Open AccessSolving Multi-criteria Optimization Problems with Population-Based ACO
Author(s) -
Michael Guntsch,
Martin Middendorf
Publication year - 2003
Publication title -
lecture notes in computer science
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 0.249
H-Index - 400
eISSN - 1611-3349
pISSN - 0302-9743
ISBN - 3-540-01869-7
DOI - 10.1007/3-540-36970-8_33
Subject(s) - changeover , tardiness , mathematical optimization , computer science , ant colony optimization algorithms , population , optimization problem , set (abstract data type) , algorithm , job shop scheduling , mathematics , schedule , telecommunications , demography , transmission (telecommunications) , sociology , programming language , operating system
In this paper a Population-based Ant Colony Optimization approach is proposed to solve multi-criteria optimization problems where the population of solutions is chosen from the set of all nondominated solutions found so far. We investigate different maximum sizes for this population. The algorithm employs one pheromone matrix for each type of optimization criterion. The matrices are derived from the chosen population of solutions, and can cope with an arbitrary number of criteria. As a test problem, Single Machine Total Tardiness with changeover costs is used.
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