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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    • "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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    ABSTRACT: Real-life problems usually include conflicting objectives. Solving multi-objective problems (i.e., obtaining the complete efficient set and the corresponding Pareto-front) via exact methods is in many cases nearly intractable. In order to cope with those problems, several (meta) heuristic procedures have been developed during the last decade whose aim is to obtain a good discrete approximation of the Pareto-front. In this vein, a new multi-objective evolutionary algorithm, called FEMOEA, which can be applied to many nonlinear multi-objective optimization problems, has recently been proposed. Through a comparison with an exact interval branch-and-bound algorithm, it has been shown that FEMOEA provides very good approximations of the Pareto-front. Furthermore, it has been compared to the reference algorithms NSGA-II, SPEA2 and MOEA/D. Comprehensive computational studies have shown that, among the studied algorithms, FEMOEA was the one providing, on average, the best results for all the quality indicators analyzed. However, when the set approximating the Pareto-front must have many points (because a high precision is required), the computational time needed by FEMOEA may not be negligible at all. Furthermore, the memory requirements needed by the algorithm when solving those instances may be so high that the available memory may not be enough. In those cases, parallelizing the algorithm and running it in a parallel architecture may be the best way forward. In this work, a parallelization of FEMOEA, called FEMOEA-Paral, is presented. To show its applicability, a bi-objective competitive facility location and design problem is solved. The results show that FEMOEA-Paral is able to maintain the effectiveness of the sequential version and this by reducing the computational costs. Furthermore, the parallel version shows good scalability. The efficiency results have been analyzed by means of a profiling and tracing toolkit for performance analysis.
    Applied Mathematics and Computation 03/2015; 255:114-124. DOI:10.1016/j.amc.2014.08.036 · 1.55 Impact Factor
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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.
    European Journal of Operational Research 03/2008; 185(3):1192–1212. DOI:10.1016/j.ejor.2006.06.048 · 2.36 Impact Factor
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    • "See [6] for a detailed explanation of these parameters. The values of these control parameters were chosen based on experience, but it should be noted that studies in [6] show that the algorithm's performance is relatively insensitive to the control parameter settings. Thirty-nine objective-function evaluations were executed on the average per optimization step, and 11,000 out of the total of 65,000 new designs were feasible. "
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    ABSTRACT: At present, optimization is an enabling technology in innovation. Multi-objective and multidisciplinary optimization tools are essential in the design process for real-world applications. In turbomaehinery design, these approaches give insight into the design space and identify the tradeoffs between the competing performance measures. This paper describes the application of a novel multi-objective variant of the tabu search algorithm to the aerodynamic design optimization of turbomachinery blades. The aim is to improve the performance of a specific stage and eventually of the whole engine. The integrated system developed for this purpose is described. It combines the optimizer with an existing geometry parameterization scheme and a well-established computational fluid dynamics package. Its performance is illustrated through a case study in which the flow characteristics most important to the overall performance of turbomachinery blades are optimized.
    AIAA Journal 03/2008; 46(3):701-711. DOI:10.2514/1.32794 · 1.21 Impact Factor
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