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In terms of the concepts of state and state transition, a new algorithm-State Transition Algorithm (STA) is proposed in order to probe into classical and intelLigent optimization algorithms. On the basis of state and state transition, it becomes much simpler and easier to understand. As for continuous function optimization problems, three special operators named rotation, translation and expansion...
In order to solve the problem of linearization, complexity and poor accuracy for parameter estimate of Muskingum Routing Model at present, this paper introduces three modern intelligent algorithms - Genetic Algorithm (GA), Simulated Annealing Algorithm (SA) and Particle Swarm Optimization Algorithm (PSO) for the parameter calibration of Muskingum model. Through specific simulation, the results of...
The heuristic methods have been widely developed for solution of complicated optimization methods. Recently hybrid methods that are based on combination of different approaches have shown more potential in this regard. This paper also introduces a new method by embedding the idea of particle swarm (PS) intelligence into the well-known method of simulated annealing (SA). This way SA has been capable...
The difficulty for accurate determination of the angles of arrival (AOA) of signals arises from the optimization of likelihood functions of high dimension. Usually, a gradient-based technique is employed to find the optimum of the function. However, this method requires heavy computational work and the differentiability of the likelihood function. This paper presents two gradient-free methods: One...
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