Optimization problems are widely encountered in various fields of mechanical engineering. Sometimes such problems can be very complex due to the actual and practical nature of the objective function or the model constraints. During the history of science of computational intelligence, many evolutionary algorithms and swarm intelligence approaches were proposed having more or less success in sol...

Many versatile and promising swarm intelligence evolutionary algorithms are being developed to solve engineering optimization problems. Although have been implemented in various fields, there is still potential for enhancement the domain of complex, electromagnetic, multimodal objective To effectively address shortcomings slow convergence speed observed both smart quantum particle (QPSO) differ...

Higher penetration of variable renewable energy sources into the grid brings down plant load factor thermal power plants. However, during sudden changes in load, plants support grid, though at higher ramping rates and with inefficient operation. Hence, further additions must be backed by battery storage systems to limit rate a avoid deploying diesel generators. In this paper, battery-integrated...

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coeffi...

Particle swarm optimization comes under lot of changes after James Kennedy and Russell Eberhart first proposes the idea in 1995. The changes has been done mainly on Inertia parameters in velocity updating equation so that the convergence rate will be higher. We are proposing a novel approach where particle’s movement will not be depend on its velocity rather it will be decided by constrained bi...

This paper aims at effectively predicting the dynamic network traffic flow based on quantum-behaved particle swarm optimization algorithm. Firstly, the dynamic network traffic flow prediction problem is analyzed through formal description. Secondly, the structure of the network traffic flow prediction model is given. In this structure, Users can used a computer to start the traffic flow predict...

In this study, we propose a novel learning approach of Radial Basis Function Neural Network (RBFNN) based on Fuzzy C-Means (FCM) and Quantum Particle Swarm Optimization (QPSO) to group similar data. The performance of RBFNN relies on the parameters such as number of hidden nodes, centres and width of Gaussian function and weight matrix between hidden layer and output layer. Generally, RBF is tr...

In this paper, we aim to solve the problem of high-rise building construction project safety risk forecasting. The main innovation of this paper lies in that we convert the project safety risk forecasting problem to a classification problem, and design a novel QPSO-SVM model to implement the classification process. Before predicting the project safety risk, we present an index system, which con...