A Survey of Swarm Intelligence Techniques in VLSI Routing Problems <s> 1) QUANTUM PARTICLE SWARM OPTIMIZATION (QPSO) <s> Particle swarm optimization is a population-based swarm intelligence algorithm based on the simulation of a social psychological metaphor instead of the survival of the fittest individual paradigm. Inspired by the classical particle swarm method and quantum mechanics theories, this work presents a new quantum-behaved approach using a mutation operator with exponential probability distribution. The simulation results demonstrate good performance of the proposed algorithm in solving a significant benchmark problem in electromagnetics, namely the TEAM workshop benchmark problem 22. <s> BIB001 </s> A Survey of Swarm Intelligence Techniques in VLSI Routing Problems <s> 1) QUANTUM PARTICLE SWARM OPTIMIZATION (QPSO) <s> This article uses Artificial Neural Network (ANN) trained with Quantum behaved Particle Swarm Optimization (QPSO) for the problem of equalization. Though the use of PSO in training of ANN finds optimal weights of the network it fails in the design of appropriate topology. But, QPSO is capable of optimizing the network topology. Here, parameters like neurons in each layer, the number of layers etc. are optimized using QPSO. Then, this QPSO tuned ANN then applied to the problem of channel equalization. The superior performance of proposed equalizer is proved through simulations. <s> BIB002 </s> A Survey of Swarm Intelligence Techniques in VLSI Routing Problems <s> 1) QUANTUM PARTICLE SWARM OPTIMIZATION (QPSO) <s> This paper presents a BDI (Belief-Desire-Intention)-agent-based Quantum-Behaved Particle Swarm Optimisation (QPSO) reconfiguration method for shipboard zonal power systems. Shipboard zonal power systems are founded on navy ships, and desired to be highly reconfigurable. Since shipboard power system reconfiguration may change its topology, and load priority should be taken into consideration, this makes shipboard reconfiguration into a non-linear distributed optimisation problem. Specially, switches in the shipboard zonal power system are modelled as intelligent BDI agents. This paper uses swarm intelligence to realise BDI-agent reasoning and optimise its reconfiguration objective. To verify the effectiveness of the proposed approach, comparative simulations are conducted on the method without reconfiguration and reconfiguration method based on PSO/QPSO. Simulation results show that the reconfiguration strategy can achieve success in realising shipboard power system service restoration cases which demonstrate the feasibility and the advantages of the proposed BDI-agent-based QPSO reconfiguration method. <s> BIB003
In the PSO algorithm, the motion state of a particle is described by position and velocity. With the evolution of time, the motion track of the particle is fixed. At the same time, the velocity of the particle is limited, so that the search space is a finite and gradually decreasing region, and global convergence cannot be guaranteed. Therefore, QPSO based on δ-potential well model  is proposed and becomes the most widely researched and used version. In QPSO, all particles have quantum behavioral properties and no longer follow the Newtonian equations for the motion state of particles in PSO, which is a deterministic description. Compared with PSO, QPSO uses a simpler position-only moving model with fewer control parameters. More importantly, particles of a quantum system can appear anywhere at a certain probability distribution to achieve global search. In recent years, QPSO has been successfully applied in various fields such as neural network BIB002 , power systems BIB003 , electronics and electromagnetics BIB001 . Therefore, QPSO has an opportunity to better solve VLSI routing problems that PSO can solve.