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A few studies have combined DE with particle swarm optimization(PSO) (refer to 16) Hendtlass [360] proposed that the DE reproduction process be applied to the particles in a PSO swarm at speci ed intervals At the speci ed intervals, the PSO swarm serves as the population for a DE algorithm, and the DE is executed for a number of generations After execution of the DE, the evolved population is then further optimized using PSO Kannan et al [437] apply DE to each particle for a number of iterations, and replaces the particle with the best individual obtained from the DE process Zhang and Xie [954], and Talbi and Batouche [836] follow a somewhat di erent approach Only the personal best positions are changed using yij (t + 1) = yij (t) + j yij (t) if j Ji (t) otherwise (1319)

Given this agreement, the next step would be to examine the primary forms and data collected on maintenance logs by military services users When talking with military services representatives who work in the environment under consideration, we spoke in their language and with examples to which they could relate All sorts of data are used by the defense and military systems as shown in Figure 44 All can be mapped to a common PLCS model for data exchange Aircraft maintenance records come from the Air Force Logistics Command, Navy Air, Army Command, Suppliers, Depots, and Program Management Of ce (Figure 45)

where is the general di erence vector de ned as j = y1j (t) y2j (t) 2 (1320)

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These tables show the amount exempt from a levy on wages, salary, and other income For example: 1 A single taxpayer who is paid weekly and claims three exemptions (including one for the taxpayer) has $26731 exempt from levy 2 If the taxpayer in number 1 is over 65 and writes 1 in the ADDITIONAL STANDARD DEDUCTION space on Parts 3, 4, and 5 of the levy, $28943 is exempt from this levy ($26731 plus $2212) 3 A taxpayer who is married, files jointly, is paid biweekly, and claims two exemptions (including one for the taxpayer) has $54038 exempt from levy 4 If the taxpayer in number 3 is over 65 and has a spouse who is blind, this taxpayer should write 2 in the ADDITIONAL STANDARD DEDUCTION space on Parts 3, 4, and 5 of the levy Then, $61346 is exempt from this levy ($54038 plus $7308) 31667 Department of the Treasury Internal Revenue Service

with y1 (t) and y2 (t) randomly selected personal best positions; the notations yi (t) and yi (t) are used to indicate a personal best and neighborhood best respectively (refer to 16) The o spring, yi (t + 1), replaces the current personal best, yi (t), only if the o spring has a better tness

Filing Status 4423 9583 19167 7917 15833 23750 8846 9583 3958 7917 11875 15833 3654 7308 10962 14615 4792

In order to improve the exploration ability of DE, Ali and T rn [19] proposed to use o two population sets The second population, referred to as the auxiliary population, Ca (t), serves as an archive of those o spring rejected by the DE selection operator During the initialization process, ns pairs of vectors are randomly created The best of the two vectors is inserted as an individual in the population, C(0), while the other vector, xa (0), is inserted in the auxiliary population, Ca (0) At each generation, for i each o spring created, if the tness of the o spring is not better than the parent, instead of discarding the o spring, xi (t), it is considered for inclusion in the auxiliary population If f (xi (t)) is better than xa (t), then xi (t) replaces xa (t) The auxiliary set i i is periodically used to replace the worst individuals in C(t) with the best individuals from Ca (t)

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Although empirical studies have shown that DE convergence is relatively insensitive to control parameter values, performance can be greatly improved if parameter values

Publication 1494 (Rev 1-2003)

are optimized For the DE strategies discussed thus far, values of control parameters are static, and do not change over time These strategies require an additional search process to nd the best values for control parameters for each di erent problem a process that is usually time consuming It is also the case that di erent values for a control parameter are optimal for di erent stages of the optimization process As an alternative, a number of DE strategies have been developed where values for control parameters adapt dynamically This section reviews these approaches

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