MOPSO3- (N-Morpholino)-2-Hydroxypropanesulfonic Acid
MOPSOMulti Objective Particle Swarm Optimisation
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1285 [sigma] Function Objective Metric MOPSO pdMOPSO WFG2 2 [I.
Therefore, MOPSOs, which are derived from PSO, are unable to converge to global optima.
Parameter settings for the NSGA-II, MOPSO and MOSA algorithms
Tables 2, 3, 4 present the control parameters for the NSGA-II, MOPSO and MOSA algorithms.
Also, the performance metric for MOPSO on the test problem ZDT4 is not available.
Step 1: Solving some MOTSPs with the MOPSO approach.
For this purpose, the data mining process for extracting rules from non-dominated solutions of the MOPSO is demonstrated.
Whereas if the transmission range of nodes rises the number of cluster in each solution decreases, moreover in case of ACONET there are more optimized solution as compared to CLPSO and MOPSO.
1 (d) MOPSO show the same clusters as number of nodes due to small range of transmission and its decreases gradually downward up to 29 as we increase the transmission range.
Prior knowledge is required on the optimization process that provided the parameterized form of the controller, which was optimized by the MOPSO.
The main focus of this paper is to achieve better QoS while selecting the ABC network, using a novel RSSI prediction algorithm based on curve fitting which uses an enhanced PSO and a network selection algorithm based on MOPSO for vertical handover in heterogeneous wireless networks.
The algorithm of MOPSO applied in the present problem is as under: