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A new optimization algorithm, namely the Forest Algorithm (FA), is introduced for the first time. This algorithm simulates trees' growth, reproduction and death in a forest to perform optimization. In the algorithm, trees and branches represent a collection of trial solutions and parameters needed to be optimized respectively, and three mechanisms, i.e. Growth, proliferation and death, are employed...
The improvised Particle Swarm Optimization (PSO) Algorithm offers better search efficiency than conventional PSO algorithm. It provides an efficient technique to obtain the best optimized result in the search space. This algorithm ensures a faster rate of convergence to the desired solution whose precision can be preset by the user. The inertia parameter is varied linearly with iteration number, which...
This letter addresses the application of metaheuristics to the single-snapshot direction-of-arrival estimation. The performances of five of the most popular optimization algorithms—Particle Swarm Optimization, Ant Colony Optimization for Continuous Domains, Simulated Annealing, Genetic Algorithms, and Differential Evolution—are compared in terms of convergence, accuracy, resolution, and computational...
This paper proposes a method of target location based on the adaptive particle swarm optimization algorithm in view of the shortcoming that the current localization solution is complex, and the standard particle swarm optimizer algorithm has low convergence rate and is easy to be trapped in local optimum. In the algorithm, the adaptive inertia weight can balance global and local search ability, and...
Hidden Markov model (HMM) is currently the most popular approach to speech recognition. The problem of optimizing model parameters is of great interest to the researchers in this area. The Baum-Welch (BW) algorithm is very popular estimation method due to its reliability and efficiency. However, it is easily trapped in local optimum. particle swarm optimization (PSO) algorithm is a stochastic global...
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