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The basic PSO has been applied successfully to a number of problems, including standard function optimization problems [25, 26, 229, 450, 454], solving permutation problems [753] and training multi-layer neural networks [224, 225, 229, 446, 449, 854] While the empirical results presented in these papers illustrated the ability of the PSO to solve optimization problems, these results also showed that the basic PSO has problems with consistently converging to good solutions A number of basic modi cations to the basic PSO have been developed to improve speed of convergence and the quality of solutions found by the PSO These modi cations include the introduction of an inertia weight, velocity clamping, velocity constriction, di erent ways of determining the personal best and global best (or local best) positions, and di erent velocity models This section discusses these basic modi cations

4 Policy is enforced by balance of consequences Make clear what are desired uses and what is prohibited Make certain that participants understand the value of model-driven data exchange that is a level of performance independent of proprietary and legacy environments Establish an enterprise-wide value statement

9/20/1999

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An important aspect that determines the e ciency and accuracy of an optimization algorithm is the exploration exploitation trade-o Exploration is the ability of a search algorithm to explore di erent regions of the search space in order to locate a good optimum Exploitation, on the other hand, is the ability to concentrate the search around a promising area in order to re ne a candidate solution A good optimization algorithm optimally balances these contradictory objectives Within the PSO, these objectives are addressed by the velocity update equation The velocity updates in equations (162) and (166) consist of three terms that contribute to the step size of particles In the early applications of the basic PSO, it was found that the velocity quickly explodes to large values, especially for particles far

from the neighborhood best and personal best positions Consequently, particles have large position updates, which result in particles leaving the boundaries of the search space the particles diverge To control the global exploration of particles, velocities are clamped to stay within boundary constraints [229] If a particle s velocity exceeds a speci ed maximum velocity, the particle s velocity is set to the maximum velocity Let Vmax,j denote the maximum allowed velocity in dimension j Particle velocity is then adjusted before the position update using, vij (t + 1) = vij (t + 1) Vmax,j if vij (t + 1) < Vmax,j if vij (t + 1) Vmax,j (1617)

Exhibit 55 05/26 06/09 000 000 000 000 000 000 500 350 00 000 000 000 000 000 15 000 000 150 000 000 40 450 250 100 000 1400 1100 85 06/23 06/30 150 800 450 000 400 000 000 100 300 700 15 000 000 000 Week Ended 06/02 06/16

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Management Continuity Policy as discussed above should reinforce the value of ensuring continuity of smart data strategy from administration to administration Government Cycles and Seasons Smart data use spans government cycles for planning, budgeting, funding, and authorization Commercial Cycles and Seasons Smart data use spans commercial cycles for planning, budgeting, funding, and authorization Note that commercial enterprise that conducts business with government has a symbiotic relationship with customers whereby commercial planning may precede government planning, though the funding and authorization follow government Industry Associations, Standards Bodies, and Professional Organizations Many industry associations, standards bodies, and professional organizations are active participants in what we are calling smart data initiatives Enterprise executives must observe that some of these external bodies have earned fees for brittle standards maintenance as a primary source of revenue Therefore open and interoperable model-driven data exchange poses a threat to some of these, and you may experience resistance for that reason They may have a con ict of interest

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