The rPSO (PSO with restart) is the standard PSO, in which the population is reinitialized when an environmental change is detected.
Table 3 presents a summary of the overall performance of the PLBA, Shrinking-based BA, Basic BA, dopt-aiNet, PSO, rPSO, rGA, Memory-based EP, CPSO, and DASA.
Nevertheless, PLBA performed better than some algorithms such as dopt-aiNet, CPSO, rPSO, Shrinking-based BA, and Basic BA on this function.
Furthermore, it can be seen in Table 22 and Figure 1 that although the dimensional change is the most challenging scenario, the PLBA performed fairly well on this change type in all benchmark problems and better than other algorithms such as Basic BA, dopt-aiNet, and rPSO, as can be seen in Tables 24-26.
Method Simulation parameters SASPSO 2011 [w.sub.s] = 0.9, [w.sub.f] = 0.1, [[phi].sub.1s] = [[phi].sub.2f] = 2.5, [[phi].sub.1f] = [[phi].sub.2s] = 0.1 TVPSO  [w.sub.s] = 0.7, [w.sub.f] = 0.4, [c.sub.1s] = 5[c.sub.1f] = 2.5, [c.sub.2f] = 5[c.sub.2s] = 2.5 SPSO 2011  w = 1/2 ln(2), [[phi].sub.1] = 1/2 + ln(2), [[phi].sub.2] = 1/2 + ln(2) SAIWPSO  [w.sub.0] = 0.729, [w.sub.max] = 1, [w.sub.min] = 0.1, [xi] = 0.005 FGIWPSO  [w.sub.s] = 0.8, [c.sub.1] = 2.8, [c.sub.2] = 1.3 RPSO
 w = (1 + rand(1))/2, [c.sub.1] = 1.494, [c.sub.2] = 1.494 CPSO  [phi] = 4.
(II) Modified-RPSO setting: RPSO have several parameters population size=40, In most of the cases n=30 works fine.
In the improved RPSO we allow the swarm to search at least ten step left and ten step right.
have several parameters population size 40, In most of the cases 30 works fine.
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