Reptile Search Algorithm Considering Different Flight Heights to Solve Engineering Optimization Design Problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Bio-inspired Computing

Background:

• The reptile search algorithm (RSA) is an optimization technique simulating reptile hunting behaviors.
• A key limitation of RSA is its tendency to get trapped in local optima during exploration.
• Existing algorithms often struggle with balancing exploration and exploitation effectively.

Purpose of the Study:

• To propose an enhanced reptile search algorithm (RSA) that addresses the local optima problem.
• To improve the global search capability and convergence speed of the RSA.
• To validate the effectiveness of the enhanced RSA on benchmark functions and engineering problems.

Main Methods:

• Introducing varied flight altitudes inspired by the northern goshawk and African vulture during the exploration phase.
• Incorporating a novel dynamic factor (DF) in the exploitation phase to enhance convergence.
• Comparing the proposed algorithm against ten state-of-the-art (SOTA) algorithms on 33 test functions.

Main Results:

• The enhanced RSA demonstrated improved global search ability and reduced susceptibility to local optima.
• The dynamic factor significantly boosted convergence speed and optimization accuracy.
• Experimental results confirmed the superior performance of the proposed algorithm over SOTA methods on test functions.

Conclusions:

• The proposed reptile search algorithm with varied flight heights and a dynamic factor is an effective optimization method.
• The enhanced RSA shows strong performance in solving complex optimization problems and practical engineering challenges.
• This bio-inspired approach offers a promising direction for developing advanced optimization techniques.