Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer

Agushaka, Jeffrey O.; Ezugwu, Absalom E.; Abualigah, Laith

doi:10.1007/s00521-022-07854-6

Cited by 241 publications

(98 citation statements)

References 65 publications

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“…Further statistical analysis was carried out using mean, standard deviation, and Friedman and Wilcoxon tests. The details about the 12 engineering problems can be found at the following: The welded beam design problem [ 35 , 36 ] The compression spring design problem (CSD) [ 37 ] The pressure vessel design problem (PVD) [ 38 ] The speed reducer design problem (SRD) [ 39 ] The three-bar truss design problem (3-BTD) [ 40 ] The gear train design problem (GTD) [ 41 ] The cantilever beam design problem (CBD) [ 42 ] The optimal design of I-shaped beam (IBD) [ 43 ] The tubular column design (TCD) [ 44 ] The piston lever design problem (PLD) [ 43 ] The corrugated bulkhead design problem (Cbhd) [ 45 ] The reinforced concrete beam design problem (RCB) [ 46 ] …”

Section: Resultsmentioning

confidence: 99%

Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

et al. 2022

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This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.

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Section: Resultsmentioning

confidence: 99%

Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

et al. 2022

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“…The commonly used method for the model optimization is the meta-heuristic algorithm like the GA and PSO (Agushaka et al, 2022a). Some scientists proposed the new metaheuristic method based the classic ones (Agushaka et al, 2022b). The SA-ACPSO method is an improvement of the traditional PSO method, which is shown in algorithm 1 (Qian et al, 2009).…”

Section: Model Optimization Using Sa-acpsomentioning

confidence: 99%

Design of a low carbon economy model by carbon cycle optimization in supply chain

Ding

2023

Front. Ecol. Evol.

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IntroductionConcerning economic globalization, enterprises must work with the cooperative partner to obtain more profits and overall planning of the supply chain has become a new focus for enterprise development. This paper studies the joint emission reduction of the supply chain in green low-carbon economy development and achieve joint emission and economic cost reduction through the optimization of carbon emission and economic dispatch.MethodsThe paper firstly uses the multi-agent model to complete the fullcycle modeling of carbon emission and economic cost; Secondly, the simulated annealing-adaptive chaos-particle swarm optimization (SAACPSO) method is used to optimize various parameters in the model to achieve emission and cost reductionResultsThe results show that after the optimization, the economic cost is reduced by 0.07 and the carbon emission is also reduced by 0.16; Finally, the practical test of the model is conducted with the collected data from the local company. The results show that the multi-objective optimization model of a joint enterprise supply chain is significantly better than single optimization in terms of emission reduction.DiscussionIt provides new ideas for a green economy and technical support for the global planning of supply chain integration.

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“…Previous research has attempted to solve constrained benchmark engineering optimization problems while maintaining a low computational cost [ 23 – 25 ]. In the context of deep learning, two commonly adopted approaches to accelerate deep convolutional neural networks include designing efficient architectures directly and optimizing network parameters through compression techniques.…”

Section: Related Workmentioning

confidence: 99%

LiteGaze: Neural architecture search for efficient gaze estimation

Guo

Miao

et al. 2023

PLoS ONE

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Gaze estimation plays a critical role in human-centered vision applications such as human–computer interaction and virtual reality. Although significant progress has been made in automatic gaze estimation by deep convolutional neural networks, it is still difficult to directly deploy deep learning based gaze estimation models across different edge devices, due to the high computational cost and various resource constraints. This work proposes LiteGaze, a deep learning framework to learn architectures for efficient gaze estimation via neural architecture search (NAS). Inspired by the once-for-all model (Cai et al., 2020), this work decouples the model training and architecture search into two different stages. In particular, a supernet is trained to support diverse architectural settings. Then specialized sub-networks are selected from the obtained supernet, given different efficiency constraints. Extensive experiments are performed on two gaze estimation datasets and demonstrate the superiority of the proposed method over previous works, advancing the real-time gaze estimation on edge devices.

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Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer

Cited by 241 publications

References 65 publications

Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

Design of a low carbon economy model by carbon cycle optimization in supply chain

LiteGaze: Neural architecture search for efficient gaze estimation

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