Automatic digital modulation recognition using artificial neural network and genetic algorithm
Signal Processing - Special issue on independent components analysis and beyond
Hybrid evolutionary algorithm for solving general variational inequality problems
Journal of Global Optimization
Study on fuzzy optimization methods based on quasi-linear fuzzy number and genetic algorithm
Computers & Mathematics with Applications
Computers & Mathematics with Applications
Co-evolutionary particle swarm optimization to solve constrained optimization problems
Computers & Mathematics with Applications
An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis
Applied Soft Computing
Engineering Applications of Artificial Intelligence
A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting
Information Sciences: an International Journal
Particle swarm algorithm for solving systems of nonlinear equations
Computers & Mathematics with Applications
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Data fusion of multi-sensor for IOT precise measurement based on improved PSO algorithms
Computers & Mathematics with Applications
Computers & Mathematics with Applications
A parallel hybrid optimization algorithm for fitting interatomic potentials
Applied Soft Computing
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A novel parallel hybrid intelligence optimization algorithm (PHIOA) is proposed based on combining the merits of particle swarm optimization with genetic algorithms. The PHIOA uses the ideas of selection, crossover and mutation from genetic algorithms (GAs) and the update velocity and situation of particle swarm optimization (PSO) under the independence of PSO and GAs. The proposed algorithm divides the individuals into two equation groups according to their fitness values. The subgroup of the top fitness values is evolved by GAs and the other subgroup is evolved by the PSO algorithm. The optimal number is selected as a global optimum at every circulation which shows better results than both PSO and GAs, then improves the overall performance of the algorithm. The PHIOA is used to optimize the structure and parameters of the fuzzy neural network. Finally, the experimental results have demonstrated the superiority of the proposed PHIOA to search the global optimal solution. The PHIOA can improve the error accuracy while speeding up the convergence process, and effectively avoid the premature convergence to compare with the existing methods.