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4 5 6 More than 6 3 4 6221 31106 62212 67396 109375 134792 Examples 5048 25240 50481 54688 2692 plus 1173 for each exemption Daily Weekly Biweekly Semimonthly 29271 58542 Monthly 41979 83958 27019 38750 13510 19375 2702 3875 13462 plus 5865 for each exemption 26923 plus 11731 for each exemption 29167 plus 12708 for each exemption 58333 plus 25416 for each exemption 1 9731 48654 97308 2 8558 42788 85577 92708 105417 185417 210833 7385 36923 73846 80000 Pay Period 5 7394 36971 73942 80104 160208

course, by a host of other considerations that are accounted for in the details of SOE implementation What makes SOE different Engineering information interoperability and embracing the value for openness and vendor independence are critical values What Is the Current Situation The current system software environment is the product of collaboration among mutually developed applications, hosted on a xed con guration of structurally integrated platforms Undesirable qualities include:

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171 Ant Colony Optimization Meta-Heuristic Algorithm 172 Simple ACO Algorithm Initialize ij (0) to small random values; Let t = 0; Place nk ants on the origin node; repeat for each ant k = 1, , nk do //Construct a path xk (t); xk (t) = ; repeat Select next node based on the probability de ned in equation (172); Add link (i, j) to path xk (t); until destination node has been reached; Remove all loops from xk (t); Calculate the path length f (xk (t)); end for each link (i, j) of the graph do //pheromone evaporation; Reduce the pheromone, ij (t), using equation (175); end for each ant k = 1, , nk do for each link (i, j) of xk (t) do 1 k = f (xk (t)) ; Update ij using equation (174); end end t = t + 1; until stopping condition is true; Return the path xk (t) with smallest f (xk (t)) as the solution;

Number of Exemptions Claimed on Statement 6 8567 42837 85673 92813 More than 6 1529 plus 1173 for each exemption 7644 plus 5865 for each exemption 15288 plus 11731 for each exemption 16562 plus 12708 for each exemption 185625 33125 plus 25416 for each exemption

terminate when all ants (or most of the ants) follow the same path The initial experiments on the binary bridge problem [212] found that ants rapidly converge to a solution, and that little time is spent exploring alternative paths To force ants to explore more, and to prevent premature convergence, pheromone intensities on links are allowed to evaporate at each iteration of the algorithm before being reinforced on the basis of the newly constructed paths For each link, (i, j), let ij (t) (1 ) ij (t) (175)

with [0, 1] The constant, , speci es the rate at which pheromones evaporate, causing ants to forget previous decisions In other words, controls the in uence of search history For large values of , pheromone evaporates rapidly, while small values of result in slower evaporation rates The more pheromones evaporate, the more random the search becomes, facilitating better exploration For = 1, the search is completely random At this point it is important to emphasize that solution construction is the result of

The desired state is a product of development from the enterprise view accomplished through collaboration among independently con gured and structurally isolated platforms Acknowledged in this approach is the real-world situation that is fraught with legacy systems Desirable qualities include more agile and less invasive solutions performing better to the bene t of the entire enterprise Figure 13 identi es engineering disciplines, advancing technologies, and structural elements in relative association Effective enterprise integration requires an enterprise view, not silos or stovepipes Optimizing enterprise performance requires re ned engineering disciplines accompanied by advancing technology and with attention to certain structural elements (Figure 14)

83750 109167

Algorithm 137 Rank-Based Crossover Operator for Di erential Evolution Rank all individuals in decreasing order of tness; for i = 1, , ns do r U (0, 1); xi (t) = xi (t) + r(xi+1 (t) xi (t)); if f (xi (t)) < f (xi+1 (t)) then xi (t) = xi (t); end end

Algorithm 138 Rank-Based Mutation Operator for Di erential Evolution Rank all individuals in decreasing order of tness; for i = 1, , ns do pm,i = ns i+1 ; ns for j = 1, , nx do r1 U (0, 1); if (r1 > pm,i ) then r2 {0, 1}; r3 U (0, 1); if (r2 = 0) then xij (t) = xij (t) + (xmax,j xij (t))r3 e 2t/nt end if (r2 = 1) then xij (t) = xij (t) (xij (t) xmin,j )r3 e 2t/nt end end end if f (xi (t)) < f (xi (t)) then xi (t) = xi (t); end end (1318) (1317)

134583 160000

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