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decision from the Joint Chiefs as it affected all military services using the same weapon system Wars in Iraq and Afghanistan consumed executive bandwidth A directed strategy would likely fail because resources required for massive change are unavailable, and dictates are rarely successful As important, no one in a position of executive power was engaged to impose such a directive Thus this case must be characterized as an information technology-driven initiative by default As such, the program inherited strategic direction from the DoD data strategy [6]:

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particle becomes inactive immediately after its local search task is completed Inactive particles follow the velocity and position updates of the basic PSO The probability, P ( i = 1 i = 0) is kept high to ensure that the PSO algorithm does not degrade to a pure local search algorithm The local search, or exploitation, is only necessary when the swarm starts to converge to a solution The probability of task execution should therefore be small initially, increasing over time To achieve this, the absolute ageing process introduced by Th raulaz et al [842] for ACO is used in the DLPSO to dynamically adjust the e values of the response thresholds i (t) = e t with , > 0 Empirical results showed that the DLPSO obtained signi cantly better results as a GA and the basic PSO [877, 878] Krink and L vberg [490, 534] used the life-cycle model to change the behavior of individuals Using the life-cycle model, an individual can be in any of three phases: a PSO particle, a GA individual, or a stochastic hill-climber The life-cycle model used here is in analogy with the biological process where an individual progresses through various phases from birth to maturity and reproduction The transition between phases of the life-cycle is usually triggered by environmental factors For the life-cycle PSO (LCPSO), the decision to change from one phase to another depends on an individual s success in searching the tness landscape In the original model, all individuals start as particles and exhibit behavior as dictated by the PSO velocity and position update equations The second phase in the life-cycle changes a particle to an individual in a GA, where its behavior is governed by the process of natural selection and survival of the ttest In the last phase, an individual changes into a solitary stochastic hill-climber An individual switches from one phase to the next if its tness is not improved over a number of consecutive iterations The LCPSO is summarized in Algorithm 167 Application of the LCPSO results in the formation of three subgroups, one for each behavior (ie PSO, GA or hill-climber) Therefore, the main population may at the same time consist of individuals of di erent behavior While the original implementation initialized all individuals as PSO particles, the initial population can also be initialized to contain individuals of di erent behavior from the rst time step The rationale for starting with PSO particles can be motivated by the observation that the PSO has been shown to converge faster than GAs Using hillclimbing as the third phase also makes sense, since exploitation should be emphasized in the later steps of the search process, with the initial PSO and GA phases focusing on exploration (1681).

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Initialize a population of individuals; repeat for all individuals do Evaluate tness; if fitness did not improve then Switch to next phase; end end for all PSO particles do Calculate new velocity vectors; Update positions; end for all GA individuals do Perform reproduction; Mutate; Select new population; end for all Hill-climbers do Find possible new neighboring solution; Evaluate tness of new solution; Move to new solution with speci ed probability; end until stopping condition is true;

Copy A For Social Security Administration Send this entire page with Form W-3 to the Social Security Administration; photocopies are not acceptable

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