Penguins Search Optimization Algorithm (PeSOA)

Gheraibia, Youcef; Moussaoui, Abdelouahab

doi:10.1007/978-3-642-38577-3_23

Cited by 57 publications

(34 citation statements)

References 18 publications

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“…We observe that, to the best of our knowledge, there is still no specific mathematical attempt to describe in a concise way the penguins parade. The mathematical literature of penguins has mostly focused on the description of the heat flow in the penguins feathers (see [4]), on the numerical analysis to mark animals for later identification (see [14]), on the statistics of the Magellanic penguins at sea (see [15]), on the hunting strategies of fishing penguins (see [7]), and on the isoperimetric arrangement of the Antarctic penguins to prevent the heat dispersion caused by the polar wind and on the crystal structures and solitary waves produced by such arrangements (see [6] and [11]). We remark that the climatic situation in Phillip Island is rather different from the Antarctic one and, given the very mild temperatures of the area, we do not think that heat considerations should affect too much the behavior and the moving strategies of the Victorian little penguins and their tendency to cluster seems more likely to be a defensive strategy against possible predators.…”

Section: Comparison With the Existing Literaturementioning

confidence: 99%

The Phillip Island penguin parade (a mathematical treatment)

Dipierro

Lombardini

Miraglio

et al. 2018

ANZIAMJ

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Penguins are flightless, so they are forced to walk while on land. In particular, they show rather specific behaviors in their homecoming, which are interesting to observe and to describe analytically.In this paper, we present a simple mathematical formulation to describe the little penguins parade in Phillip Island.We observed that penguins have the tendency to waddle back and forth on the shore to create a sufficiently large group and then walk home compactly together.The mathematical framework that we introduce describes this phenomenon, by taking into account "natural parameters", such as the eye-sight of the penguins, their cruising speed and the possible "fear" of animals. On the one hand, this favors the formation of conglomerates of penguins that gather together, but, on the other hand, this may lead to the "panic" of isolated and exposed individuals.The model that we propose is based on a set of ordinary differential equations. Due to the discontinuous behavior of the speed of the penguins, the mathematical treatment (to get existence and uniqueness of the solution) is based on a "stop-and-go" procedure.We use this setting to provide rigorous examples in which at least some penguins manage to safely return home (there are also cases in which some penguins freeze due to panic).To facilitate the intuition of the model, we also present some simple numerical simulations that can be compared with the actual movement of the penguins parade.

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Section: Comparison With the Existing Literaturementioning

confidence: 99%

The Phillip Island penguin parade (a mathematical treatment)

Dipierro

Lombardini

Miraglio

et al. 2018

ANZIAMJ

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show abstract

“…Penguins search optimisation algorithm (Pe-SOA) is a new swarm based meta-heuristic algorithm which was proposed in [16]. The PeSOA algorithm has been used to solve combinatorial called Adaptive Genetic Algorithm (AGA).…”

Section: Penguins Search Optimisation Algorithmmentioning

confidence: 99%

“…The process is repeated until a specified amount of fish is obtained or the maximum number of iterations is reached. The authors of [16] have shown that the PeSOA outperforms genetic algorithms and particle swarm optimisation in obtaining better values for benchmark optimisation functions. After a number of dives, a penguin returns to the surface and shares with its group affiliates the position and quantity of the food found.…”

Section: Penguins Search Optimisation Algorithmmentioning

confidence: 99%

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Penguins Search Optimisation Algorithm for Association Rules Mining

Gheraibia¹

2016

CIT. J. comput. inf. technol.

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Association Rules Mining (ARM) is one of the most popular and well-known approaches for the decisionmaking process. All classic exhaustive ARM algorithms are time consuming and generate a very large number of association rules, even the recent proposed meta-heuristics based methods generate a small number of high quality rules but with high overlapping. To deal with this issue, we propose a new ARM approach based on penguins search optimisation algorithm (Pe-ARM for short). Moreover, an efficient measure is incorporated into the main process to evaluate the amount of overlapping among the generated rules. The proposed approach also ensures a good diversification over the whole solutions space. To demonstrate the effectiveness of the proposed approach, several experiments have been carried out on different data sets and specifically on the biological ones. The results reveal that the proposed approach outperforms the well known meta-heuristics ARM algorithms in both execution time and solution quality.

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“…PeSOA is a new population based meta-heauristic optimization algorithm proposed by Youcef Gheraibia and Abdelouahab Moussaoui in 2013 to solve optimization problems ( [15]), based on collaborative hunting strategy of penguins. Penguins are air-breathing aquatic predators and as surface is a place for penguins they are forced to return after every foraging trip.…”

Section: Penguins Search Optimization Algorithm (Pesoa)mentioning

confidence: 99%

Optimal Placement of DSTATCOM in Radial Distribution System using Fuzzy and Penguins Search Optimization Algorithm

K¹,

V²

2015

International Journal of Innovative Research in Electrical, Ele

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This paper proposes a hybrid methodology to identify the optimal location and sizing for distribution static compensator (DSTATCOM) placement as a distribution flexible ac transmission system (DFACT) device in a distribution system, by using fuzzy and Penguins search optimization algorithm (PeSOA). The main purpose is to reduce the power loss and to improve the voltage profile of distribution networks. The PeSOA is meta-heauristic search method inspired from the hunting strategy of penguins. Here fuzzy is used for finding the optimal location and PeSOA is used to find the size of DSTATCOM in radial distribution networks. The feasibility and satisfying performance of the proposed method is examined on the IEEE 33-bus and 69-bus distribution system.

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Penguins Search Optimization Algorithm (PeSOA)

Cited by 57 publications

References 18 publications

The Phillip Island penguin parade (a mathematical treatment)

The Phillip Island penguin parade (a mathematical treatment)

Penguins Search Optimisation Algorithm for Association Rules Mining

Optimal Placement of DSTATCOM in Radial Distribution System using Fuzzy and Penguins Search Optimization Algorithm

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