Multi-knowledge extraction is significant for many real-world applications. The nature inspired population-based reduction approaches are attractive to find multiple reducts in the decision systems, which could be applied to generate multi-knowledge and to improve decision accuracy. In this Chapter, we introduce two nature inspired populationbased computational optimization techniques namely Pa...

to achieve an excellent thermal-mechanical performance of cmcs, it is necessary to analyze and design the thickness of the multi-layered interphases for an optimized trs distribution. an optimization was performed with a new version of the particle swarm optimization, the bsg-starcraft radius pso linked to a finite element software.

Back propagation(BP) is used to solve real world problems which use the concept of multilayer perceptron(MLP). BP have the disadvantage of trapped in local minima, slow convergence rate and more error prone. To optimize BP Algorithm, Particle swarm optimization(PSO) and Genetic algorithm(GA) is used. Limitation of PSO_Hill and PSO_A* is overcomes when these algorithms are combined and on the ba...

Watermark perceptibility and robustness must be ensured in order to enhance the practicality of digital watermarking. Taking human visual system characteristics into account fully, a particle swarm optimization method based on multi-wavelet digital watermarking is proposed. It utilizes Sa4 multiwavelet to embed digital watermark, chooses intermediate frequency in embedding digital watermark ada...

Training neural networks is a complex task that is important for supervised learning. A few metaheuristic optimization techniques have been applied to increase the effectiveness of the training process. The Cuckoo Search (CS) algorithm is a recently developed meta-heuristic optimization algorithm which is suitable for solving optimization problems. In this paper, Cuckoo search is implemented in...

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...

Aiming at industrial organization multi-objective optimization problem in Equipment Manufacturing Industry, The paper proposes a new type of double layer evolutionary cultural particle swarm optimization algorithm. The algorithm combines the advantages of the particle swarm optimization algorithm and cultural algorithm. It not only revises the problem that the particles are easy to “premature”,...

in this paper an approach based on evolutionary algorithms to find pareto optimal pair of state and control for multi-objective optimal control problems (moocp)'s is introduced‎. ‎in this approach‎, ‎first a discretized form of the time-control space is considered and then‎, ‎a piecewise linear control and a piecewise linear trajectory are obtained from the discretized time-control space using ...

Statistical models have been used to estimate the refractive index of 72 imidazolium-based ionic liquids using the electronic polarisability of their ions as the data for two different mathematical approaches: artificial neural networks, in the form of multi-layer perceptrons, and multiple linear regression models. Although the artificial neural networks and linear models have been able to acco...

This paper presents a multi-agent search technique to design an optimal composite box-beam helicopter rotor blade. The search technique is called particle swarm optimization (‘inspired by the choreography of a bird flock’). The continuous geometry parameters (cross-sectional dimensions) and discrete ply angles of the box-beams are considered as design variables. The objective of the design prob...