Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems

Abstract

The reptile search algorithm is a newly developed optimization technique that can efficiently solve various optimization problems. However, while solving high-dimensional nonconvex optimization problems, the reptile search algorithm retains some drawbacks, such as slow convergence speed, high computational complexity, and local minima trapping. Therefore, an improved reptile search algorithm (IRSA) based on a sine cosine algorithm and Levy flight is proposed in this work. The modified sine cosine algorithm with enhanced global search capabilities avoids local minima trapping by conducting a full-scale search of the solution space, and the Levy flight operator with a jump size control factor increases the exploitation capabilities of the search agents. The enhanced algorithm was applied to a set of 23 well-known test functions. Additionally, statistical analysis was performed by considering 30 runs for various performance measures like best, worse, average values, and standard deviation. The statistical results showed that the improved reptile search algorithm gives a fast convergence speed, low time complexity, and efficient global search. For further verification, improved reptile search algorithm results were compared with the RSA and various state-of-the-art metaheuristic techniques. In the second phase of the paper, we used the IRSA to train hyperparameters such as weight and biases for a multi-layer perceptron neural network and a smoothing parameter (σ) for a radial basis function neural network. To validate the effectiveness of training, the improved reptile search algorithm trained multi-layer perceptron neural network classifier was tested on various challenging, real-world classification problems. Furthermore, as a second application, the IRSA-trained RBFNN regression model was used for day-ahead wind and solar power forecasting. Experimental results clearly demonstrated the superior classification and prediction capabilities of the proposed hybrid model. Qualitative, quantitative, comparative, statistical, and complexity analysis revealed improved global exploration, high efficiency, high convergence speed, high prediction accuracy, and low time complexity in the proposed technique.

Elevating Student Performance Prediction using Extra-Trees Classifier and Meta-Heuristic Optimization Algorithms

“…The Reptile Search Algorithm (RSA) draws inspiration from the foraging behaviors observed in crocodiles within their natural environment [44]. It operates by alternating between encircling and hunting search phases, with the transition between these phases achieved by dividing the total number of iterations into four segments [45], [46].…”

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

“…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].…”

Automated cyberattack detection using optimal ensemble deep learning model

“…The GRU unit and the output of the gates are evaluated. The output of integration of reset and update gates is represented in Equation (18). The reset and update gates, the computation for the GRU unit, and their corresponding outputs are given as follows below: The output of the gates is calculated using the hyperbolic tangent, the logistic sigmoid function, and the GRU unit.…”

“…The presented IRSO‐EDLCS technique primarily carries out the IRSO‐FS technique to improve the detection rate. The RSO is a recently established optimization method that could effectively resolve different problems of optimization 18 . But the RSO poses some drawbacks while resolving high dimensional non‐convex optimization problems like higher computation difficulty, local minima trapping and slower convergence speed.…”