Innovative parallel grasshopper optimization algorithm for reliability optimization.

Area of Science:

• Engineering Optimization
• Computational Intelligence

Background:

• Reliability optimization is crucial for engineering systems.
• Existing hybrid algorithms often use sequential strategies.
• Balancing global exploration and local refinement is a key challenge.

Purpose of the Study:

• To introduce a novel Parallel Grasshopper Optimization Algorithm (p-GOA) for reliability optimization problems.
• To enhance the balance between global exploration and local refinement in optimization.
• To address redundancy allocation issues with resource constraints.

Main Methods:

• Developed a parallel cooperative strategy integrating Grasshopper Optimization Algorithm (GOA), SOMA, and Non-Uniform Mutation Operator (NUMO).
• Divided the population into two parallel groups: one for global exploration (SOMA migration), the other for local refinement (NUMO mutation).
• Employed a penalty-free method to guide the search towards feasible solutions.

Main Results:

• The p-GOA consistently identified more reliable systems compared to existing approaches.
• Demonstrated faster convergence rates in solving reliability optimization problems.
• Effectively handled real-world engineering constraints, including cost, weight, and volume.

Conclusions:

• The p-GOA offers a superior approach to reliability optimization through its parallel dual-strategy.
• The algorithm effectively balances exploration and exploitation without penalty functions.
• p-GOA shows significant potential for practical engineering applications.