Particle swarmoptimization (PSO) is a heuristic global optimizationmethod, proposed originally byKennedy and Eberhart in 1995. It is now one of themost commonly used optimization techniques.This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO)...

Evolutionary Algorithms (EAs) are emerging as competitive and reliable techniques for several optimization tasks. Juxtapositioning their higher-level and implicit correspondence; it is provocative to query if one optimization algorithm can benefit from another by studying underlying similarities and dissimilarities. This paper establishes a clear and fundamental algorithmic linking between part...

This paper studies the problem of dynamic Routing and Wavelength Assignment (RWA) in Wavelength Division Multiplexed (WDM) networks with the wavelength the continuity constraint applied. A hybrid algorithm using Particle Swarm Optimization (PSO), inspired by an Ant System (AS), is used. For the routing sub-problem, particles of the swarm use the tour building approach of AS together with the ex...

Extended Kalman filter (EKF) is widely used for speed estimation in sensorless vector control of induction motor. The major and unsolved issue in the practical implementation of the EKF is the choice of the process and measurement noise covariance matrices. In this paper, a speed estimation method using EKF optimized by improved particle swarm optimization (IPSO) is proposed. By combining the a...

In this paper we propose a novel hybrid algorithm (GA/PSO) combining the strengths of particle swarm optimization with genetic algorithms to evolve the weights of recurrent neural networks. Particle swarm optimization and genetic algorithms are two optimization techniques that have proven to be successful in solving difficult problems, in particular both can successfully evolve recurrent neural...

Particle swarmoptimization (PSO) is a heuristic global optimizationmethod, proposed originally byKennedy and Eberhart in 1995. It is now one of themost commonly used optimization techniques.This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO)...

In this paper a new effective optimization algorithm called hybrid particle swarm optimizer with breeding and subpopulation is presented. This algorithm is essentially, as PSO and GA, a population-based heuristic search technique, now in use for the optimization of electromagnetic structures, modeled on the concepts of natural selection and evolution (GA) but also based on cultural and social r...

To realize the integration of process planning and scheduling (IPPS) in the manufacturing system, a particle swarm optimization (PSO) algorithm is utilized. Based on the general PSO (GPSO) model, one GPSO algorithm is projected to solve IPPS. In GPSO, crossover and mutation operations of genetic algorithm are respectively used for particles to exchange information and search randomly, and tabu ...

Sensor deployment is an important issue in designing sensor networks. In this paper, particle swarm optimization (PSO) approach is applied to maximize the coverage based on a probabilistic sensor model in mobile sensor networks and to reduce cost by finding the optimal positions for the clusterhead nodes based on a well-known energy model. During the coverage optimization process, sensors move ...

In ad hoc sensor networks, sensor nodes have very limited energy resources, thus energy consuming operations such as data collection, transmission and reception must be kept at a minimum. This paper applies particle swarm optimization (PSO) approach to optimize the coverage in ad hoc sensor networks deployment and to reduce cost by clustering method based on a wellknown energy model. Sensor nod...