Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Engineering Design

Background:

• Constrained engineering design problems require robust optimization techniques.
• The Dwarf Mongoose Optimization Algorithm (DMO) is a metaheuristic algorithm with potential for complex problem-solving.
• Existing DMO limitations include computational overhead in alpha selection and less efficient group interactions.

Purpose of the Study:

• To introduce an improved Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design.
• To enhance the exploration and exploitation capabilities of the original DMO algorithm.
• To validate the effectiveness of IDMO against benchmark functions and engineering problems.

Main Methods:

• Modification of the alpha selection process to prioritize the fittest individual.
• Introduction of a new operator (ω) to control alpha movement and enhance search dynamics.
• Incorporation of randomization in scout group movements for increased diversity.
• Revision of the babysitter exchange criterion to facilitate knowledge sharing among mongooses.

Main Results:

• The enhanced IDMO algorithm demonstrated superior performance in solving classical, CEC 2020 benchmark, and engineering optimization problems.
• Statistical analysis and performance metrics confirmed IDMO's effectiveness compared to DMO and eight other algorithms.
• IDMO achieved better solutions in most tested scenarios, indicating improved convergence and solution quality.

Conclusions:

• The proposed IDMO algorithm offers a significant advancement over the standard DMO for constrained engineering design.
• The modifications effectively balance exploration and exploitation, leading to more efficient and robust optimization.
• IDMO presents a promising tool for tackling complex real-world engineering challenges.