Sharif Digital Repository

In this paper, an optimized control strategy is proposed for a split parallel hydraulic hybrid truck. The model of the vehicle was simulated in Simulink. According to a global optimization technique, a fuzzy control strategy is developed for the vehicle. This strategy shows flexibility for different drive cycles and a desirable fuel consumption reduction, especially for a low speed drive cycle, which is extracted according to an urban utility vehicle mission  

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... 

An efficient isogeometric-based framework is presented to integrate optimum design and formability analysis of sheet metal forming processes. To assess the quality of the formed parts, several objective functions such as fracture, wrinkling, thickness variation, and stretching are studied. In this framework, geometric parameters of addendum surfaces and middle tools are considered as design variables, the objective functions are calculated using the recently developed one-step and multi-step inverse isogeometric methods, and the optimum design variables are obtained using the genetic global optimization algorithm. The major advantage of employing the inverse methods is to analyze the... 

In this paper, a new approach based on hybrid Particle Swarm-Based- Simulated Annealing Optimization (PSO-B-SA) for solving thermal unit commitment (UC) problems is proposed. The PSO-B-SA presented in this paper solves the two sub-problems simultaneously and independently; unit-scheduled problem that determines on/off status of units and the economic dispatch problem for production amount of generating units. Problem formulation of UC is defined as minimization of total objective function while satisfying all the associated constraints such as minimum up and down time, production limits and the required demand and spinning reserve. Simulation results show that the proposed approach can... 

In this paper, a new approach for path generation and control of multi-agent systems is proposed. This method is based on global optimization techniques used for solving the inverse kinematic problem of redundant manipulators. Convergence of any performance function such as obstacle avoidance, collision avoidance, and heading angle to its global optimum is guaranteed by introducing a control law which is based on the Pontryagin's Maximum Principle. The efficacy of the proposed algorithm is demonstrated through simulation experiments. © 2006 IEEE  

Water cycle algorithm (WCA) is a population-based metaheuristic algorithm, inspired by the water cycle process and movement of rivers and streams towards sea. The WCA shows good performance in both exploration and exploitation phases. Further, the relationship between improvised exploitation and each parameter under asymmetric interval is derived and an iterative convergence of WCA is proved theoretically. In this paper, CEC’15 computationally expensive benchmark problems (i.e., 15 problems) have been considered for efficiency measurement of WCA accompanied with other optimisers. Also, a new discretisation strategy for the WCA has been proposed and applied along with other optimisers for... 

Application of the network equivalent concept for external system representation for power system transient analysis is well known. However, the challenge to utilize an equivalent network, approximated by a rational function, is to guarantee the passivity of the corresponding model. In this regard, special techniques are required to enforce the passivity of the equivalent model through a post processing approach that minimizes its impact on the original model characteristics. In this paper, the passivity is enforced by expressing the problem in terms of a convex optimization problem that guarantees the global optimal solution. The convex optimization problem is efficiently solved by recently... 

Harmony search (HS) is a new meta-heuristic optimization method imitating the music improvisation process where musicians improvise their instruments' pitches searching for a perfect state of harmony. A new variant of HS, called global-best harmony search (GHS), is proposed in this paper where concepts from swarm intelligence are borrowed to enhance the performance of HS. The performance of the GHS is investigated and compared with HS and a recently developed variation of HS. The experiments conducted show that the GHS generally outperformed the other approaches when applied to ten benchmark problems. The effect of noise on the performance of the three HS variants is investigated and a... 

This paper describes a procedure that uses particle swarm optimization (PSO) combined with the simulated annealing (SA) to analyze the bidding strategy of Generating Companies (Gencos) in an electricity market where they have incomplete information about their opponents. In the proposed methodology, Gencos prepare their strategic bids according to the Supply Function Equilibrium (SFE) model and they change their bidding strategies until Nash equilibrium points are obtained. Nash equilibrium points constitute a central solution concept in the game theory and are computed with solving a global optimization problem. In this paper a new computational intelligence technique is introduced that can... 

This paper develops an Improved harmony search (IHS) algorithm for solving optimization problems. IHS employs a novel method for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. In this paper the impacts of constant parameters on harmony search algorithm are discussed and a strategy for tuning these parameters is presented. The IHS algorithm has been successfully applied to various benchmarking and standard engineering optimization problems. Numerical results reveal that the proposed algorithm can find better solutions when compared to HS and other heuristic or deterministic methods and is a powerful search algorithm for various... 

In this paper, a new algorithm for solving the shortest loop design problem is presented. The shortest loop design problem is to find the shortest loop for an automated guided vehicle covering at least one edge of each department of a block layout. In this paper, first it is shown that this problem can be represented as a graph model. The properties of the presented model enable us to design a meta-heuristic based on ant colony system algorithm for solving the shortest loop design problem. Computational results show the efficiency of our algorithm in compare to the other techniques. © 2006 Elsevier Ltd. All rights reserved  

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... 

We consider the problem of minimizing rank of a matrix under linear and nonlinear matrix inequality constraints. This problem arises in diverse applications such as estimation, control and signal processing and it is known to be computationally NP-hard even when constraints are linear. In this paper, we first formulize the RMP as an optimization problem with linear objective and simple nonlinear semialgebraic constraints. We then proceed to solve the problem with augmented Lagrangian method known in nonlinear optimization. Despite of other heuristic and approximate methods in the subject, this method guarantees to find the global optimum in the sense that it does not depends on the choice of... 

A non-equilibrium simplex simulated annealing algorithm is applied as a global optimization method to parameter optimization for an equation of state based on the generalized statistical associating fluid theory incorporating potentials of variable range. The parameters are determined by optimizing the calculated phase behaviour of a number of pure solvents, such as water and alcohols, and aqueous electrolyte solutions. The optimized parameters obtained via the simulated annealing algorithm are compared to those obtained using the simplex method and, for the electrolyte solutions, a gradient-based quasi-Newton method. In the case of the pure solvents, the lowest values of the objective... 

In this work, a metaheuristic optimization algorithm is developed based on the idea of interaction between the demanders and the suppliers in the real estate market. After reviewing the basic theory behind the idea, the working principles of the algorithm are developed and explained in details. The proposed framework yields the exploration and exploitation ability of the algorithm and also leads the algorithm to converge to the global maxima. In order to test the performance of the algorithm, 23 well-known benchmark functions of different characteristics are selected from the literature and the results are compared with seven metaheuristic algorithms. The algorithm is also evaluated on two... 

Bayesian optimization algorithm (BOA) utilizes a Bayesian network to estimate the probability distribution of candidate solutions and creates the next generation by sampling the constructed Bayesian network. This paper proposes an improved real-coded BOA (IrBOA) for continuous global optimization. In order to create a set of Bayesian networks, the candidate solutions are partitioned by an adaptive clustering method. Each Bayesian network has its own structure and parameters, and the next generation is produced from this set of networks. The adaptive clustering method automatically determines the correct number of clusters so that the probabilistic building-block crossover (PBBC) is... 

The process of coarsening the detailed geological model of a reservoir to simulation models is known as upscaling. There are two fundamental steps in the procedure of upscaling, i.e., defining the coarse grid geometry and calculating the average properties for the generated coarse grid. In this paper, the focus will be on investigating the applicability of optimization in the context of coarse grid geometry definition. To do so, different objective function candidates will be defined, and their behavior in terms of predicting the two-phase flow accuracy of coarse grids will be analyzed to determine the proper objective function. A modified objective function employing the idea of analytical... 

Imprecise productions with low quality are produced by the incapable manufacturing processes. Prediction of the process capability in the design stage plays a key role to improve the product quality. In this paper, a new method is proposed to optimally reduce the locating error by allocating the minimum adjustments of locators. To quantify the precision of the manufacturing process, a proper tool that is called the geometric capability ratio (GCR) of the manufacturing process is introduced. First, based on a part fixture model, the relationship between the locating error and its sources is developed. Then, using the proposed geometric capability ratio, the manufacturing process capability is... 

Imprecise productions with low quality are produced by the incapable manufacturing processes. Prediction of the process capability in the design stage plays a key role to improve the product quality. In this paper, a new method is proposed to optimally reduce the locating error by allocating the minimum adjustments of locators. To quantify the precision of the manufacturing process, a proper tool that is called the geometric capability ratio (GCR) of the manufacturing process is introduced. First, based on a part fixture model, the relationship between the locating error and its sources is developed. Then, using the proposed geometric capability ratio, the manufacturing process capability is... 

This article presents a methodological approach for controller gain tuning of wind turbines using global optimization algorithms. For this purpose, the wind turbine structural and aerodynamic modeling are first described and a complete model for a 5 MW wind turbine is developed as a case study based on a systematic modeling approach. The turbine control requirements are then described and classified using its power curve to generate an appropriate control structure for satisfying all turbine control modes simultaneously. Next, the controller gain tuning procedure is formulated as an engineering optimization problem where the command tracking error and minimum response time are defined as...