Particles swarm

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. 

Goudos, S.K. and Sahalos, J.N., Microwave absorber optimal design using multiobjective particle swarm optimisation, Microwave Optic. Tech. Letts., 48, 1553, 2006. 

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. 

This result can also be applied directly to coarse particle swarms. For fine particle systems, the suspending fluid properties are assumed to be modified by the fines in suspension, which necessitates modifying the fluid properties in the definitions of the Reynolds and Archimedes numbers accordingly. Furthermore, because the particle drag is a direct function of the local relative velocity between the fluid and the solid (the interstitial relative velocity, Fr), it is this velocity that must be used in the drag equations (e.g., the modified Dallavalle equation). Since Vr = Vs/(1 — Reynolds number and drag coefficient for the suspension (e.g., the particle swarm ) are (after Barnea and Mizrahi, 1973) ... 

There are methods that deliberately avoid the use of gradient and Hessian information. Such approaches typically require many more iterations but can nevertheless save overall on computation. Some popular ones are the Simplex Method, Genetic Algorithms, Simulated Annealing, Particle Swarm and Ant Colony Optimization, and variants thereof. 

Thomson s plum-pudding model of the atom. Thomson proposed that the atom might be made of thousands of tiny, negatively charged particles swarming within a cloud of positive charge, much like plums and raisins in an old-fashioned Christmas plum pudding. 

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]. 

Fig. 5.4 shows the effect of the dynamic viscosity /z upon Although the state of flow in the tank is turbulent, the viscosity of the medium is important, because it affects the sinking velocity of the particle swarm iVss. The particle Reynolds number Rep formulated with Wss and dp ties in the laminar to transition ranges. 

On the basis of the analogy between no.9 and dp (Fig. 5.5), on the one hand, and the sinking velocity of the particle swarm Wss and dp, on the other, the sinking velocity of the swarm u>gs was later incorporated into the relevance list. This consciously took account of the fact, that this property of the particle swarm is calculated from the sinking velocity Wg of a single particle in a liquid at rest and thus strictly speaking only applies to a liquid at rest. 

The study carried out by Geisler et al. [152], which considered the dimensioning of the specific stirrer power per unit mass of the suspension e = FlpV, indicated the paramount importance of the D/dp parameter. In industrially-sized tanks (D/dp > 500) this quantity consists of two sum terms from an Ejs, which is required for maintaining that vertical flow rate n>t, which is equal to the terminal sinking velocity of the particle swarm Wss, and from the term edre, which covers the frictional losses of the flow upon reversal of the flow direction ... 

Pmin corresponds to the sinking power of the particle swarm. 

Now the lower boundary values must be given for the range, in which the relationship of Fig. 5.14 applies, because for the suspension of individual particles other relationships apply than for particle swarms. 

According to Einenkel [114] identical suspension conditions = const) were attained, if the ratio between the suspension power P and the sinking power of the particle swarm Psink = is proportional Re" ... 

Drew and Wallis [37] (p 61) examined the forces on spheres in two-phase suspensions based on theoretical analyzes. Their result included lift forces that give rise to a net transverse force on particle swarms if the group of spheres are translating and rotating as a unit. Note that this force is different from... 

Huang, J., Ma, G., Muhammad, I., Cheng, Y. Identifying P-glyco-protein substrates using a support vector machine optimized by a particle swarm. J. Chem. Inf Model. 2007, 47, 1638-1647. 

Cedeno, W. and Agrafiotis, D.K. (2003) Using particle swarms for the development of QSAR models based on fC-nearest neighbor and kernel regression./. Comput. Aid. Mol Des., 17, 255-263. 

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. 

Agrafiotis DK, Cedeno W. Feature selection for structure-activity correlation using binary particle swarms. J Med Chem 2002 45 1098-1107. 

T he relation of the magnetic susceptibility of the liquid crystal phase to x that of the solid phase is a complicated, many-body problem. The energy of the system depends not only on the orientation of the molecules with respect to the magnetic field but also on their orientation with respect to each other. To obviate this difficulty, one introduces a set of noninteracting quasi particles (swarms) of mass M (3). The energy is then... 

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... 

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