Implementing a Container Ship Stowage Problem for Humanitarian Aid in Palestine Based on Cultural Algorithms

Implementing a Container Ship Stowage Problem for Humanitarian Aid in Palestine Based on Cultural Algorithms

Alberto Ochoa-Zezzatti (Juarez City University, Mexico), Julio Arreola (Juarez City University, Mexico), Kyrk Haltaufoerhyde (Cardiff Technological University, UK) and Vinicius Scarandangotti (Sassari University, Italy)
DOI: 10.4018/978-1-4666-9779-9.ch016
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Abstract

Bioinspired algorithms are a generic term used to refer to the solution of computational problems, based on the planning and implementation based on existing models in the evolutionary process-related nature. Most evolutionary algorithms proposed paradigms that occur in the biology of living things and concepts of natural selection, mutation and reproduction. However, other paradigms that can be taken in the creation of evolutionary algorithms also exist such as the forces of nature, which have been many algorithms based on water, gas and wind reactions. Many of the environments involving unstructured problems in this case a problem of accommodation of containers of humanitarian aid to a company with limited resources, which can be considered from the perspective of cultural paradigms, because the cultural paradigms offer a wide range categorized models that ignore the possible solutions to the problem-a common situation in real life. The purpose of this research is to apply evolutionary computation properties of cultural technology; in this case, to corroborate through data mining analysis of how low the support of various companies use technology for their own benefit to propose a solution to a given problem, in our case carry different types of goods deemed humanitarian aid . The mentioned above, to carry out an adaptation from the standpoint of the modeling societies. An environment for conducting tests for this type of analysis in our case a model arrangement of containers was developed in order to enable learning about not very conventional characteristics of a cultural technology. This environment was named Allaliyah in Palestinian culture means “Together we can achieve everything.”
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1. Introduction

Most of computer problems and especially those related to intelligent optimization are located in the real world, are characterized by not having a definitive (final) solution [1]. Cultural Algorithms use culture as a vehicle for storing relevant information so accessible to members of the population in our case a company artificially for many generations, were developed to model the evolution of the cultural component in time and to demonstrate how This learns and acquires such knowledge [10]. A cultural algorithm can described with the following pseudocode. Initially, a population of individuals representing the solution space is represented as a set of solutions within the search space, which is generated at random to create the first generation of the company. In our example, the solution space contains a list of attributes that can be used in the classification procedure. The space of beliefs is empty. For each generation, the Cultural Algorithm may imply a population of individuals using the “frame” Vote-inherite-Promote (VIP). During the voting phase of this process, members of the population are evaluated to identify their contribution to the belief space, using the function of acceptance. These beliefs allowed contribute most of the solution of the problem and are selected or placed on the ballot to contribute to the current space of beliefs. The belief space is modified when combined with inherited beliefs are beliefs that have been added by the current generation, this is done using a process of reasoning that allows updating the belief space (see Figure 1).

Figure 1.

Pseudo code of Cultural Algorithms

  • Begin

  • t=0;

  • Initialize POP(t); /* Initialization of population */

  • Initialize BLF(t); /* Initialization of believing space */

  • Repeat

  • Evaluate POP(t);

  • Vote (BLF (t), Accept (POP(t))));

  • Adjust (BLF (t));

  • Evolve(POP(t), Influence(BLF(t)));

  • t = t +1;

  • Select POP(t) from POP(t-1);

  • Until (Term condition is reached)

  • End

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