Chaotic state of matter search with elite opposition based learning: A new hybrid metaheuristic algorithm

Khanduja, Neha; Bhushan, Bharat

doi:10.1002/oca.2810

Cited by 12 publications

(5 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The EOBL technique, a variant of opposition-based learning (OBL) [ 29 ], has been favored by researchers to augment the effectiveness of metaheuristic optimization methods [ 51 ]. It leverages the power of the elite agents and their current counterparts, generating opposite solutions to achieve superior outcomes [ 52 ].…”

Section: Aquila Optimizer and Its Improved Versionmentioning

confidence: 99%

An elite approach to re-design Aquila optimizer for efficient AFR system control

Izci,

Ekinci,

Hussien

2023

PLoS ONE

View full text Add to dashboard Cite

Controlling the air-fuel ratio system (AFR) in lean combustion spark-ignition engines is crucial for mitigating emissions and addressing climate change. In this regard, this study proposes an enhanced version of the Aquila optimizer (ImpAO) with a modified elite opposition-based learning technique to optimize the feedforward (FF) mechanism and proportional-integral (PI) controller parameters for AFR control. Simulation results demonstrate ImpAO’s outstanding performance compared to state-of-the-art algorithms. It achieves a minimum cost function value of 0.6759, exhibiting robustness and stability with an average ± standard deviation range of 0.6823±0.0047. The Wilcoxon signed-rank test confirms highly significant differences (p<0.001) between ImpAO and other algorithms. ImpAO also outperforms competitors in terms of elapsed time, with an average of 43.6072 s per run. Transient response analysis reveals that ImpAO achieves a lower rise time of 1.1845 s, settling time of 3.0188 s, overshoot of 0.1679%, and peak time of 4.0371 s compared to alternative algorithms. The algorithm consistently achieves lower error-based cost function values, indicating more accurate control. ImpAO demonstrates superior capabilities in tracking the desired input signal compared to other algorithms. Comparative assessment with recent metaheuristic algorithms further confirms ImpAO’s superior performance in terms of transient response metrics and error-based cost functions. In summary, the simulation results provide strong evidence of the exceptional performance and effectiveness of the proposed ImpAO algorithm. It establishes ImpAO as a reliable and superior solution for optimizing the FF mechanism-supported PI controller for the AFR system, surpassing state-of-the-art algorithms and recent metaheuristic optimizers.

show abstract

Section: Aquila Optimizer and Its Improved Versionmentioning

confidence: 99%

An elite approach to re-design Aquila optimizer for efficient AFR system control

Izci,

Ekinci,

Hussien

2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The opposition-based learning strategy enhances sparrow swarm diversity and somewhat improves the algorithm's performance [12]. In the swarm intelligence optimization algorithm, elite individuals possess more helpful information than ordinary ones, and a swarm generated by reversing the elite individuals leads to a higher quality diversity [13]. The best individuals from the current and opposition-based swarm are chosen to form a new swarm that enters the next iteration.…”

Section: Fusion Variation Strategymentioning

confidence: 99%

Muti-Fusion Swarm Intelligence Optimization Algorithm in Base Station Coverage Optimization Problems

Yan¹,

Bian²

2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

As millimeter waves will be widely used in the Internet of Things (IoT) and Telematics to provide high bandwidth communication and mass connectivity, the coverage optimization of base stations can effectively improve the quality of communication services. How to optimize the convergence speed of the base station coverage solution is crucial for IoT service providers. This paper proposes the Muti-Fusion Sparrow Search Algorithm (MFSSA) optimize the situation to address the problem of discrete coverage maximization and rapid convergence. Firstly, the initial swarm diversity is enriched using a sine chaotic map, and dynamic adaptive weighting is added to the discoverer location update strategy to improve the global search capability. Diverse swarms have a more remarkable ability to forage for food and avoid predation and are less likely to fall into a "precocious" state. Such a swarm is very suitable for solving NP-hard problems. Secondly, an elite opposition-based learning strategy is added to expand the search range of the algorithm, and a t-distribution-based one-fifth rule is introduced to reduce the probability of falling into a local optimum. This fusion mutation strategy can significantly optimize the adaptability and searchability of the algorithm. Finally, the experimental results show that the MFSSA algorithm can effectively improve the coverage of the deployment scheme in the base station coverage optimization problem, and the convergence speed is better than other algorithms. MFSSA is improved by more than 10% compared to the original algorithm.

show abstract

“…It is reported in [34] that the opposite solutions of elite individuals are more likely to fall in the global optimal region. Moreover, EOBL technology has also been successfully applied to various algorithms to improve their performance [35][36][37]. Adopting EOBL strategy to enhance population diversity during random search of Hawks in DHHCO.…”

Section: Eobl-based Diversity Strategymentioning

confidence: 99%

Data-driven Harris Hawks constrained optimization for computationally expensive constrained problems

Dong

Wang

et al. 2022

Complex Intell. Syst.

View full text Add to dashboard Cite

Aiming at the constrained optimization problem where function evaluation is time-consuming, this paper proposed a novel algorithm called data-driven Harris Hawks constrained optimization (DHHCO). In DHHCO, Kriging models are utilized to prospect potentially optimal areas by leveraging computationally expensive historical data during optimization. Three powerful strategies are, respectively, embedded into different phases of conventional Harris Hawks optimization (HHO) to generate diverse candidate sample data for exploiting around the existing sample data and exploring uncharted region. Moreover, a Kriging-based data-driven strategy composed of data-driven population construction and individual selection strategy is presented, which fully mines and utilizes the potential available information in the existing sample data. DHHCO inherits and develops HHO's offspring updating mechanism, and meanwhile exerts the prediction ability of Kriging, reduces the number of expensive function evaluations, and provides new ideas for data-driven constraint optimization. Comprehensive experiments have been conducted on 13 benchmark functions and a real-world expensive optimization problem. The experimental results suggest that the proposed DHHCO can achieve quite competitive performance compared with six representative algorithms and can find the near global optimum with 200 function evaluations for most examples. Moreover, DHHCO is applied to the structural optimization of the internal components of the real underwater vehicle, and the final satisfactory weight reduction effect is more than 18%.

show abstract

Chaotic state of matter search with elite opposition based learning: A new hybrid metaheuristic algorithm

Cited by 12 publications

References 47 publications

An elite approach to re-design Aquila optimizer for efficient AFR system control

An elite approach to re-design Aquila optimizer for efficient AFR system control

Muti-Fusion Swarm Intelligence Optimization Algorithm in Base Station Coverage Optimization Problems

Data-driven Harris Hawks constrained optimization for computationally expensive constrained problems

Contact Info

Product

Resources

About