Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives

This paper presents a hybrid differential evolution (DE) with quantum-behaved particle swarm optimization (QPSO) for the unmanned aerial vehicle (UAV) route planning on the sea. The proposed method, denoted as DEQPSO, combines the DE algorithm with the QPSO algorithm in an attempt to further enhance the performance of both algorithms. The route planning for UAV on the sea is formulated as an optimization problem. A simple method of pretreatment to the terrain environment is proposed. A novel route planner for UAV is designed to generate a safe and flyable path in the presence of different threat environments based on the DEQPSO algorithm. To show the high performance of the proposed method, the DEQPSO algorithm is compared with the real-valued genetic algorithm, DE, standard particle swarm optimization (PSO), hybrid particle swarm with differential evolution operator, and QPSO in terms of the solution quality, robustness, and the convergence property. Experimental results demonstrate that the proposed method is capable of generating higher quality paths efficiently for UAV than any other tested optimization algorithms.

Route Planning for Unmanned Aerial Vehicle (UAV) on the Sea Using Hybrid Differential Evolution and Quantum-Behaved Particle Swarm Optimization

Optimal deployment and accurate localization of sensor nodes have a strong influence on the performance of a wireless sensor network (WSN). This paper considers real-time autonomous deployment of sensor nodes from an unmanned aerial vehicle (UAV). Such a deployment has importance, particularly in ad hoc WSNs, for emergency applications, such as disaster monitoring and battlefield surveillance. The objective is to deploy the nodes only in the terrains of interest, which are identified by segmentation of the images captured by a camera on board the UAV. Bioinspired algorithms, particle swarm optimization (PSO) and bacterial foraging algorithm (BFA), are presented in this paper for image segmentation. In addition, PSO and BFA are presented for distributed localization of the deployed nodes. Image segmentation for autonomous deployment and distributed localization are formulated as multidimensional optimization problems, and PSO and BFA are used as optimization tools. Comparisons of the results of PSO and BFA for autonomous deployment and distributed localization are presented. Simulation results show that both the algorithms perform multilevel image segmentation faster than the exhaustive search for optimal thresholds. Besides, PSO-based localization is observed to be faster, and BFA-based localization is more accurate.

Bio-inspired Algorithms for Autonomous Deployment and Localization of Sensor Nodes

In this paper we focus our attention on the long-term load forecasting problem, that is the prediction of energy consumption for several months ahead (up to one or more years), useful in order to ease the proper scheduling of operative conditions (such as the planning of fuel supply) While several effective techniques are available in the short-term framework, no reliable methods have been proposed for long-term predictions For this purpose, we describe in this work a new procedure, which exploits the Empirical Mode Decomposition method to disaggregate a time series into two sets of components, respectively describing the trend and the local oscillations of the energy consumption values These sets are then used for training Support Vector Regression models The experimental results, obtained both on a public-domain and on an office building dataset, allow to validate the effectiveness of the proposed method

Energy Load Forecasting Using Empirical Mode Decomposition and Support Vector Regression

Conventional derivative based learning rule poses stability problem when used in adaptive identification of infinite impulse response (IIR) systems. In addition the performance of these methods substantially deteriorates when reduced order adaptive models are used for such identification. In this paper the IIR system identification task is formulated as an optimization problem and a recently introduced cat swarm optimization (CSO) is used to develop a new population based learning rule for the model. Both actual and reduced order identification of few benchmarked IIR plants is carried out through simulation study. The results demonstrate superior identification performance of the new method compared to that achieved by genetic algorithm (GA) and particle swarm optimization (PSO) based identification.

IIR system identification using cat swarm optimization

In this paper, swarm and evolutionary algorithms have been applied for the design of digital filters. Particle swarm optimization (PSO) and differential evolution particle swarm optimization (DEPSO) have been used here for the design of linear phase finite impulse response (FIR) filters. Two different fitness functions have been studied and experimented, each having its own significance. The first study considers a fitness function based on the passband and stopband ripple, while the second study considers a fitness function based on the mean squared error between the actual and the ideal filter response. DEPSO seems to be promising tool for FIR filter design especially in a dynamic environment where filter coefficients have to be adapted and fast convergence is of importance.

/pdf/differential-evolution-particle-swarm-optimization-for-5aqiy8sykh.pdf

Differential evolution particle swarm optimization for digital filter design

http://brain2grid.com/wp-content/uploads/2009/12/quantum-inspired-pso.pdf

Neural networks letter: Quantum inspired PSO for the optimization of simultaneous recurrent neural networks as MIMO learning systems

In this paper, particle swarm optimization with quantum infusion (PSO-QI) has been applied for the design of digital filters. In PSO-QI, Global best (gbest) particle (in PSO star topology) obtained from particle swarm optimization is enhanced by doing a tournament with an offspring produced by quantum behaved PSO, and selecting the winner as the new gbest. Filters are designed based on the best approximation to the ideal response by minimizing the maximum ripples in passband and stopband of the filter response. PSO-QI, as is shown in the paper, converges to a better fitness. This new algorithm is implemented in the design of finite impulse response (FIR) and infinite impulse response (IIR) filter.

/pdf/particle-swarm-optimization-with-quantum-infusion-for-the-5446t8vjpz.pdf

Particle Swarm Optimization with Quantum Infusion for the design of digital filters

Cellular neural networks (CNNs), as previously described, consist of identical units called cells that are connected to their adjacent neighbors. These cells interact with each other in order to fulfill a common goal. The current methods involved in learning of CNNs are usually centralized (cells are trained in one location) and synchronous (all cells are trained simultaneously either sequentially or in parallel depending on the available hardware/software platform). In this paper, a generic architecture of CNNs is presented and a special case of supervised learning is demonstrated explaining the internal components of a cell. A decentralized asynchronous learning (DAL) framework for CNNs is developed in which each cell of the CNN learns in a spatially and temporally distributed environment. An application of DAL framework is demonstrated by developing a CNN-based wide-area monitoring system for power systems. The results obtained are compared against equivalent traditional methods and shown to be better in terms of accuracy and speed.

/pdf/decentralized-asynchronous-learning-in-cellular-neural-3f0q4817z7.pdf

Decentralized Asynchronous Learning in Cellular Neural Networks

Cellular computational networks-A scalable architecture for learning the dynamics of large networked systems

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