A Chaotic Multi-Objective Runge–Kutta Optimization Algorithm for Optimized Circuit Design

Algorithm 2

The pseudocode of CMRUN

Part 1. Initialization

Define the number of objective functions (nObj = 2)

Initialize the fitness function for both objective functions

Randomly generate the initial population for the CMRUN

Evaluate the objective function values of each population member

Sort the costs obtained from the objective function

Initialize the chaos parameters

Update the convergence curves of both objectives with the first best costs

Part 2. CMRUN operations

 forit = 1: MaxIt

  Update the chaotic parameters

  forn = 1: N

   Apply chaotic parameters in updating the algorithm’s equations

   Determine the solutions , , and for each objective function

   Perform operations to improve and update the solutions

   Update best costs for the objective functions

   Check if solutions go outside the search space and bring them back

   Update chaos parameters for ESQ

   Enhance the solution quality

   forj = 1 : dim

    Determine from Equation 30

   end for

   Perform boundary check for solutions again

   if

    Evaluate position

     if

     if rand <

      Determine position

     end

    end

   end

  Modernize positions and

  end for

  Modernize position

 it = it + 1

 end

Part 3. Return and best costs

   Update Convergence Curves