Scuba Diver Optimization Algorithm: Car Side Impact Design

The Scuba Diver Optimization Algorithm (SDOA)Algorithm Implementation for Car Side Impact Design Optimization

SDOA Algorithm Implementation for Car Side Impact Design Optimization

SDOA Algorithm Implementation for Car Side Impact Design Optimization

Core Algorithm: Scuba Diver Optimization Algorithm (SDOA)

• Nature-inspired metaheuristic based on scuba diving behavior
• Population-based approach with adaptive search strategies
• Key mechanism: Oxygen levels regulate transition from exploration to exploitation

Problem Solved: Car Side Impact Design Optimization

• Objective: Minimize car weight while satisfying 10 safety constraints
• 11 design variables representing material thicknesses and positions
• 10 nonlinear constraints related to safety metrics (abdomen load, chest deflections, etc.)

Algorithm Components1. Diver Representation & Initialization

• Each diver = a candidate solution vector (11 variables)
• Initialized with uniform distribution across search space
• Includes random diversification (1/3 of population)

2. Oxygen & Depth Management

• Oxygen decay: O_i(t) = O_i(t-1) * exp(-alpha * t / t_max)
• 5 depth stages based on oxygen thresholds:

1. D1 (>75): Global exploration (Levy flight)
2. D2 (>55): Moderate exploration (crossover/random walk)
3. B1 (>35): Exploitation (local search + elite crossover)
4. D3 (>15): Fine-tuning (non-uniform mutation)
5. Reset (≤15): Diversification/reinitialization

3. Search Operators

• Levy flight: Heavy-tailed exploration for global search
• Crossover: Blending solutions with best/elite divers
• Local search: Hill-climbing around current position
• Non-uniform mutation: Decreasing perturbation over time
• Random walk: Small perturbations for local exploration

4. Constraint Handling

• Penalty function approach: Infeasible solutions penalized (1e6 × constraint violation)
• Feasibility prioritized: Accept feasible solutions over infeasible ones
• Constraint verification: Detailed constraint validation function

5. Adaptive Mechanisms

• Elite preservation: Top 15 solutions protected each iteration
• Parameter adaptation: Mutation/exploration rates decrease over time
• Oxygen replenishment: Successful moves increase oxygen levels
• Communication: Divers pull toward best solution with probability

Key Parameters

• Population size: 150 divers
• Maximum iterations: 300
• Initial oxygen: 150, decay factor α = 0.5
• Elite size: 15 solutions

Results & Visualization

The implementation includes comprehensive visualization:

1. Convergence history (best vs. average fitness)
2. Constraint violation tracking
3. Optimal design variables display
4. Diver distribution across depth stages
5. Feasibility rate pie chart
6. Algorithm parameters summary

Strengths of Implementation

1. Balanced search: Effective exploration-exploitation balance
2. Constraint handling: Robust penalty-based approach
3. Adaptive behavior: Dynamic parameter adjustment
4. Elitism: Preserves best solutions
5. Comprehensive analysis: Full tracking of algorithm behavior

Algorithm Characteristics

• Computational complexity: O(nDivers × maxIter × nVars)
• Memory usage: Moderate (stores history arrays)
• Convergence: Typically converges within 150-200 iterations
• Scalability: Suitable for medium-scale engineering problems

This implementation demonstrates how the novel SDOA metaheuristic can be effectively adapted for constrained engineering optimization problems, maintaining the algorithm's core principles while addressing problem-specific requirements.