An improved quantum-behaved particle swarm optimization algorithm
ABSTRACT Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithm, which shows good search ability in many optimization problems. In this paper, we present an improved QPSO algorithm, called IQPSO, by combining QPSO and an opposition-based learning concept. Experimental studies on four well-known benchmark problems show that IQPSO achieves better results than QPSO and other variants of PSO on majority of test problems.
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ABSTRACT: This paper presents two techniques for DC model parameter extraction for a Gallium Arsenide (GaAs) based MEtal Semiconductor Field Effect Transistor (MESFET) device. The proposed methods uses Particle Swarm Optimization (PSO) and Quantum Particle Swarm Optimization (QPSO) methods for optimizing the difference between measured data and simulated data. Simulated data are obtained by using four different popular DC models. These techniques avoid complex computational steps involved in traditional parameter extraction techniques. The performance comparison in terms of quality of solution and execution time of classical PSO and QPSO to extract the model parameters are presented. The validity of this approach is verified by comparing the simulated and measured results of a fabricated GaAs MESFET device with gate length of 0.7 μm and gate width of 600 μm (4 × 150). Simulation results indicate that both the technique based on PSO and QPSO accurately extracts the model parameters of MESFET.Microelectronics Reliability 06/2009; · 1.21 Impact Factor
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ABSTRACT: Particle swarm optimization (PSO) is an evolutionary algorithm used extensively. This paper presented a new particle swarm optimizer based on evolutionary game (EGPSO). We map particles’ finding optimal solution in PSO algorithm to players’ pursuing maximum utility by choosing strategies in evolutionary games, using replicator dynamics to model the behavior of particles. And in order to overcome premature convergence a multi-start technique was introduced. Experimental results show that EGPSO can overcome premature convergence and has great performance of convergence property over traditional PSO.Journal of Computational and Applied Mathematics 04/2008; 214(1):30-35. · 1.08 Impact Factor
Conference Paper: Comparison between Genetic Algorithms and Particle Swarm Optimization.[Show abstract] [Hide abstract]
ABSTRACT: This paper compares two evolutionary computation paradigms: genetic algorithms and particle swarm optimization. The operators of each paradigm are reviewed, focusing on how each affects search behavior in the problem space. The goals of the paper are to provide additional insights into how each paradigm works, and to suggest ways in which performance might be improved by incorporating features from one paradigm into the other.Evolutionary Programming VII, 7th International Conference, EP98, San Diego, CA, USA, March 25-27, 1998, Proceedings; 01/1998