Particle swarm optimization

Swarm intelligence is a term that is applied to two rather different techniques — ant colony or pheromone trail optimization and particle swarm optimization. We deal here briefly with the latter. 

Imagine a flock of starlings searching for food, which lies in a small area of the local countryside. None of the birds knows in advance where the food is located, so each of them will search for food independently, but will not move so far from the rest of the flock that they lose touch with it. By acting 

Realistic flocking behavior can be generated by constructing some simulated birds (often known as boids ), each of which obeys the rules (1) move toward other members of the flock, (2) don t crash into anything, and (3) match your speed with the boids near you. 

Each agent therefore moves in a way that is determined both by the success of its own search and the success of the entire flock. When a good solution is found, the flock moves toward it and, because many agents are present, the general area is then investigated thoroughly. 

Give particles random positions and velocities in the search space. 

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Any colony optimization (ACO) and swarm intelligence are forms of agent-based modeling inspired by colonies of social animals such as ants and bees [32]. ACO has become popular in engineering for optimal routing in water distribution systems [33, 34]. Particle swarm optimization has been successfully used to train ANNs, for instance, ANNs to predict river water levels [35], for parameter estimation, for example, in hydrology [36]. 

Chau KW (2006) Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River. J Hydrol 329 363-367... 

Gill MK, Kaheil YH, Khalil A, McKee M, Bastidas L (2006) Multiobjective particle swarm optimization for parameter estimation in hydrology. Water Resour Res 42 W07417... 

AI methods may be used in various ways. The models may be used as a standalone application, e.g., in recent work on the design of microwave absorbers using particle swarm optimization (PSO).6 Alternatively, a computational tool, such as a finite element analysis or a quantum mechanical calculation, may be combined with an AI technique, such as an evolutionary algorithm. 

AR Cockshott, BE Hartman. Improving the fermentation medium for echinocandin B production part II particle swarm optimization 36 661-669, 2001. 

J. Kennedy and R. Eberhart (1995). Particle Swarm Optimization. In Proeeedings of the 1995 IEEE international conference on neural networks, Perth, Australia. Vol VI 1942 - 1948. 

Some researchers have combined various optimization algorithms to improve the search efficiency and computational effort, including evolutionary algorithms (EA), simulated annealing (SA), particle swarm optimization (PSO), ant colony optimization (AGO), hybrid PSO-SQP, hybrid GA-ACO. Nevertheless, the combination of the GA and SQP algorithms is reported only in a few works [1,2]. 

Tang, L.-J., Zhou, Y.-P., Jiang, J.-H., Zou, H.-Y, Wu, H.-L., Shen, G.-L. and Yu, R.-Q. (2007) Radial basis function network-based transform for a nonlinear support vector machine as optimized by a particle swarm optimization algorithm with application to QSAR studies./. Chem. Inf. Model, 47,1438-1445. 

Artificial intelligence algorithms have also been embedded in docking codes, notably ant colony optimization (AGO) and particle swarm optimization (PSO) [80,... 

Shinzawa, H., Jiang, J.-H., Iwahashi, M., Noda, I. Ozaki, Y. (2007). Self-modeling Curve Resolution (SMCR) by Particle Swarm Optimization (ISO). Analytica Chimica Acta, Vol. 595, No. 1-2, pp. 275-281... 

Kennedy, J., and Eberhait, R. C. (1999). Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks, Vol. 4, Piscataway, NJ pp. 1942-1948. 

Abstract. In the present paper the problem of reuse water networks (RWN) have been modeled and optimized by the application of a modified Particle Swarm Optimization (PSO) algorithm. A proposed modified PSO method lead with both discrete and continuous variables in Mixed Integer Non-Linear Programming (MINLP) formulation that represent the water allocation problems. Pinch Analysis concepts are used jointly with the improved PSO method. Two literature problems considering mono and multicomponent problems were solved with the developed systematic and results has shown excellent performance in the optimality of reuse water network synthesis based on the criterion of minimization of annual total cost. 

Ravagnani, M.A.S.S., Silva, A.P., Biscaia Jr., E.C., CahaUero, J.A. Optimal design of sheU-and-tube heat heat exchangers using particle swarm optimization. Ind. Eng. Chem. Res. 48, 2927-2935 (2009)... 

Training a neural network model essentially means selecting one model from the set of allowed models (or, in a Bayesian framework, determining a distribution over the set of allowed models) that minimizes the cost criterion. There are numerous algorithms available for training neural network models most of them can be viewed as a straightforward application of optimization theory and statistical estimation. Recent developments in this field use particle swarm optimization and other swarm intelligence techniques. 

The particle swarm optimization (PSO) imitates the flocking behavior of birds or fish. The algorithm is based on the work of Reynolds (Reynolds 1987), where a model of decentralized behavior of herds and flocks is presented. The swarm is set up by a large number of particles, which represent the solutions. Every solution, meaning each particle, can choose its path by itself and follows three basic rales ... 

Some well-known stochastic methods for solving SOO problems are simulated annealing (SA), GAs,DE and particle swarm optimization (PSO). These were initially proposed and developed for optimization problems with bounds only [that is, unconstrained problems without Equations (4.7) and (4.8)]. Subsequently, they were extended to constrained problems by incorporating a strategy for handling constraints. One relatively simple and popular sdategy is the penalty function, which involves modifying the objective function (Equation 4.5) by the addition (in the case of minimization) of a term which depends on constraint violation. Eor example, see Equation (4.9),... 

Instead of transforming a MOO problem into a SOO problem and solving it repeatedly, researchers have modified stochastic SOO methods for solving MOO problems. MOO methods such as NSGA-n, I-MODE and Multi-objective Particle Swarm optimization (MOPSO) can generate many Pareto-optimal solutions in a single run even for problems with many... 

MOPSO Multi-objective Particle Swarm Optimization... 

Khan, M.S., Husnil, Y.A., Kwon, Y.S., and Lee, M. (2011) Automated Optimization of Process Plant using Particle Swarm Optimization. Proceedings of the 2011 4th International Symposium on Advanced Control of Industrial Processes, Thousand Islands Lake, Hangzhou. 

N.M. Kwok, Q.R Ha, D. Liu, G. Fang, Contrast enhancement and intensity preservation for gray-level images using multiobiective particle swarm optimization. IEEE Trans. Autom. Sci. Eng. 6(1), 145-155 (2009)... 

Parsopoulos, K.E. and Vrahatis, M.N. (2002) Recent Approaches to Global Optimization Problems Throu Particle Swarm Optimization, Kluwer Academic Publishers, Netherlands. 

Ferat, S., fietin, Y., Ziya, A., Onder, U., 2007. Fault diagnosis for airplane engines using Bayesian networks and distributed particle swarm optimization. Parallel Computing, Volume 33, Issue 2, Pages 124-143. 

Elegbede C. Structural safety assessment based on particles swarm optimization. 2005. Stmctural Safety. 

Method for optimization of the maintenance activities in the nuclear power plant is presented. The optimization is done with the apphcation of the modified particle swarm optimization algorithm. The safety of the system is assessed throng the mean value of the system unavailabihty calculated for discrete time points. 

The developed method was apphed on test system representing simplified high pressure injection system of the nuclear power plant. The comparison of the obtained results with the results of other two optimization algorithms is done. The obtained results show that particle swarm optimization algorithm surpass other optimization algorithms by its speed and optimal function value. 

Cai, J., Ma, X., Li, L., Haipeng, R, Chaotic particle swarm optimization for economic dispatch considering... 

Coelho, L.S., Lee, C.S., Solving economic load dispatch problems in power systems using chaotic and Gaussian particle swarm optimization approaches. International Journal of Electrical Power Energy Systems, 2008, 30(5), Pages 297-307. 

Some examples of these algorithms are the Genetic Algorithms [1], Ant Colony Optimization [2], Particle Swarm Optimization [3], DNA computing [4], among others. 

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