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optimization algorithm to nd the optimum Gehlhaar and Fogel suggest initializing in areas that do not contain the optima, in order to validate the ability of the algorithm to locate solutions outside the initialized space [312] The last aspect of the PSO algorithms concerns the stopping conditions, ie criteria used to terminate the iterative search process A number of termination criteria have been used and proposed in the literature When selecting a termination criterion, two important aspects have to be considered: 1 The stopping condition should not cause the PSO to prematurely converge, since suboptimal solutions will be obtained 2 The stopping condition should protect against oversampling of the tness If a stopping condition requires frequent calculation of the tness function, computational complexity of the search process can be signi cantly increased The following stopping conditions have been used: Terminate when a maximum number of iterations, or FEs, has been exceeded It is obvious to realize that if this maximum number of iterations (or FEs) is too small, termination may occur before a good solution has been found This criterion is usually used in conjunction with convergence criteria to force termination if the algorithm fails to converge Used on its own, this criterion is useful in studies where the objective is to evaluate the best solution found in a restricted time period Terminate when an acceptable solution has been found Assume that x represents the optimum of the objective function f Then, this criterion will terminate the search process as soon as a particle, xi , is found such that f (xi ) |f (x ) |; that is, when an acceptable error has been reached The value of the threshold, , has to be selected with care If is too large, the search process terminates on a bad, suboptimal solution On the other hand, if is too small, the search may not terminate at all This is especially true for the basic PSO, since it has di culties in re ning solutions [81, 361, 765, 782] Furthermore, this stopping condition assumes prior knowledge of what the optimum is which is ne for problems such as training neural networks, where the optimum is usually zero It is, however, the case that knowledge of the optimum is usually not available Terminate when no improvement is observed over a number of iterations There are di erent ways in which improvement can be measured For example, if the average change in particle positions is small, the swarm can be considered to have converged Alternatively, if the average particle velocity over a number of iterations is approximately zero, only small position updates are made, and the search can be terminated The search can also be terminated if there is no signi cant improvement over a number of iterations Unfortunately, these stopping conditions introduce two parameters for which sensible values need to be found: (1) the window of iterations (or function evaluations) for which the performance is monitored, and (2) a threshold to indicate what constitutes unacceptable performance Terminate when the normalized swarm radius is close to zero When.

11/10/1999

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1 Set Direction Describe your starting position What are the current CONOPS and OA for enterprise performance optimization employing data from the senior executive perspective Model the as-is situation Given an introduction to smart data concepts and characteristics, where do you want to be Model the to-be situation What performance outcomes do you want What do you want to change What is the current state of your data and associated assets Have data specialists perform an objective assessment 2 Concentrate Resources The chances are that much of your IT support resources are skewed to infrastructure maintenance and management This skew must be shifted to direct more resources directly to smart data vision development and direct and immediate executive support for planning, problem solving, decision making, sense making, and predicting 3 Maintain Consistency Develop a CONOPS and OA that will ensure commitment and focus on processes and mechanisms that will ensure maximum executive support for optimizing enterprise performance 4 Retain Flexibility Technology and ideas about smart data are advancing Accommodate the advance by adopting spiral development and iterative reviews Your smart data strategy must be customized to your unique enterprise and to your unique position in the total enterprise context Technical Solution and Enablement Selecting standards that are most relevant to your enterprise and ones that satisfy the criteria for openness and interoperable data exchange is a technical requirement Quali ed staf ng and profession development are critical success factors in breaking from the cycle imposed by layers of legacy barriers Having strong command of modeling techniques and analytics are critical ingredients Enterprise performance is optimized by smart data strategy through policy, goals, and actions

161 Basic Particle Swarm Optimization the normalized swarm radius, calculated as [863] Rnorm = Rmax diameter(S)

(1612)

4 8 8 4 40

.

52 POLICY There are many different types of policies, and even multiple policies regarding data as we have reviewed examples We are addressing a speci c policy advocating smart data ideas applied speci cally in support of executive enterprise performance optimization The intent of the policy is to address every member of the user community with regard to their credentials and privileges and rights to access, publish, distribute, use, repackage, and exchange information 1 Policy is sponsored by the CEO and owned by the enterprise You will have to tailor your policy to the needs of your executive and the needs of the enterprise When searching for examples of smart data policy, we really didn t nd many good ones

8/9/1999

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