Pareto parameter/ranking

Secondary population update. Efficiency scores are initially used to update the Pareto-archive. The current Pareto-archive is erased and a subset of the current working population that favours individuals with high efficiency score, i.e. low domination rank and high chromosome graph diversity, takes its place. Note that the size of the secondary population selected is limited by a user-supplied parameter. The secondary population mechanism has been designed specifically to preserve good solutions, non-dominated or dominated but substantially structurally unique, from all... 

Example 7.2 Consider a four-objective optimization problem. The four objectives are denoted as Ci, C2, C5, and C4. It is desired to maximize Cj and C3, and minimize C2 and C4. Rank the Pareto-optimal solutions contained in Table 7.2 using NFM subject to the ranking parameters of Table 7.3. 

Table 7.3 Parameters used to rank the Pareto domain nsing Net Flow.

The Pareto domain will first be ranked with the NFM. NFM uses four parameters for each objective criterion to express the preferences of the decision-maker the relative weight and three thresholds (indifference, preference, and veto). Table 7.10 provides, for each objective function... 

Moreover, changing combination of network properties constructs three other algorithms which are AL y, ALrov and AL y of Table 1. ALjy (Takahashi et al. 2013) excludes STEPs on thie node degree from ALsjjy. AL py and AL y substitute rank of Pareto solution for STEPs on slope of Pareto solution of AL py and ALj y, respectively. Varying parameters R and ec as shown in Table 2, numerical experiments are conducted. For example, ALjjpy takes values of 3 patterns on R and 10 patterns on ec, so 30 experiment patterns are conducted. Table 1 also indicates where the result... 

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