Conference Paper

A Multi-objective Tabu Search Algorithm for Constrained Optimisation Problems

DOI: 10.1007/978-3-540-31880-4_34 Conference: Evolutionary Multi-Criterion Optimization, Third International Conference, EMO 2005, Guanajuato, Mexico, March 9-11, 2005, Proceedings
Source: DBLP


Real-world engineering optimisation problems are typically multi-objective and highly constrained, and constraints may be
both costly to evaluate and binary in nature. In addition, objective functions may be computationally expensive and, in the
commercial design cycle, there is a premium placed on rapid initial progress in the optimisation run. In these circumstances,
evolutionary algorithms may not be the best choice; we have developed a multi-objective Tabu Search algorithm, designed to
perform well under these conditions. Here we present the algorithm along with the constraint handling approach, and test it
on a number of benchmark constrained test problems. In addition, we perform a parametric study on a variety of unconstrained
test problems in order to determine the optimal parameter settings. Our algorithm performs well compared to a leading multi-objective
Genetic Algorithm, and we find that its performance is robust to parameter settings.

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Available from: Timoleon Kipouros, Sep 18, 2014
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    • "These include extensions of simulated annealing [20], tabu search [21], scatter search [22], ant systems [23], or particle swarm optimization [24], among others, to multiobjective programming. However, most of them are designed to deal with combinatorial MOPs (some exceptions are [21] [22]). Nonetheless, the most common approaches utilized in literature to cope with (1) is the use of multiobjective evolutionary algorithms (MOEAs). "
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    • "By a good approximation we mean a discrete set of points spread over the complete Paretofront and evenly distributed over it. There is a plethora of methods with that purpose in literature although most of them are designed to deal with combinatorial MOPs (some exceptions are [5] [14] [17] [21] [25]). Nonetheless, the most common approach utilized in literature to cope with (1) is the use of multi-objective evolutionary algorithms (MOEAs). "
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    • "Jaeggi et al. developed the first multi-objective TS variant presented in this paper and executed a performance comparison on a set of unconstrained test functions [19]. The constraint handling approach and the performance of the algorithm on constrained optimisation problems was also presented in [20] "
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    ABSTRACT: While there have been many adaptations of some of the more popular meta-heuristics for continuous multi-objective optimisation problems, Tabu Search has received relatively little attention, despite its suitability and effectiveness on a number of real-world design optimisation problems. In this paper we present an adaptation of a single-objective Tabu Search algorithm for multiple objectives. Further, inspired by path relinking strategies common in discrete optimisation problems, we enhance our algorithm to allow it to handle problems with large numbers of design variables. This is achieved by a novel parameter selection strategy that, unlike a full parametric analysis, avoids the use of objective function evaluations, thus keeping the overall computational cost of the procedure to a minimum. We assess the performance of our two Tabu Search variants on a range of standard test functions and compare it to a leading multi-objective Genetic Algorithm, NSGA-II. The path relinking-inspired parameter selection scheme gives a clear performance improvement over the basic multi-objective Tabu Search adaptation and both variants perform comparably with the NSGA-II.
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