An Advance Computing Numerical Heuristic of Nonlinear SIR Dengue Fever System Using the Morlet Wavelet Kernel

Table 1

Optimization performance taking the MWNN-GA-IPA for the nonlinear SIR dengue fever system.

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Start of GA

 Inputs: the chromosomes are characterized with the same system element as

 W = [, , ]

 Population: the chromosomes set is written as

 , and

 Output: global values of the weight are represented as W_GA-Best

 Initialization: for the selection of chromosomes, select the weight vector values. Fit evaluation: modify the values of fitness “e” in population “P” for each vector with the use of systems 4–8

(i) Stopping criteria: terminate when [e = 10⁻²¹], [Generations = 55], [StallLimit = 140], [PopSize = 285], and [TolFun = TolCon = 10⁻²¹]

 Move to storage

 Ranking: rank individual weight vector in population using the values of the fitness

 Storage: save W_GA-Best, iterations, time, e, and count of function for the presence of GA

End of GA

IPA starts

 Inputs: start point: W_GA-Best

 Output:W_GA-IPA shows the best weight values of GA-IPA

 Initialize:W_GA-Best, iterations, assignments, and other values

 Terminate: stop, when [e = 10⁻²⁰], [Iterations = 750], [MaxFunEvals = 267000], [TolCon = TolX = 10⁻²²], and [TolFun = 10⁻²²] achieved.

 Evaluation of fitness: compute W and e using equations (8)–(12)

 Amendments: adjust “fmincon” for IPA, compute e of better-quality of ‘W’ using systems 4–8

 Accumulate: transmute W_GA-IPA, e, function counts, iterations, and time for the existing IPA runs

IPA process ends

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