نتایج جستجو برای: imperialist competitive algorithm ica
تعداد نتایج: 840090 فیلتر نتایج به سال:
Imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) are two approaches for determining the solution of any objective function, but they use different strategies. Therefore, a comparison of their performance is required. The comparison is made on the basis of test functions, and then both techniques are applied to an eddy current non-destructive evaluation system to rec...
It’s of great significance for wind power integration into grid to forecast wind power. Based on forecasting wind power by BP neural network, the article introduces global optimization algorithm, Imperialist Competitive Algorithm (ICA) to provide optimized initial weights of BP neural network. Thus, it can overcome the entrapment in local optical optimum of BP neural network. Compared with BP n...
Cloud computing has become a universal trend now. So, for users, the reliability is an effective factor to use this technology. In addition, users prefer to implement and get their work done quickly. This paper takes into account these two parameters for resource allocation due to their importance In this method, the Imperialist Competitive algorithm with the addition of a cross layer of cloud ...
The article presents the hybrid metaheuristic-neural assessment of the pull-off adhesion in existing multi-layer cement composites using artificial neural networks (ANNs) and the imperialist competitive algorithm (ICA). The ICA is a metaheuristic algorithm inspired by the human political-social evolution. This method is based solely on the use of ANNs and two non-destructive testing (NDT) metho...
In this paper, optimization of the backstepping controller parameters in a grid-connected single-phase inverter is studied using Imperialist competitive algorithm (ICA), Genetic Algorithm (GA) and Particle swarm optimization (PSO) algorithm. The controller is developed for the system based on state-space averaged model. By selection of a suitable Lyapunov function, stability of the proposed con...
In engineering, flood routing is an important technique necessary for the solution of a floodcontrol problem and for the satisfactory operation of a flood-prediction service. A simple conceptual model like the Muskingum model is very effective for the flood routing process. One challenge in application of the Muskingum model is that its parameters cannot be measured physically. In this article ...
This paper presents an implementation of the Imperialist Competitive Algorithm (ICA) for solving the fuzzy random portfolio selection problem where the asset returns are represented by fuzzy random variables. Portfolio Optimization is an important research field in modern finance. By using the necessity-based model, fuzzy random variables reformulate to the linear programming and ICA will be de...
The human has always been to up to the best in everything, And this perfectionism has led to the creation of optimization methods. The goal of optimization is to determine the variables to find the best acceptable answer to the limitations in a problem, so that the objective function is a minimum or a maximum. Metaheuristic algorithms are one of inaccurate optimization methods that inspired by ...
The integrated product mix-outsourcing optimization is a major problem in manufacturing enterprise. Generally, heuristic or meta-heuristic solution approaches are used to optimize such problems. Heuristic approaches for these problems include Theory of Constraints (TOC) and Standard Accounting. Sometimes heuristic approaches are inefficient especially in large problems and instead, in these cas...
Imperialist Competitive Algorithm (ICA) is a new population-based evolutionary algorithm. It divides its population of solutions into several sub-populations, and then searches for the optimal solution through two operations: assimilation and competition. The assimilation operation moves each non-best solution (called colony) in a sub-population toward the best solution (called imperialist) in ...
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