An improved ant colony optimization algorithm for nonlinear resource-leveling problems

Authors:
Jin-qiang Geng;Li-ping Weng;Si-hong Liu
Affiliations:
College of Water Conservancy and Hydropower, Hohai University, Nanjing 210098, PR China;Business School of Hohai University, Nanjing 210098, PR China;College of Water Conservancy and Hydropower, Hohai University, Nanjing 210098, PR China
Venue:
Computers & Mathematics with Applications
Year:
2011

Citing 4
Cited 2

An Island Model Based Ant System with Lookahead for the Shortest Supersequence Problem

PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
An efficient hybrid algorithm for resource-constrained project scheduling

Information Sciences: an International Journal
Ant colony system: a cooperative learning approach to the traveling salesman problem

IEEE Transactions on Evolutionary Computation
Ant colony optimization for resource-constrained project scheduling

IEEE Transactions on Evolutionary Computation

A personalized auxiliary material recommendation system based on learning style on Facebook applying an artificial bee colony algorithm

Computers & Mathematics with Applications
The total adjustment cost problem: Applications, models, and solution algorithms

Journal of Scheduling

Quantified Score

Hi-index	0.09

Visualization

Abstract

The notion of using a meta-heuristic approach to solve nonlinear resource-leveling problems has been intensively studied in recent years. Premature convergence and poor exploitation are the main obstacles for the heuristic algorithms. Analyzing the characteristics of the project topology network, this paper introduces a directional ant colony optimization (DACO) algorithm for solving nonlinear resource-leveling problems. The DACO algorithm introduced can efficiently improve the convergence rate and the quality of solution for real-project scheduling.