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Optimization problems are ubiquitous and consequential. In fact every sphere of human activity that can be quantified can be formulated as an optimization problem. The focus of this work is on Global Optimization which is not only desirable but also necessary in many cases. In the past few decades several Global optimization algorithms have been suggested in literature out of which stochastic, population...
Mind evolutionary computation (MEC) is a new approach of evolution computation that was proposed in 1998 by Chengyi Sun. It has excellent performances on various aspects. For using MEC to optimize multi-modal functions better, studied the parameter Nm in peak radius MEC. In this paper, a two-level MEC algorithm was built, in which the high-level MEC was adopted to optimize the parameters of the low-level...
It proposed an idea of using support vector machines (SVMs) to learn the efficient set of a multiple objective discrete optimization (MODO) problem. We conjecture that a surface generated by SVM could provide a good approximation of the efficient set. As the efficient set is learned at a single SVM implementation by using a group of seeds that symbolize efficient and dominated solutions. To be able...
In order to solve the optimization problems with constraint, a constrained differential evolutionary (CDE) algorithm is presented in this paper. By introducing constraint handling based upon the differential evolutionary (DE) algorithm, the CDE algorithm can find the optimal solution within a fairly short period of time. Empirical results show that the CDE algorithm is an effective, general and robust...
In this paper, the authors propose a new evolutionary optimization i.e. synchronous bacterial foraging optimization (SBFO). The SBFO can be used for optimization of multimodal and high dimensional functions. It also enhances computational throughput and global search capability. The convergence of original BFO to the optimum value is very slow and its performance is also heavily affected with increased...
Information spread mechanism (ISM) plays an essential role in evolutionary algorithms, forming different optimization methodologies. This paper briefly analyzes some existed ISMs and proposes a novel information spread evolutionary algorithm (NISEA). The algorithm uses a special ISM aiming at diffusing partial information of an individual to accelerate the improvement of the whole individual. Two...
This paper proposes a novel optimization algorithm called cellular probabilistic optimization algorithms (CPOA) based on the probabilistic representation of solutions for real coded problems. In place of binary integers, the basic unit of information here is a probability density function. This probabilistic coding allows superposition of states for a more efficient algorithm. This probabilistic representation...
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