Optimization of agricultural product storage using real-coded genetic algorithm based on sub-population determination
Abstract
The storage of fresh agricultural products is a combinatorial problem that should be solved to to maximize number of items in the storage and also maximize the total profit without exceed the capacity of storage. The problem can be addressed as a knapsack problem that can be classified as NP-hard problem. We propose a genetic algorithm (GA) based on sub-population determination to address the problem. Sub-population GA can naturally divide the population into a set of sub-population with certain mechanism in order to obtain a better result. GA based on sub-population is applied by generating a set of sub-population which is happened in the process of initializing population. A special migration mechanism is developed to maintain population diversity. The experiment shows GA based on sub-population determination provide better results comparable to those achieved by classical GA.
Keywords
agricultural product; genetic algorithm; knapsack problem; migration; sub-population;
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PDFDOI: http://doi.org/10.11591/ijai.v11.i3.pp826-835
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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).