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

Abstract

The reptile search algorithm is an effective optimization method based on the natural laws of the biological world. By restoring and simulating the hunting process of reptiles, good optimization results can be achieved. However, due to the limitations of natural laws, it is easy to fall into local optima during the exploration phase. Inspired by the different search fields of biological organisms with varying flight heights, this paper proposes a reptile search algorithm considering different flight heights. In the exploration phase, introducing the different flight altitude abilities of two animals, the northern goshawk and the African vulture, enables reptiles to have better search horizons, improve their global search ability, and reduce the probability of falling into local optima during the exploration phase. A novel dynamic factor (DF) is proposed in the exploitation phase to improve the algorithm’s convergence speed and optimization accuracy. To verify the effectiveness of the proposed algorithm, the test results were compared with ten state-of-the-art (SOTA) algorithms on thirty-three famous test functions. The experimental results show that the proposed algorithm has good performance. In addition, the proposed algorithm and ten SOTA algorithms were applied to three micromachine practical engineering problems, and the experimental results show that the proposed algorithm has good problem-solving ability.

MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems

“…The RSA method, created by Abualigah et al [69], is an innovative optimization technique that emulates the encircling and hunting behaviors of crocodiles. This section elucidates the exploration and exploitation skills of the RSA, which are derived from its intelligent surroundings and hunting strategies employed to capture prey.…”

“…Some potential defects of the original RSA include: (i) the RSA, like many optimization algorithms, can sometimes get trapped in local optima, especially in complex search spaces with multiple peaks and valleys. This means the algorithm might converge to a sub-optimal solution rather than the global optimum, (ii) the performance of the RSA can be sensitive to its parameter settings, such as the values of α and β, (iii) when dealing with high-dimensional problems, the RSA might exhibit slow convergence rates, (iv) the original RSA might not always strike the right balance between exploration and exploitation, (v) there might be situations where the algorithm becomes stagnant, with solutions oscillating around certain values without significant improvements, (vi) the computational cost can increase significantly, and the algorithm might struggle to find good solutions within a reasonable time frame, and (vii) the original RSA does not have mechanisms to adapt its parameters or strategies based on the problem's characteristics or its current performance [68][69][70][71]. This lack of adaptability can hinder its performance on diverse problems.…”

“…Additionally, Crocodiles engage in communication and collaboration to effectively execute hunting strategies. The RSA aims to develop robust search algorithms that yield high-quality outcomes and generate novel solutions to address intricate realworld problems [68,69]. According to the authors, the RSA has effectively addressed artificial landscape functions and practical engineering challenges, surpassing other widely used optimization techniques [63].…”

Multi-Strategy Improved Sand Cat Swarm Optimization: Global Optimization and Feature Selection

“…Our research team is also committed to improving the effectiveness of original biological intelligence algorithms by introducing some mathematical theories. We proposed algorithms such as the enhanced snake optimizer (ESO) [32], hybrid golden jackal optimization and golden sine algorithm with dynamic lens-imaging learning (LSGJO) [33], reptile search algorithm considering different flight heights (FRSA) [34], etc., to provide some ideas for solving optimization problems.…”