This paper establishes the models of the steelmaking continuous casting (SCC) process, and proposed the improved algorithms for this problem. The simulation results of a computerized scheduling system are also given to prove the model. The SCC process scheduling problem is very difficult to get a good performance solution in practice. The scheduling of the SCC process requires that each cast pl...

A novel optimization concept for modeling irregular-shaped patch antenna with high bandwidth and efficient radiation attributes is proposed in this paper, along the ability to accomplish design at a reduced computational cost burden. revolutionary computing perception established Gravitational Search Algorithm (GSA) Quantum Based Delta Particle Swarm Optimization (QPSO), now known as GSA-QPSO. ...

A novel multi-class classification method for bacteria detection termed quantum-behaved particle swarm optimization-based kernel extreme learning machine (QPSO-KELM) based on an electronic nose (E-nose) technology is proposed in this paper. Time and frequency domain features are extracted from E-nose signals used for detecting four different classes of wounds (uninfected and infected with Staph...

DNA (deoxyribonucleic acid) computing that is a new computation model based on DNA molecules for information storage has been increasingly used for optimization and data analysis in recent years. However, DNA computing algorithm has some limitations in terms of convergence speed, adaptability, and effectiveness. In this paper, a new approach for improvement of DNA computing is proposed. This ne...

Linear Quadratic Regulator (LQR) is an optimal multivariable feedback control approach that minimizes the excursion in state trajectories of a system while requiring minimum controller effort. The behaviour of a LQR controller is determined by two parameters: state and control weighting matrices. These two matrices are main design parameters to be selected by designer and greatly influence the ...

In this study, a quantum-behaved particle swarm optimization (QPSO) based on hybrid evolution (HEQPSO) approach is proposed to estimate parameters of chaotic dynamic systems, in which the proposed HEQPSO algorithm combines the conceptions of genetic algorithm (GA) and adaptive annealing learning algorithm with the QPSO algorithm. That is, the mutation strategy in GA is used for conquering prema...

In cognitive radio network model consisting of secondary users and primary users, in order to solve the difficult multi-objective spectrum allocation issue about maximizing network efficiency and users’ fairness to access network, this paper proposes a new discrete multi-objective combinatorial optimization mechanism— HJ-DQPSO based on Hooke Jeeves (HJ) and Quantum Particle Swarm Optimization (...

Particle swarm optimization (PSO) algorithm is population-based heuristic global search algorithm inspired by social behavior patterns of organisms that live and interact within large groups. The PSO is based on researches on swarms such as fish schooling and bird flocking. Inspired by the classical PSO method and quantum mechanics theories, this work presents a quantum-inspired version of the ...

This research uses the improved Quantum Particle Swarm Optimization (QPSO) algorithm to build an Internet of Things (IoT) life comfort monitoring system based on wireless sensing networks. The purpose is improve quality intelligent life. functions include automatic basketball court lighting system, infants’ sleeping posture and accidental falls elderly, human thermal measurement other related s...