Paper Info
Title
A memetic immunological algorithm for resource allocation problem
Abstract
In this research work, we present a combination of a memetic algorithm and an immunological algorithm that we call Memetic Immunological Algorithm - MIA. This algorithm has been designed to tackle the resource allocation problem on a communication network. The aim of the problem is to supply all resource requested on a communication network with minimal costs and using a fixed number of providers, everyone with a limited resource quantity to be supplied. The scheduling of several resource allocations is a classical combinatorial problem that finds many applications in real-world problems. MIA incorporates two deterministic approaches: (1) a local search operator, which is based on the exploration of the neighbourhood; and (2) a deterministic approach for the assignment scheme based on the Depth First Search (DFS) algorithm. The results show that the usage of a local search procedure and mainly the DFS algorithm is an effective and efficient approach to better exploring the complex search space of the problem. To evaluate the performances of MIA we have used 28 different instances. The obtained results suggest that MIA is an effective optimization algorithm in terms of the quality of the solution produced and of the computational effort.
Year
DOI
Venue
2011
10.1007/978-3-642-22371-6_27
ICARIS
Keywords
Field
DocType
memetic immunological algorithm,resource allocation,immunological algorithm,resource allocation problem,memetic algorithm,deterministic approach,real-world problem,classical combinatorial problem,effective optimization algorithm,dfs algorithm,limited resource quantity,communication network,memetic algorithms,combinatorial optimization
Memetic algorithm,Scheduling (computing),Computer science,Artificial intelligence,Deterministic system (philosophy),Mathematical optimization,Breadth-first search,Depth-first search,Algorithm,Combinatorial optimization,Resource allocation,Local search (optimization),Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
9
Authors
3
Name
Order
Citations
PageRank
Jole Costanza1215.98
vincenzo cutello255357.63
Mario Pavone321219.41