In view of the shortcomings of the test data generation algorithm including particle swarm optimization algorithm and ant colony algorithm, a new algorithm is proposed, which is based on the combination of particle swarm algorithm and parameter adjustment. This algorithm can dynamically adjust its search capabilities based on the fitness value of particles , combine the advantages of particle s...

Partial Transmit Sequence technique is one of the most popular technique for PAPR reduction in OFDM. But in this scheme the complexity for searching the phase factor is increased with increase in number of sub blocks. This paper describes the different types of optimization methods used in PTS scheme, for optimization of phase factor & reducing the searching complexity .Also we can analyze thei...

In this paper, two nature inspired meta heuristic approaches particle swarm optimization and firefly algorithm are discussed. Both the approaches are population based approaches and has wide applications in various problems. Various factors influencing its performance is compared on the basis of selection of size of population, number of iterations, quality of solution, convergence criterion an...

It is significant to discover a global optimization in the problems dealing with large dimensional scales to increase the quality of decision-making in the mining operation. It has been broadly confirmed that the long-term production scheduling (LTPS) problem performs a main role in mining projects to develop the performance regarding the obtainability of constraints, while maximizing the whole...

A number of algorithms based on the evolutionary processing have been proposed forcommunication networks backbone such as Genetic Algorithm (GA). However, there has beenlittle work on the SWARM optimization algorithms such as Particle Swarm Optimization(PSO) for backbone topology design. In this paper, the performance of PSO on GA isdiscussed and a new algorithm as PSOGA is proposed for the net...

The basic thought of particle swarm optimization is introduced firstly, then particle swarm optimization algorithm model is established. The application of the improved particle swarm optimization algorithm to power supply system fault diagnosis is analyzed in accordance with problem of the algorithm, and migration strategy is added to particle swarm optimization algorithm. Finally the paramete...

The job-shop scheduling problem has attracted many researchers’ attention in the past few decades, and many algorithms based on heuristic algorithms, genetic algorithms, and particle swarm optimization algorithms have been presented to solve it, respectively. Unfortunately, their results have not been satisfied at all yet. In this paper, a new hybrid swarm intelligence algorithm consists of par...

Genetic Algorithm (GA), Ant Colony Optimization (ACO) algorithm and Particle Swarm Optimization (PSO) are proposed for feature selection, and their performance is compared. The Spatial Gray Level Dependence Method (SGLDM) is used for feature extraction. The selected features are fed to a three-layer Backpropagation Network hybrid with Ant Colony Optimization and Particle Swarm Optimization (BPN...

In this paper, we propose a novel algorithm to enhance the noisy speech in the framework of dual-channel speech enhancement. The new method is a hybrid optimization algorithm, which employs the  combination of  the  conventional θ-PSO and the shuffled sub-swarms particle optimization (SSPSO) technique. It is known that the θ-PSO algorithm has better optimization performance than standard PSO al...