2023
DOI: 10.1016/j.cnsns.2023.107338
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Leopard seal optimization (LSO): A natural inspired meta-heuristic algorithm

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Cited by 17 publications
(3 citation statements)
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“…• Authorization and Control Access: Ensures that an authenticated object or person has the requisite rights to access certain resources or is qualified to carry out certain duties. To manually configure a smart meter, for example, an on-the-field agent needs authorization and access control rights [28][29][30].…”
Section: Services For the Iot-based Smart Eh's Securitymentioning
confidence: 99%
“…• Authorization and Control Access: Ensures that an authenticated object or person has the requisite rights to access certain resources or is qualified to carry out certain duties. To manually configure a smart meter, for example, an on-the-field agent needs authorization and access control rights [28][29][30].…”
Section: Services For the Iot-based Smart Eh's Securitymentioning
confidence: 99%
“…Who is involved might be determined in part by laws or by ordinances in the area. Public notices, laws and mandates, Web announcements, conferences, seminars, awards, publications, solar commercials, funding announcements, and public hearings are all potential avenues for gaining support from the general populace [15]- [17]. If all options are investigated and many stakeholders are taken into account, the outcome of the project improves.…”
Section: Step 2: Consult With Stakeholdersmentioning
confidence: 99%
“…Furthermore, there are many recent metaheuristic algorithms that have been stimulated by alive creatures' behaviors, such as the grey wolf optimizer (GWO), which was stimulated by the hierarchy of guidance and hunting [7]; the whale optimization algorithm (WOA), which was stimulated by producing spiral bubbles around a school of fish [8]; the salp swarm algorithm (SSA), which was stimulated by the teeming of salps to track food [9]; Harris hawk optimization (HHO), which was stimulated by the teeming work of many hawks to attack prey [10]; the mantis search algorithm (MSA), which was inspired by the foraging process of mantises [11]; the nutcracker optimization algorithm (NOA), which was stimulated by the seasonal deeds of nutcrackers in finding, storing, and memorizing food [12]; the Aquila optimizer (AO), which was stimulated by the hunting style of Aquila [13]; the black widow optimizer (BWO), which was stimulated by the mating and flesh-eating of black widow spiders [14]; and the Tunicate swarm algorithm (TSA), which was stimulated by the swarming manners of tunicates in tracking food [15]. Consequently, many algorithms are stimulated by the conduct of living creatures, for example, dolphins [16], white sharks [17], vultures [18], orcas [19], starlings [20], rabbits [21], frogs [22], butterflies [23], hyenas [24], reptiles [25], coati [26], leopards [27], and eagles [28].…”
Section: Introductionmentioning
confidence: 99%