the main purpose in this paper, to express strong algorithmic optimization in their ability to detect expression target of sar that use in aircraft, plane and satellites to observe the target on the ground. for this purpose,the sar technology and its applications are introduced. previous algorithms used in this field with poor diagnosis and appropriate speed is low, like means and fuzzy and pso...

The study and research of evolutionary algorithms (EAs) is getting great attention in recent years. Although EAs have earned extensive acceptance through numerous successful applications in many fields, the problem of finding the best combination of evolutionary parameters especially for population size that need the manual settings by the user is still unresolved. In this paper, our system is ...

Many real-world optimization problems are large-scale in nature. In order to solve these problems, an optimization algorithm is required that is able to apply a global search regardless of the problems’ particularities. This paper proposes a self-adaptive differential evolution algorithm, called jDElscop, for solving large-scale optimization problems with continuous variables. The proposed algo...

In this report, AI-BL1.0, an open-source Labview-based program for automatic on-line beamline optimization, is presented. The optimization algorithms used in the program are Genetic Algorithm and Differential Evolution. Efficiency was improved by use of a strategy known as Observer Mode for Evolutionary Algorithm. The program was constructed and validated at the XAFCA beamline of the Singapore ...

this paper focuses on multi-objective designing of multi-machine thyristor controlled series compensator (tcsc) using strength pareto evolutionary algorithm (spea). the tcsc parameters designing problem is converted to an optimization problem with the multi-objective function including the desired damping factor and the desired damping ratio of the power system modes, which is solved by a spea ...

In this paper, we proposed an algorithm for solving the problem of task scheduling using particle swarm optimization algorithm, with changes in the Selection and removing the guide and also using the technique to get away from the bad, to move away from local extreme and diversity. Scheduling algorithms play an important role in grid computing, parallel tasks Scheduling and sending them to ...

This paper presents a differential evolution algorithm to solve continuous function optimization problems. The algorithm was tested using 14 newly proposed benchmark instances in Congress on Evolutionary Computation 2005. For these benchmark problems, the problem definition files, codes and evaluation criteria are available in http://www.ntu.edu.sg/home/EPNSugan. Since these benchmarks are newl...

Differential Evolution (DE) is arguably one of the most powerful stochastic real parameter optimization algorithms in current use. DE operates through the similar computational steps as employed by a standard Evolutionary Algorithm (EA). However, unlike the traditional EAs, the DEvariants perturb the current-generation population members with the scaled differences of randomly selected and dist...

0020-0255/$ see front matter 2011 Elsevier Inc doi:10.1016/j.ins.2011.09.001 ⇑ Corresponding author. E-mail address: [email protected] (Y. Wang). Differential evolution (DE) is a class of simple yet powerful evolutionary algorithms for global numerical optimization. Binomial crossover and exponential crossover are two commonly used crossover operators in current popular DE. It is noteworthy that...

Theoretical analysis of the dynamics of evolutionary algorithms is believed to be very important to understand the search behavior of evolutionary algorithms and to develop more efficient algorithms. In this paper we investigate the dynamics of a canonical Differential Evolution (DE) algorithm with DE/rand/1 type mutation and binomial crossover. Differential Evolution (DE) is well-known as a si...