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Many real world optimization problems are dynamic in which global optimum and local optima change over time. Particle swarm optimization has performed well to find and track optima in dynamic environments. In this paper, we propose a new particle swarm optimization algorithm for dynamic environments. The proposed algorithm utilizes a parent swarm to explore(More)
— many problems in the real world are dynamic in which the environment changes. However, the nature itself provides solutions for adaptation to these changes in order to gain the maximum benefit, i.e. finding the global optimum, at any moment. One of these solutions is hibernation of animals when food is scarce and an animal may use more energy in searching(More)
— Recommendation systems aim at directing users toward the resources that best meet their needs and interests. In this paper, we propose a new recommendation algorithm based on a hybrid method of distributed learning automata and graph partitioning. The proposed method utilizes usage data and hyperlink graph of the web site. The idea of the proposed method(More)
PSO, like many stochastic search methods, is very sensitive to efficient parameter setting such that modifying a single parameter may cause a considerable change in the result. In this paper, we study the ability of learning automata for adaptive PSO parameter selection. We introduced two classes of learning automata based algorithms for adaptive selection(More)
In real life we are often confronted with dynamic optimization problems whose optima change over time. These problems challenge traditional optimization methods as well as conventional evolutionary optimization algorithms. In this paper, we propose an evolutionary model that combines the differential evolution algorithm with cellular automata to address(More)
— Determining similarity between web pages is a key factor for the success of many web mining applications such as recommendation systems and adaptive web sites. In this paper, we propose a new hybrid method of distributed learning automata and graph partitioning to determine similarity between web pages using the web usage data. The idea of the proposed(More)
In this paper, we propose Alpinist CellularDE to address dynamic optimization problems. Alpinist CellularDE tries to detect different regions of the landscape and uses this information to perform more effective search and increase its performance. Moreover, in Alpinist CellularDE a directed local search is proposed to track local optima after detecting a(More)
— Many real world optimization problems are dynamic in which the landscape is time dependent and the optimums may change over time such as dynamic economic modeling, dynamic resource scheduling and dynamic vehicle routing. These problems challenge traditional optimization methods as well as conventional evolutionary optimization algorithms. In these(More)
Background and Objectives. The aim of this study was to determine the frequency of bla NDM, bla PER, bla VEB, bla IMP, and bla VIM type genes among A. baumannii isolates from hospitalized patients in two hospitals in Tehran, Iran. Patients and Methods. Antibiotic susceptibility tests were performed by Kirby-Bauer disc diffusion and Broth microdilution(More)