Ant Lion Optimizer: A Comprehensive Survey of Its Variants and Applications

Abualigah, Laith; Shehab, Mohammad; Mirjalili, Seyedali; Elaziz, Mohamed Abd

doi:10.1007/s11831-020-09420-6

Cited by 140 publications

(56 citation statements)

References 76 publications

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“…The values of capacitance are more significant as the number of conductors is increasing. Figure 5 shows the result of the three-phase transmission line inductance per unit length for different conductor [78] O (mn2) [82] Convergence Fastest rate [54] Good convergence [59] Better convergence [62] Slow rate [65] Fast rate [70] Quickly rate [75] Suffer from permanent convergence [79] Rapidly converged [82] Strength The balance between exploration and exploitation [55] The balance between exploration and exploitation [57] The balance between exploration and exploitation [61] The balance between intensification and diversification [66] Deal with complex fitness landscape [71] Do not have overlapping and mutation calculation [76] Increase the diversity of the new solutions [80] Avoid trapped at local optimum [83] Weaknesses Low precision [56] High precision [60] High accuracy [61] Trapped in a local optimum [67] Evaluation is relatively expensive [72] Suffers from partial optimism [76] Get stuck on local optimal [80] Needs huge memory resources [83] To more clearly show the dependence of bundle conductors on the AC transmissions line, it is plotted in Figures 2-5. This trend is consistent with the hypothesis that a more bundle conductor yields larger capacitance per unit length.…”

Section: Convergence Curve Of Proposed Gwo: the Technique For The Besmentioning

confidence: 99%

Application of grey wolf optimisation algorithm in parameter calculation of overhead transmission line system

Shaikh

Chen

Jatoi³

et al. 2021

IET Science Measure & Tech

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The transmission line is the main component in the power system consisting of inductance, capacitance, and resistance. These parameters are important during the transmission line design. This research work applies a novel optimisation technique, grey wolf optimisation (GWO), to calculate the overhead transmission line parameter. The best optimal value is estimated with the control variables. Furthermore, the effect of different bundle conductors, that is, two, three, and four bundle conductors, radius, and spacing between the conductors on the transmission line is also analysed. GWO is a recently developed nature-inspired meta-heuristic algorithm. Single-phase and three-phase transmission line test systems have been adopted for testing purposes. The proposed algorithm is inspired by the command hierarchy and hunting system of grey wolves. The algorithm is applied to 14 benchmark optimisation functions with dimension and number of search agents. The results of the GWO algorithms are optimised and are superior as compared to previously applied algorithms. The proposed algorithm achieved the best optimal solutions for most of these functions that have been validated statistically. From the results, it is identified that the proposed algorithm is computationally efficient and performs significantly better in terms of accuracy, robustness, and convergence speed. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Convergence Curve Of Proposed Gwo: the Technique For The Besmentioning

confidence: 99%

Application of grey wolf optimisation algorithm in parameter calculation of overhead transmission line system

Shaikh

Chen

Jatoi³

et al. 2021

IET Science Measure & Tech

View full text Add to dashboard Cite

show abstract

“…The prime motivation is the well known No Free Lunch (NFL) theorem [36], which states that any single algorithm cannot perform equally well on all the optimization problems. Thus, our work targets at enhancing the CSO by hybridizing it with ALO, which is a metaheuristic technique mimicking the hunting process of antlions [37][38][39]. Numerical simulations demonstrate that fine-tuning of the solutions obtained by ALO with CSO can significantly reduce the chances of getting stuck in local optima, thus leading to an enhanced convergence of the hybrid algorithm.…”

Section: Introductionmentioning

confidence: 99%

A hybrid ant lion optimization chicken swarm optimization algorithm for charger placement problem

Deb

Gao

2021

Complex Intell. Syst.

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Transportation electrification is known to be a viable alternative to deal with the alarming issues of global warming, air pollution, and energy crisis. Public acceptance of Electric Vehicles (EVs) requires the availability of charging infrastructure. However, the optimal placement of chargers is indeed a complex problem with multiple design variables, objective functions, and constraints. Chargers must be placed with the EV drivers’ convenience and security of the power distribution network being taken into account. The solutions to such an emerging optimization problem are mostly based on metaheuristics. This work proposes a novel metaheuristic considering the hybridization of Chicken Swarm Optimization (CSO) with Ant Lion Optimization (ALO) for effectively and efficiently coping with the charger placement problem. The amalgamation of CSO with ALO can enhance the performance of ALO, thereby preventing it from getting stuck in the local optima. Our hybrid algorithm has the strengths from both CSO and ALO, which is tested on the standard benchmark functions as well as the above charger placement problem. Simulation results demonstrate that it performs moderately better than the counterpart methods.

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“…In addition, an improved grey-wolf optimizer algorithm (I-GWO) and multiverse optimization (MVO) algorithm have been applied to accomplish the same goal. The ant-lion optimizer (ALO) algorithm has also been implemented to model PEFCs [28].…”

Section: Introductionmentioning

confidence: 99%

Optimization Algorithms: Optimal Parameters Computation for Modeling the Polarization Curves of a PEFC Considering the Effect of the Relative Humidity

et al. 2021

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The development of green energy conversion devices has been promising to face climate change and global warming challenges over the last few years. Energy applications require a confident performance prediction, especially in polymer electrolyte fuel cell (PEFC), to guarantee optimal operation. Several researchers have employed optimization algorithms (OAs) to identify operating parameters to improve the PEFC performance. In the current study, several nature-based OAs have been performed to compute the optimal parameters used to describe the polarization curves in a PEFC. Different relative humidity (RH) values, one of the most influential variables on PEFC performance, have been considered. To develop this study, experimental data have been collected from a lab-scale fuel cell test system establishing different RH percentages, from 18 to 100%. OAs like neural network algorithm (NNA), improved grey-wolf optimizer (I-GWO), ant lion optimizer (ALO), bird swarm algorithm (BSA), and multi-verse optimization (MVO) were evaluated and compared using statistical parameters as training error and time. Results gave enough information to conclude that NNA had better performance and showed better results over other highlighted OAs. Finally, it was found that sparsity and noise are more present at lower relative humidity values. At low RH, a PEFC operates under critical conditions, affecting the fitting on OAs.

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Ant Lion Optimizer: A Comprehensive Survey of Its Variants and Applications

Cited by 140 publications

References 76 publications

Application of grey wolf optimisation algorithm in parameter calculation of overhead transmission line system

Application of grey wolf optimisation algorithm in parameter calculation of overhead transmission line system

A hybrid ant lion optimization chicken swarm optimization algorithm for charger placement problem

Optimization Algorithms: Optimal Parameters Computation for Modeling the Polarization Curves of a PEFC Considering the Effect of the Relative Humidity

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