Determining the optimal location of wells with the aid of an automated search algorithm is a significant and difficult step in the reservoir development process. It is a computationally intensive task due to the large number of simulation runs required. Therefore,the key issue to such automatic optimization is development of algorithms that can find acceptable solutions with a minimum numbe...

Broadcasting antenna array null filling is a very challenging problem for antenna design optimization. This paper compares five antenna design optimization algorithms (Differential Evolution, Particle Swarm, Taguchi, Invasive Weed, Adaptive Invasive Weed) as solutions to the antenna array null filling problem. The algorithms compared are evolutionary algorithms which use mechanisms inspired by ...

with the rapid development of the internet, the amount of information and data which are produced, are extremely massive. hence, client will be confused with huge amount of data, and it is difficult to understand which ones are useful. data mining can overcome this problem. while data mining is using on cloud computing, it is reducing time of processing, energy usage and costs. as the speed of ...

Differential Evolution (DE) is a stochastic, population based search technique, which can be classified as an Evolutionary Algorithm (EA) using the concepts of selection crossover and reproduction to guide the search. It has emerged as a powerful tool for solving optimization problems in the past few years. However, the convergence rate of DE still does not meet all the requirements, and attemp...

The main purpose of this paper is to solve an inverse random differential equation problem using evolutionary algorithms. Particle Swarm Algorithm and Genetic Algorithm are two algorithms that are used in this paper. In this paper, we solve the inverse problem by solving the inverse random differential equation using Crank-Nicholson's method. Then, using the particle swarm optimization algorith...

This paper proposes a novel and unconventional Memetic Computing approach for solving continuous optimization problems characterized by memory limitations. The proposed algorithm, unlike employing an explorative evolutionary framework and a set of local search algorithms, employs multiple exploitative search within the main framework and performs a multiple step global search by means of a rand...

In this study, the performance of the algorithms of whale, Differential evolutionary, crow search, and Gray Wolf optimization were evaluated to operate the Golestan Dam reservoir with the objective function of meeting downstream water needs. Also, after defining the objective function and its constraints, the convergence degree of the algorithms was compared with each other and with the absolut...

Differential evolution (DE) algorithms compose an efficient type of evolutionary algorithm (EA) for the global optimization domain. But DE is not completely free from the problems of slow and/or premature convergence. In this paper, an improving clustering-based differential evolution with chaotic sequences and new mutation operator (CCDE) is proposed for the unconstrained global optimization p...

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). Over the last few decades, a number of Differential Evolution (DE) algorithms have been proposed with excellent performance on mathematical benchmarks. However, li...

Parameter identification of system models is a fundamental step in the process of designing a controller for a system. In control engineering, a wide selection of analytic identification techniques exists for linear systems, but not for non-linear systems. Instead, the model parameters may be determined by an optimization algorithm by minimizing the error between model output and measured data....