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Articles

An improved PSO algorithm with genetic and neighborhood-based diversity operators for the job shop scheduling problem

Figures & data

Table 1. An example of JSSP.

Figure 1. Disjunctive graph of a JSSP.

Figure 1. Disjunctive graph of a JSSP.

Figure 2. A feasible solution of the example in .

Figure 2. A feasible solution of the example in Figure 1.

Figure 3. Operation encoding of the feasible solution in .

Figure 3. Operation encoding of the feasible solution in Figure 2.

Figure 4. Gantt chart of the feasible solution constructed by operation decoding.

Figure 4. Gantt chart of the feasible solution constructed by operation decoding.

Figure 5. Diversity enhancement operator.

Figure 5. Diversity enhancement operator.

Figure 6. Critical path of the feasible solution in .

Figure 6. Critical path of the feasible solution in Figure 2.

Figure 7. An example of the local search operator.

Figure 7. An example of the local search operator.

Figure 8. Feasible solution and critical path after the local search operator.

Figure 8. Feasible solution and critical path after the local search operator.

Figure 9. Gantt chart of the feasible solution after the local search operator.

Figure 9. Gantt chart of the feasible solution after the local search operator.

Figure 10. An example of the crossover operator.

Figure 10. An example of the crossover operator.

Figure 11. Flow chart of the PSO-NGO algorithm.

Figure 11. Flow chart of the PSO-NGO algorithm.

Table 2. Parameter settings for PSO and GA.

Table 3. Comparison of the different strategies: PSO, NPSO, GPSO, and PSO-NGO.

Table 4. Computational results of PSO-NGO, HPSO, PSO-VNS, MPSO, Best-so-far ABC, PSO-AIS, HGA, and MA for Ft and La test problems.

Table 5. Computational results of PSO-NGO, HPSO, and TSSB for Ta test problems.

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