An effective ant colony optimization algorithm (ACO) for multi-objective resource allocation problem (MORAP)

The multi-objective resource allocation problem (MORAP) addresses the important issue which seeks to find the expected objectives by allocating the limited amount of resource to various activates. Resources may be manpower, assets, raw material or anything else in limited supply which can be used to...

Full description

Saved in:
Bibliographic Details
Published inApplied mathematics and computation Vol. 200; no. 1; pp. 167 - 177
Main Authors Chaharsooghi, S.K., Meimand Kermani, Amir H.
Format Journal Article
LanguageEnglish
Published New York, NY Elsevier Inc 15.06.2008
Elsevier
Subjects
Ant colony optimization
Calculus of variations and optimal control
Combinatorics. Ordered structures
Exact sciences and technology
Mathematical analysis
Mathematics
Multi-objective optimization model
Multi-objective resources allocation problem (MORAP)
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in mathematical programming, optimization and calculus of variations
Order, lattices, ordered algebraic structures
Sciences and techniques of general use
Multi-objective optimization model
Ant colony optimization
Multi-objective resources allocation problem (MORAP)
Numerical analysis
Algorithm performance
Genetic algorithm
Applied mathematics
Resource allocation
Optimization method
Learning algorithm
Mathematical programming
Online AccessGet full text
ISSN0096-3003
1873-5649
DOI10.1016/j.amc.2007.09.070

Cover

More Information
Summary:The multi-objective resource allocation problem (MORAP) addresses the important issue which seeks to find the expected objectives by allocating the limited amount of resource to various activates. Resources may be manpower, assets, raw material or anything else in limited supply which can be used to accomplish the goals. The goals may be objectives (i.e., minimizing costs, or maximizing efficiency) usually driven by specific future needs. In this paper, in order to obtain a set of Pareto solution efficiently, we proposed a modified version of ant colony optimization (ACO), in this algorithm we try to increase the efficiency of algorithm by increasing the learning of ants. Effectiveness and efficiency of proposed algorithm was validated by comparing the result of ACO with hybrid genetic algorithm (hGA) which was applied to MORAP later.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2007.09.070