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As with the maximum velocity, the optimal value for the inertia weight is problemdependent [781] Initial implementations of the inertia weight used a static value for the entire search duration, for all particles for each dimension Later implementations made use of dynamically changing inertia values These approaches usually start with large inertia values, which decreases over time to smaller values In doing so, particles are allowed to explore in the initial search steps, while favoring exploitation as time increases At this time it is crucial to mention the important relationship between the values of w, and the acceleration constants The choice of value for w has to be made in conjunction with the selection of the values for c1 and c2 Van den Bergh and Engelbrecht [863, 870] showed that w> 1 (c1 + c2 ) 1 2 (1623)

information-sharing challenges by agreeing on shared vocabularies and exposing and sharing data as a service Krieger said that industry understands the agility and power that separating data from applications represents Consider the Google Maps service and the Google Earth application It is based on a community vocabulary for modeling and storing geographic data called KML (keyhole markup language) By publishing data as a service in KML, the Google Maps service or the Google Earth application seamlessly plots the data on a map or a globe On metadata, Krieger explained how it makes data assets visible or discoverable For example, in digital cameras, where the data is the photographic image, metadata would typically include the date the photograph was taken and details of the camera settings The Department of Defense Discovery Metadata Speci cation (DDMS) speci es how to use metadata to make data assets visible to the enterprise It describes how developers, engineers and users should advertise data assets posted to shared spaces so that others can discover them We didn t make up our metadata standard The DDMS is based on the industry Dublin Core standard, and we added a security leg to it This approach moves people away from hoarding data and increases data visibility and sharing Asked by Bradley how he handles resistance to the information standard, Krieger replied: It helps when the community develops the vocabulary or information exchange semantics This helps both data consumers and producers One-way translation from the community standard at the producer or consumer location is the only requirement Technologies like XML easily enable data wrapping for oneway translations Resistance is reduced because you only need to x one side of any legacy system This is how you can republish data from a legacy system to unanticipated users without rebuilding legacy applications Smart Data Strategy Work The work begins with an assessment of the current situation Are your primary processes modeled and fully attributed as described in the book Have you, as executive, clearly inventoried the questions that you need answering to optimize performance in the enterprise and requirements for planning, decision making, problem solving, sense making, and predicting Following these prerequisites, your management team, including information technologists, can address the enterprise performance optimization system CONOPS and OA Then you will implement with system development and operational improvements in executive support Smart Data Strategy Staf ng Is your organization properly staffed for the initiative Review the skills, knowledge, and experience of your staff to ensure they have what it takes to implement the strategy.

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guarantees convergent particle trajectories If this condition is not satis ed, divergent or cyclic behavior may occur A similar condition was derived by Trelea [851] Approaches to dynamically varying the inertia weight can be grouped into the following categories: Random adjustments, where a di erent inertia weight is randomly selected at each iteration One approach is to sample from a Gaussian distribution, eg w N (072, ) (1624)

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where is small enough to ensure that w is not predominantly greater than one Alternatively, Peng et al used [673] w = (c1 r1 + c2 r2 ) with no random scaling of the cognitive and social components Linear decreasing, where an initially large inertia weight (usually 09) is linearly decreased to a small value (usually 04) From Naka et al [619], Ratnaweera et al [706], Suganthan [820], Yoshida et al [941] w(t) = (w(0) w(nt )) (nt t) + w(nt ) nt (1626) (1625)

where nt is the maximum number of time steps for which the algorithm is executed, w(0) is the initial inertia weight, w(nt ) is the nal inertia weight, and w(t) is the inertia at time step t Note that w(0) > w(nt ) Nonlinear decreasing, where an initially large value decreases nonlinearly to a small value Nonlinear decreasing methods allow a shorter exploration time than the linear decreasing methods, with more time spent on re ning solutions (exploiting) Nonlinear decreasing methods will be more appropriate for smoother search spaces The following nonlinear methods have been de ned: From Peram et al [675], w(t + 1) = with w(0) = 09 (w(t) 04)(nt t) nt + 04 (1627)

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