Traditional optimization methods are not well suitable for thinning large arrays to obtain a low sidelobe level (SLL). The chaotic binary particle swarm optimization (CBPSO) algorithm is presented as a useful alternative for the synthesis of thinned arrays. The proposed algorithm can be improved by nonlinear inertia weight with chaotic mutation to increase the diversity of particles. Two exampl...

The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the ...

Particle swarm optimization algorithm is easy to reach premature convergence in the solution process, and fall into the local optimal solution. Aiming at the problem, this paper proposes a particle swarm optimization algorithm with chaotic mapping (CM-PSO). The algorithms uses chaotic mapping function to optimize the initial state of population, improve the probability of obtain optimal solutio...

clustering is the process of dividing a set of input data into a number of subgroups. the members of each subgroup are similar to each other but different from members of other subgroups. the genetic algorithm has enjoyed many applications in clustering data. one of these applications is the clustering of images. the problem with the earlier methods used in clustering images was in selecting in...

Abstract: This study proposes a novel chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm for solving shortterm hydrothermal scheduling problem with a set of equality and inequality constraints. In the proposed method, chaotic local search technique is employed to enhance the local search capability and convergence rate of the algorithm. In addition, a novel constraint handlin...

Parameter estimation for fractional-order chaotic systems is an important issue in fractional-order chaotic control and synchronization and could be essentially formulated as a multidimensional optimization problem. A novel algorithm called quantum parallel particle swarm optimization (QPPSO) is proposed to solve the parameter estimation for fractional-order chaotic systems. The parallel charac...

This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust t...