Binary Aquila Optimizer for 0–1 knapsack problems

Baş, Emine

doi:10.1016/j.engappai.2022.105592

Cited by 24 publications

(6 citation statements)

References 31 publications

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“…The improved versions of AO can handle a large range of difficult real-world optimization problems better than the standard AO. The strategies used in AO are hybridization with NIOAs [22,23], oppositional-based learning [24], chaotic sequence [25], Levy flight-based strategy [26], Gauss map and crisscross operator [27], Niche Thought with Dispersed Chaotic Swarm [28], random learning mechanism and Nelder-Mead Simplex Search [29], wavelet mutation [30], Weighted Adaptive Searching Technique [31], Binay AO [32], and multi-objective AO [33].…”

Section: Previous Work On Ao and Dolmentioning

confidence: 99%

Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems

Varshney,

Kumar,

Ali

et al. 2024

Biomimetics

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One of the most important tasks in handling real-world global optimization problems is to achieve a balance between exploration and exploitation in any nature-inspired optimization method. As a result, the search agents of an algorithm constantly strive to investigate the unexplored regions of a search space. Aquila Optimizer (AO) is a recent addition to the field of metaheuristics that finds the solution to an optimization problem using the hunting behavior of Aquila. However, in some cases, AO skips the true solutions and is trapped at sub-optimal solutions. These problems lead to premature convergence (stagnation), which is harmful in determining the global optima. Therefore, to solve the above-mentioned problem, the present study aims to establish comparatively better synergy between exploration and exploitation and to escape from local stagnation in AO. In this direction, firstly, the exploration ability of AO is improved by integrating Dynamic Random Walk (DRW), and, secondly, the balance between exploration and exploitation is maintained through Dynamic Oppositional Learning (DOL). Due to its dynamic search space and low complexity, the DOL-inspired DRW technique is more computationally efficient and has higher exploration potential for convergence to the best optimum. This allows the algorithm to be improved even further and prevents premature convergence. The proposed algorithm is named DAO. A well-known set of CEC2017 and CEC2019 benchmark functions as well as three engineering problems are used for the performance evaluation. The superior ability of the proposed DAO is demonstrated by the examination of the numerical data produced and its comparison with existing metaheuristic algorithms.

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Section: Previous Work On Ao and Dolmentioning

confidence: 99%

Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems

Varshney,

Kumar,

Ali

et al. 2024

Biomimetics

View full text Add to dashboard Cite

show abstract

“…Bas [ 88 ] developed a binary AO, namely (BAO) and employed for 0–1 knapsack problem. With the use of eight distinct transfer function tests, the BAO-T algorithm was developed.…”

Section: Related Work On Classical Ao and Its Improved Variantsmentioning

confidence: 99%

A Comprehensive Survey on Aquila Optimizer

Sasmal

Hussien

Das

et al. 2023

Arch Computat Methods Eng

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Aquila Optimizer (AO) is a well-known nature-inspired optimization algorithm (NIOA) that was created in 2021 based on the prey grabbing behavior of Aquila. AO is a population-based NIOA that has demonstrated its effectiveness in the field of complex and nonlinear optimization in a short period of time. As a result, the purpose of this study is to provide an updated survey on the topic. This survey accurately reports on the designed enhanced AO variations and their applications. In order to properly assess AO, a rigorous comparison between AO and its peer NIOAs is conducted over mathematical benchmark functions. The experimental results show the AO provides competitive outcomes.

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“…These algorithms offer the features of self-organization, self-adaptation, and self-learning, and they have been widely applied in various domains, such as biology [17,18], feature selection [19], optimization computing [20], image classification [21], and artificial intelligence [22,23]. map and crisscross operator [51], random learning mechanism and Nelder-Mead simplex search [52], wavelet mutation [53], weighted adaptive searching technique [54], binary AO [55], etc. A fine literature examination of the AO algorithm and its application is offered in reference [56].…”

Section: Introductionmentioning

confidence: 99%

Using the Grey Wolf Aquila Synergistic Algorithm for Design Problems in Structural Engineering

Varshney,

Kumar,

Ali

et al. 2024

Biomimetics

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The Aquila Optimizer (AO) is a metaheuristic algorithm that is inspired by the hunting behavior of the Aquila bird. The AO approach has been proven to perform effectively on a range of benchmark optimization issues. However, the AO algorithm may suffer from limited exploration ability in specific situations. To increase the exploration ability of the AO algorithm, this work offers a hybrid approach that employs the alpha position of the Grey Wolf Optimizer (GWO) to drive the search process of the AO algorithm. At the same time, we applied the quasi-opposition-based learning (QOBL) strategy in each phase of the Aquila Optimizer algorithm. This strategy develops quasi-oppositional solutions to current solutions. The quasi-oppositional solutions are then utilized to direct the search phase of the AO algorithm. The GWO method is also notable for its resistance to noise. This means that it can perform effectively even when the objective function is noisy. The AO algorithm, on the other hand, may be sensitive to noise. By integrating the GWO approach into the AO algorithm, we can strengthen its robustness to noise, and hence, improve its performance in real-world issues. In order to evaluate the effectiveness of the technique, the algorithm was benchmarked on 23 well-known test functions and CEC2017 test functions and compared with other popular metaheuristic algorithms. The findings demonstrate that our proposed method has excellent efficacy. Finally, it was applied to five practical engineering issues, and the results showed that the technique is suitable for tough problems with uncertain search spaces.

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Binary Aquila Optimizer for 0–1 knapsack problems

Cited by 24 publications

References 31 publications

Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems

Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems

A Comprehensive Survey on Aquila Optimizer

Using the Grey Wolf Aquila Synergistic Algorithm for Design Problems in Structural Engineering

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