Chaotic Mapping Based Advanced Aquila Optimizer With Single Stage Evolutionary Algorithm

Abstract

The intelligent optimization techniques have been introduced by carefully observing the behavior of various hunters like a whale, grey wolf, Aquila, and lizards for estimating global optimum solutions in fair time by forming appropriate mathematical models. However, hunting-based algorithms suffer from slow and pre-requisite convergence and get caught up in local optima. Aquila Optimizer (AO) is one of the recently developed hunting-based methods that encounter a similar type of shortcoming in a few situations. This research introduces the concept of chaotic mapping to the standard AO in order to increase the convergence speed. Also to maintain the balance of exploration performed by AO with its exploitation capability, a single stage evolutionary algorithm is also integrated with it. The performance of standard AO and modified AO are tested for well-defined unimodal and multimodal Benchmark functions. The proposed framework produces one population by standard AO and a new population by single stage genetic algorithm based evolutionary concept in which binary tournament selection, roulette wheel selection, shuffle crossing over and displacement mutationoccur to generate a new population.The chaotic mapping criteria are then applied to obtain various variants of the standard AO technique. The general results obtained from the proposed novel chaotic mapping-based advanced AO with single stage evolutionary algorithm shows that it outperforms the standard AO. This advanced technique is thus applied to real-world design engineering problems to study its significance from an industrial point of view.

A Method for Identifying the Wear State of Grinding Wheels Based on VMD Denoising and AO-CNN-LSTM

“…Its adaptability allows for adjustments based on the distinct characteristics of the search space, making it suitable for complex tasks related to neural network parameter optimization. AO demonstrates the ability to rapidly converge to a global optimum, proving crucial for enhancing model performance [19]. Whether in terms of model tuning or computational efficiency, AO exhibits favorable performance in the experiments conducted in this paper.…”

A Comprehensive Survey on Aquila Optimizer

“…For faster convergence, Verma et al [ 79 ] incorporated chaotic mapping into the traditional AO. A single-stage evolutionary algorithm is also incorporated into AO in order to maintain the equilibrium between its exploration and exploitation capabilities.…”

Adaptive Aquila Optimizer Combining Niche Thought with Dispersed Chaotic Swarm

“…Wang S et al [ 14 ] combined the development phase of Harris Hawks Optimization (HHO) with the exploration phase of AO to propose an improved hybrid Aquila Optimizer and Harris Hawks Optimization (IHAOHHO), combining both nonlinear escape energy parameters and stochastic opposition-based learning strategies to enhance the exploration and development of algorithms in standard test functions and industrial engineering design problems, demonstrating strong superior performance and good promise. Verma M et al [ 15 ] generated a population by a standard AO and a new population by a single-stage genetic algorithm based on the concept of evolution, where binary tournament selection, roulette wheel selection, shuffle crossover and replacement mutations occur to generate a new population. The chaotic mapping criterion is then applied to obtain various variants of the standard AO technique.…”