Nature Inspired Computational Technique for the Numerical Solution of Nonlinear Singular Boundary Value Problems Arising in Physiology

Pseudocode 1

Hybridization of GA with IPA and ASA.

Step  1. Population Initialization

    A population of 𝑁 individuals or chromosome is generated using

    random number generator. The length of the chromosome represents the

     number of unknown adjustable parameters to be optimized.

Step  2. Fitness Evaluation

    A problem relevant fitness function is used to compute the fitness of

    each individual in the current population.

Step  3. Stoppage Criteria

     The algorithm stops if the maximum number of generations (cycles) has

    exceeded or a certain level of fitness value has reached. If the stopping

    criterion is fulfilled then go to step 6 for local search fine tuning, else

    continue and repeat steps 2 to 5.

Step  4. Selection and Reproduction

      The chromosomes from the current population are selected on the basis

    of their fitness which acts as parents for new generation. These parents

    produce children (offsprings) with a probability to their fitness through

    crossover operation.

Step  5. Mutation

    Mutation operation introduces random alterations in the genes to retain

    the genetic diversity to find a good solution.

Step  6. Local Search Fine Tuning

    The optimal chromosome achieved by the GA is fed to IPA for fine

     tuning and improvement.