIR) spectrum, in order to improve plant nutrition composition at an early growth stage. A 30% over-dose of nitrogen was applied to half of the tomato pots. Five leaves were randomly collected from each pot for 3 days (classes D0,... 

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... 

Optimal planning of residential complex energy systems requires thorough mathematical modelling to address the interconnections between all the energy installations from the largest ones, shared by all the residents, to the smallest ones in each distinct unit. Besides, conflicting desires of investors and residents in various aspects such as reliability index make this problem more challenging. In response, this paper presents a thorough framework to obtain the optimum design and operation of a residential complex energy system from scratch. To address the appropriate interconnection between various components of such an energy system, a multi-layer energy hub structure is proposed. Besides,... 

Dynamic similar models are designed to study the flight behavior of the full-scale aircraft in early design stages. Due to physical and operational constraints, full dynamic similarity between the scaled-down model and full-scale aircraft is not feasible. Thus, the scale model would be flying at different Reynolds number and Mach number. A given aircraft configuration with specific aerodynamic characteristics will have different performance if Mach number and Reynolds number are changed considerably, which results in different dynamic behavior of the scale model. To compensate for these dissimilarities, it is proposed to modify the airfoil geometry of the scale model to preserve aerodynamic... 

Automotive Industry has an important place all around the world and sales forecasting process supports companies to meet their goals such as sales revenue increase, efficiency improvement, capacity planning and customer care. Traditional methods such as time series and econometrics have been applied by scientists during last decades. However, recently sales forecast problem by means of machine learning techniques are welcomed by data scientists because of increasing power of information technology in both hardware and software aspects. In this research, the hybridization of clustering method, Adaptive network Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) are developed... 

In this research, the economic lot scheduling problem (ELSP), as an NP-hard problem in terms of a bi-objective approach considering deteriorating items and shortage, is studied. The goal is to simultaneously minimise ‘setup and inventory holding costs, comprising deterioration’, and ‘total amount of units facing shortage throughout every period. Two policies besides a heuristic method are employed simultaneously, named extended basic period and Power-of-Two (PoT), to make sure of having feasible replenishment cycles. For handling the considered problem, three multi-objective techniques are employed: non-dominated sorting genetic algorithm II (NSGAII), non-dominated ranking genetic algorithm... 

The present study investigated a counter-flow cooling tower performance by integrating the Artificial Neural Networks and Intelligent Optimisation Algorithms (ANN-IOAs). For this purpose, two scenarios were evaluated. In the first scenario, inlet air wet-bulb temperature (T aw), inlet air dry bulb temperature (T ad), water to the air mass flow rate ratio (mw /ma), and rotor speed (υ) were the input parameters for the ANNs, while the output temperature (T wo) was the ANNs output. In the second scenario, the same input parameters applied for the first scenario were used as input variables and the tower efficiency (ε) was considered as an output parameter. The well-known IOAs methods, namely,... 

The trade-off between the fuel consumption and drag coefficient makes the investigations of drag reduction of utmost importance. In this paper, the rear-end shape optimization of Ahmed body is performed. Before changing the geometry, to identify the suitable simulation method and validate it, the standard Ahmed body is simulated using k − ω shear stress transport (SST) and k-epsilon turbulence models. The slant angle, rear box angle, and rear box length as variables were optimized simultaneously. Optimizations conducted by genetic algorithm (GA) and particle swarm optimization (PSO) methods indicate a 26.3% decrease in the drag coefficient. To ensure the validity of the results, a... 

The present study investigated a counter-flow cooling tower performance by integrating the Artificial Neural Networks and Intelligent Optimisation Algorithms (ANN-IOAs). For this purpose, two scenarios were evaluated. In the first scenario, inlet air wet-bulb temperature (T aw), inlet air dry bulb temperature (T ad), water to the air mass flow rate ratio (mw /ma), and rotor speed (υ) were the input parameters for the ANNs, while the output temperature (T wo) was the ANNs output. In the second scenario, the same input parameters applied for the first scenario were used as input variables and the tower efficiency (ε) was considered as an output parameter. The well-known IOAs methods, namely,... 

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)... 

The goal of this research is to formulate an inventory control problem of a single item with variable demand dependent on displayed stock level and selling price of the commodity. The item deteriorates based on a three-parameter Weibull distribution and advance payment is needed to purchase the item with the all-unit discount policy. Shortages are allowed partially and backlogged with the rate dependent on the length of customers' waiting time. The corresponding problem is formulated as a profit maximisation model. For solving this problem, four different variants of particle swarm optimisation (PSO) are utilised. Then, the application of the model is illustrated with the help of a numerical... 

An economic-statistical design with multiple assignable causes following exponential distribution is presented in this paper for linear profiles. For this purpose, a tri-objective optimization model is proposed to minimize the cost with desired statistical performances. Average Run Length (ARL) as the primary statistical measure is employed for the appraisal of the designed linear profiles. The first objective to be minimized is a cost function that models the implementation cost in different states. The second objective is to maximize ARL or the in-control average run-length of the monitoring scheme. The third objective to be minimized is ARL 1 or the out-of-control average run-length of... 

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... 

This study aims to investigate the hydrogen production process using an integrated system based on solar energy. This system includes an evacuated tube collector to absorb solar energy as input energy of the system. A parametric analysis was conducted to determine the most important design parameters and evaluate these parameters' impact on the system's objective functions. For identifying the optimum system conditions, multi-objective optimization was performed using particle swarm optimization (PSO) algorithm. The results obtained from the parametric analysis show that an increment in the collector mass flow rate and the turbine inlet temperature, as well as a decrement in the collector... 

The casing-collapse hazard is one that drilling engineers seek to mitigate with careful well design and operating procedures. However, certain rock formations and their fluid pressure and stress conditions are more prone to casing-collapse risks than others. The Gachsaran Formation in south west Iran, is one such formation, central to oil and gas resource exploration and development in the Zagros region and consisting of complex alternations of anhydrite, marl and salt. The casing-collapse incidents in this formation have resulted over decades in substantial lost production and remedial costs to mitigate the issues surrounding wells with failed casing string. High and vertically-varying... 

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... 

To have a sustainable economy and environment, several countries have widely inclined to the utilization of non-fossil fuels like biomass fuels to produce heat and electricity. The advantage of employing biomass for combustion is emerging as a potential renewable energy, which is regarded as a cheap fuel. Chemical constituents or elements are essential properties in biomass applications, which would be costly and labor-intensive to experimentally estimate them. One of the criteria to evaluate the energy of biomass from an economic perspective is the higher heating value (HHV). In the present work, we have applied multilayer perceptron artificial neural network (MLP-ANN), least-squares...