Distribution and Selection of Ornamental Fishes' Issues on a Koi Fish Pond Using Krill Algorithm to an Order Picking Model

Distribution and Selection of Ornamental Fishes' Issues on a Koi Fish Pond Using Krill Algorithm to an Order Picking Model

Erwin Adán Martinez Gomez (Autonomous University of Juarez City, Mexico)
DOI: 10.4018/978-1-5225-8131-4.ch015


This chapter presents a problem solved by social modeling, associated with the adequate choice of colors to issues and their distribution in a koi fish pond using a range of 64 colors to specify different features related to the principal attributes of an issue adequate to represent the symbolic capital of a society from Memory Alpha. An algorithm of study based on a krill herd is presented regarding the selection of 47 issues using data obtained from the diverse cultural patterns described in this repository, a bio-inspired algorithm, to solve a specific problem adapted from the modeled literature about societies. The set of the study was formed by 1087 societies, which allowed to examine individual characteristics without affecting the visualization in the proposed koi fish pond. Demonstrating that the matching of features such as social, linguistic, and cultural specify the correct selection of colors, this research tries to explain this innovative representation and location of a sample of societies on a koi fish pond.
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The Lagrangian Model of Krill Herding, as proposed by Abualigah (2019), states the objective function of the Krill Herd algorithm (Wang, Gandomi, Alavi & Gong, 2019). The fitness function of a krill is a combination of the highest density of the krill and the distance of food from the krill (Wang Gai-Ge, et al., 2016a). The fitness value gives the imaginary distances of the krill from the herd density and food (Wang, Gandomi, Alavi & Deb, 2016b). In a two-dimensional surface, the time-dependent position of a krill is mainly influenced/controlled by the following three factors: i) movement induced by other krill individuals, ii) foraging activity and iii) random diffusion. To extend the algorithm to an n-dimensional space, the fitness function of the algorithm (Wang, Deb, Gandomi & Alavi, 2016) (for ith krill individual) is determined as below:

(1) where:

  • Ni: Motion induced on the ith krill individual due to the other krill individuals.

  • Fi: Foraging motion.

  • Di: Search space to find the best solution.

The random diffusion is very important, and the motion induced by other Krill Individuals is decisive; the fitness function of the algorithm mainly depends on the density of the krills in the search space. So, it is essential to maintain a high krill density in order to achieve an optimum solution as is proposed by Abualigah, Khader and Hanandeh (2018). The individuals keep on rebuilding the system and maintaining this high density under the influence of the other individuals. The movement of a krill individual is mainly dependent on the neighboring krill individuals and the mutual effects between them. The direction of the krill movement i is calculated based upon the different swarm densities i.e., i) ‘local effect’ provided by local krill density, ii) ‘target effect’ provided by target krill density, and iii) ‘repulsive effect’ provided by repulsive swarm density. Considering the influence of all these effects, the movement of the ith krill individual Ni can be defined as:

(2) where 978-1-5225-8131-4.ch015.m03 and:

  • N max: Maximum induced speed.

  • n: Inertia weight.

  • Old Ni: Previous motion induced.

  • i local: Local effect provided by the neighboring krill individuals.

  • i target: Target effect provided by the best krill individual.

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