A memorized multi-objective Sinh-Cosh optimizer for solving multi-objective engineering design problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Engineering Applications

Background:

• Multi-objective optimization problems present significant challenges in various scientific and engineering fields.
• Existing optimization algorithms often struggle with balancing convergence and diversity for complex, real-world applications.

Purpose of the Study:

• To introduce and evaluate the Multi-Objective Sinh-Cosh Optimization Algorithm (MOSCHO), an extension of the Sinh-Cosh optimizer.
• To enhance the capability of finding non-dominated solutions by integrating memorized local optima with global solutions.
• To assess MOSCHO's effectiveness on benchmark functions and real-world engineering design problems.

Main Methods:

• The proposed MOSCHO algorithm incorporates a memorized local optimum strategy within the Sinh-Cosh optimization framework.
• The algorithm's search space is bounded by integrating local and global optimal solutions to guide the update process.
• Performance evaluation involved seven standard metrics, comparing MOSCHO against established multi-objective optimization algorithms.

Main Results:

• MOSCHO demonstrated significant convergence and diversity capabilities across tested functions and applications.
• The algorithm achieved superior performance on ZDT3, ZDT4, and MMF14 benchmark functions.
• MOSCHO exhibited strong performance (>75% of metrics) on the SRN constrained function and real-world engineering problems.

Conclusions:

• The Multi-Objective Sinh-Cosh Optimization Algorithm (MOSCHO) is a robust and effective method for tackling complex multi-objective optimization tasks.
• The integration of memorized local optima significantly contributes to MOSCHO's ability to find high-quality non-dominated solutions.
• MOSCHO shows particular promise for real-world engineering design applications due to its strong performance and adaptability.