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165 Single-Solution Particle Swarm Optimization For their multi-phase PSO (MPPSO), the velocity update is de ned as [16, 17]: vij (t + 1) = wvij (t) + c1 xij (t) + c2 yj (t)

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Essentially, the scope of new technologies in media asset management such as SANs can be broken down into four industry segments (briefly overviewed as follows, and dealt with in detail later in the chapter): Nonlinear editing in postproduction Both Sony and Lightworks use nonlinear editing applications that can benefit from storage area networks (SANs) incorporating centralized storage The Sony system can run two nonlinear editing machines that require that dualstream uncompressed video be sent to each machine The two machines share and edit video clips and then allow three DVD authoring machines to pick up the video clips and create a DVD master

(1676)

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IBC implementation at the IBC exhibition used the IBM FCRSS (Dolphin) connected to the Sony stand across 1 km of fiber-optic cable This type of solution is being used in departments within broadcasting companies, postproduction houses, educational establishments, and corporate media departments The main criteria for these systems are performance and a high level of availability

The personal best position is excluded from the velocity equation, since a hill-climbing procedure is followed where a particle s position is only updated if the new position results in improved performance Let the tuple (w, c1 , c2 ) represent the values of the inertia weight, w, and acceleration coe cients c1 and c2 Particles that nd themselves in phase 1 exhibit an attraction towards the global best position, which is achieved by setting (w, c1 , c2 ) = (1, 1, 1) Particles in phase 2 have (w, c1 , c2 ) = (1, 1, 1), forcing them to move away from the global best position Sub-swarms switch phases either when the number of iterations in the current phase exceeds a user speci ed threshold, or when particles in any phase show no improvement in tness during a userspeci ed number of consecutive iterations In addition to the velocity update as given in equation (1676), velocity vectors are periodically initialized to new random vectors Care should be taken with this process, not to reinitialize velocities when a good solution is found, since it may pull particles out of this good optimum To make sure that this does not happen, particle velocities can be reinitialized based on a reinitialization probability This probability starts with a large value that decreases over time This approach ensures large diversity in the initial steps of the algorithm, emphasizing exploration, while exploitation is favored in the later steps of the search Cooperation between the subgroups is achieved through the selection of the global best particle, which is the best position found by all the particles in both sub-swarms Particle positions are not updated using the standard position update equation Instead, a hill-climbing process is followed to ensure that the tness of a particle is monotonically decreasing (increasing) in the case of a minimization (maximization) problem The position vector is updated by randomly selecting consecutive components from the velocity vector and adding these velocity components to the corresponding position components If no improvement is obtained for any subset of consecutive components, the position vector does not change If an improvement is obtained, the corresponding position vector is accepted The value of changes for each particle, since it is randomly selected, with U (1, max ), with max initially small, increasing to a maximum of nx (the dimension of particles) The attractive and repulsive PSO (ARPSO) developed by Riget and Vesterstr m [729, 730, 877] follows a similar process where the entire swarm alternates between an attraction and repulsion phase The di erence between the MPPSO and ARPSO lies in the velocity equation, in that there are no explicit sub-swarms in ARPSO, and ARPSO uses information from the environment to switch between phases While.

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ARPSO was originally applied to one swarm, nothing prevents its application to subswarms The ARPSO is based on the diversity guided evolutionary algorithm developed by Ursem [861], where the standard Gaussian mutation operator is changed to a directed mutation in order to increase diversity In ARPSO, if the diversity of the swarm, measured using diversity(S(t)) = 1 ns

(xij (t) xj (t))2

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