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We propose an approach that allows to stop generating the initial solutions when the average quality of K best solutions in CS over all utility functions is the same as the average quality of local optima of these functions

Genetic local search for multi-objective combinatorial optimization

European Journal of Operational Research, no. 1 (2002): 50-71

Cited by: 675|Views9
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Abstract

The paper presents a new genetic local search (GLS) algorithm for multi-objective combinatorial optimization (MOCO). The goal of the algorithm is to generate in a short time a set of approximately efficient solutions that will allow the decision maker to choose a good compromise solution. In each iteration, the algorithm draws at random a...More

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Introduction
  • Combinatorial optimization ®nds applications in many areas, e.g. in production scheduling, project scheduling, sta€ scheduling, time-tabling, production facilities design, vehicle routing, telecommunication routing, investment planning, location and many others.

    Solutions of real-life combinatorial optimization problems usually have to be evaluated taking into account di€erent points of view corresponding to multiple, often conicting objectives. *

    Tel.: +48-61-66-52-371; fax: +48-61-877-1525.

    www-idss.cs.put.poznan.pl/jaszkiewicz

    The goal of multi-objective optimization is to

    ®nd the single solution giving the best compromise between multiple objectives.
  • Since usually there is no single solution that optimizes simultaneously all the objectives, selection of the best compromise solution requires taking into account preferences of the DM.
  • Many multi-objective optimization methods reduce the search space to the set of ef®cient solutions
  • Note that this approach is not valid if the DM searches for a sample of best solutions as the second best and other good solutions do not need to be ecient under the same assumptions about the DM's preferences
Highlights
  • Combinatorial optimization ®nds applications in many areas, e.g. in production scheduling, project scheduling, sta€ scheduling, time-tabling, production facilities design, vehicle routing, telecommunication routing, investment planning, location and many others.

    Solutions of real-life combinatorial optimization problems usually have to be evaluated taking into account di€erent points of view corresponding to multiple, often conicting objectives. *

    Tel.: +48-61-66-52-371; fax: +48-61-877-1525.

    www-idss.cs.put.poznan.pl/jaszkiewicz

    The goal of multi-objective optimization is to

    ®nd the single solution giving the best compromise between multiple objectives
  • Since usually there is no single solution that optimizes simultaneously all the objectives, selection of the best compromise solution requires taking into account preferences of the DM
  • Many multi-objective optimization methods reduce the search space to the set of ef®cient solutions. Note that this approach is not valid if the DM searches for a sample of best solutions as the second best and other good solutions do not need to be ecient under the same assumptions about the DM's preferences
  • The elite size in IM multi-objective genetic local search (MOGLS) was set equal to 10% of the population size
Results
  • The elite size in IM MOGLS was set equal to 10% of the population size.
Conclusion
  • Conclusions and directions for further research

    A new MOGLS algorithm has been described.
Summary
  • Introduction:

    Combinatorial optimization ®nds applications in many areas, e.g. in production scheduling, project scheduling, sta€ scheduling, time-tabling, production facilities design, vehicle routing, telecommunication routing, investment planning, location and many others.

    Solutions of real-life combinatorial optimization problems usually have to be evaluated taking into account di€erent points of view corresponding to multiple, often conicting objectives. *

    Tel.: +48-61-66-52-371; fax: +48-61-877-1525.

    www-idss.cs.put.poznan.pl/jaszkiewicz

    The goal of multi-objective optimization is to

    ®nd the single solution giving the best compromise between multiple objectives.
  • Since usually there is no single solution that optimizes simultaneously all the objectives, selection of the best compromise solution requires taking into account preferences of the DM.
  • Many multi-objective optimization methods reduce the search space to the set of ef®cient solutions
  • Note that this approach is not valid if the DM searches for a sample of best solutions as the second best and other good solutions do not need to be ecient under the same assumptions about the DM's preferences
  • Results:

    The elite size in IM MOGLS was set equal to 10% of the population size.
  • Conclusion:

    Conclusions and directions for further research

    A new MOGLS algorithm has been described.
Tables
  • Table1: Numbers of starting solutions for multi-objective TSP instancesa
Download tables as Excel
Funding
  • I would like to thank my colleagues Michael Hansen and Pedro Borges for fruitful discussions. This work has been supported by KBN grant No
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