# Non-Vertical Well Trajectory Design Based on Multi-Objective Optimization
## Appendix B  
``` Input: Objective function f(x) Constraints g(x) Population size N Maximum generation G_maxOutput: Pareto optimal solution set ABegin// Step 1: Initialization
1. Set M = 5 // Objectives: length, friction, torque, complexity, accuracy
2. Initialize population P(0) = {x_1, x_2,..., x_N}
3. Initialize dynamic reference vector generator EISDA
4. Initialize non-dominated archive A = \(\O\)// Step 2: Evaluate initial population For each individual x_i in P(0): Evaluate objective values: f(x_i) If constraints g(x_i) violated: Apply penalty to f(x_i) // Step 3: Main loop For g = 1 to G_max: // a: Dynamic reference vector generation R_g = EISDA.generate_vectors(P(g-1), g, G_max) // b: Generate offspring Q(g) = Variation(P(g-1)) // Selection, Crossover, Mutation // c: Evaluate offspring For each individual q_i in Q(g): Evaluate objective values: f(q_i) If constraints g(q_i) violated: Apply penalty to f(q_i) // d: Combine populations C(g) = P(g - 1) \(\cup\) Q(g) // e: Environmental selection P(g) = Select(C(g), R_g) // Reference-vector-based non-dominated sorting // f: Update non-dominated archive A = update_non_dominated(A, P(g)) // g: Record statisticsEnd For // Step 4: Post-processing Best_solution = TOPSIS(A) Visualize Pareto_front(A) Plot trajectory(Best_solution) // Step5: Save results Return A End Algorithm

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