Kinetic-molecular theory optimization algorithm using opposition-based learning and varying accelerated motion

Fan, Chaodong; Zheng, Ningjun; Zheng, Jinhua; Liu, Yingnan

doi:10.1007/s00500-020-05057-6

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…The main idea of OBL is simultaneously considering the fitness of an estimate and its corresponding opposite estimate to obtain a better candidate solution. The OBL concept has successfully been used in varieties of meta-heuristics algorithms [58][59][60][61][62] to improve the convergence speed. Different from the original OBL, this paper utilizes an improved OBL strategy, called random opposition-based learning (ROBL) [63], which is defined by:…”

Section: Random Opposition-based Learning (Robl)mentioning

confidence: 99%

An Improved Hybrid Aquila Optimizer and Harris Hawks Algorithm for Solving Industrial Engineering Optimization Problems

et al. 2021

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Aquila Optimizer (AO) and Harris Hawks Optimizer (HHO) are recently proposed meta-heuristic optimization algorithms. AO possesses strong global exploration capability but insufficient local exploitation ability. However, the exploitation phase of HHO is pretty good, while the exploration capability is far from satisfactory. Considering the characteristics of these two algorithms, an improved hybrid AO and HHO combined with a nonlinear escaping energy parameter and random opposition-based learning strategy is proposed, namely IHAOHHO, to improve the searching performance in this paper. Firstly, combining the salient features of AO and HHO retains valuable exploration and exploitation capabilities. In the second place, random opposition-based learning (ROBL) is added in the exploitation phase to improve local optima avoidance. Finally, the nonlinear escaping energy parameter is utilized better to balance the exploration and exploitation phases of IHAOHHO. These two strategies effectively enhance the exploration and exploitation of the proposed algorithm. To verify the optimization performance, IHAOHHO is comprehensively analyzed on 23 standard benchmark functions. Moreover, the practicability of IHAOHHO is also highlighted by four industrial engineering design problems. Compared with the original AO and HHO and five state-of-the-art algorithms, the results show that IHAOHHO has strong superior performance and promising prospects.

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Section: Random Opposition-based Learning (Robl)mentioning

confidence: 99%

An Improved Hybrid Aquila Optimizer and Harris Hawks Algorithm for Solving Industrial Engineering Optimization Problems

et al. 2021

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Artificial lizard search optimization (ALSO): a novel nature-inspired meta-heuristic algorithm

2021

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Boosting aquila optimizer by marine predators algorithm for combinatorial optimization

Wang,

Jia,

Hussien

et al. 2024

Journal of Computational Design and Engineering

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In this study, an improved version of Aquila Optimizer (AO) known as EHAOMPA has been developed by using the Marine Predators Algorithm (MPA). MPA is a recent and well-behaved optimizer with a unique memory saving and FADs mechanism. At the same time, it suffers from various defects such as inadequate global search, sluggish convergence, and stagnation of local optima. However, AO has contented robust global exploration capability, fast convergence speed, and high search efficiency. Thus, the proposed EHAOMPA aims to complement the shortcomings of AO and MPA while bringing new features. Specifically, the representative-based hunting technique is incorporated into the exploration stage to enhance population diversity. At the same time, random opposition-based learning (ROBL) is introduced into the exploitation stage to prevent the optimizer from sticking to local optima. This study tests the performance of EHAOMPA's on twenty-three standard mathematical benchmark functions, 29 complex test functions from the CEC2017 test suite, six constrained industrial engineering design problems, and a CNN-hyperparameter optimization for COVID-19 CT-image detection problem. EHAOMPA is compared with four existing optimization algorithm types, achieving the best performance on both numerical and practical issues. Compared to other methods, the test function results demonstrate that EHAOMPA exhibits a more potent global search capability, a higher convergence rate, increased accuracy, and an improved ability to avoid local optima. The excellent experimental results in practical problems indicate that the developed EHAOMPA has great potential in solving real-world optimization problems. The combination of multiple strategies can effectively improve the performance of the algorithm. The source code of the EHAOMPA is publicly available at https://github.com/WangShuang92/EHAOMPA.

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Kinetic-molecular theory optimization algorithm using opposition-based learning and varying accelerated motion

Cited by 4 publications

References 29 publications

An Improved Hybrid Aquila Optimizer and Harris Hawks Algorithm for Solving Industrial Engineering Optimization Problems

An Improved Hybrid Aquila Optimizer and Harris Hawks Algorithm for Solving Industrial Engineering Optimization Problems

Artificial lizard search optimization (ALSO): a novel nature-inspired meta-heuristic algorithm

Boosting aquila optimizer by marine predators algorithm for combinatorial optimization

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