Paper Info
Title
A Binary Grasshopper Algorithm Applied to the Knapsack Problem.
Abstract
In engineering and science, there are many combinatorial optimization problems. A lot of these problems are NP-hard and can hardly be addressed by full techniques. Therefore, designing binary algorithms based on swarm intelligence continuous metaheuristics is an area of interest in operational research. In this paper we use a general binarization mechanism based on the percentile concept. We apply the percentile concept to grasshopper algorithm to solve multidimensional knapsack problem (MKP). Experiments are designed to demonstrate the utility of the percentile concept in binarization. Additionally we verify the efficiency of our algorithm through benchmark instances, showing that binary grasshopper algorithm (BGOA) obtains adequate results when it is evaluated against another state of the art algorithm.
Year
DOI
Venue
2018
10.1007/978-3-319-91189-2_14
ARTIFICIAL INTELLIGENCE AND ALGORITHMS IN INTELLIGENT SYSTEMS
Keywords
Field
DocType
Combinatorial optimization,KnapSack,Metaheuristics,Percentile
Combinatorial optimization problem,Computer science,Swarm intelligence,Algorithm,Combinatorial optimization,Knapsack problem,Percentile,Area of interest,Metaheuristic,Binary number
Conference
Volume
ISSN
Citations 
764
2194-5357
0
PageRank 
References 
Authors
0.34
10
4
Name
Order
Citations
PageRank
Hernan Pinto101.01
Alvaro Peña200.68
Matías Valenzuela301.01
Andrés Fernández400.68