Conference Paper

Multi-objective Parallel Tabu Search.

DOI: 10.1007/978-3-540-30217-9_74 Conference: Parallel Problem Solving from Nature - PPSN VIII, 8th International Conference, Birmingham, UK, September 18-22, 2004, Proceedings
Source: DBLP

ABSTRACT This paper describes the implementation of a parallel Tabu Search algorithm for multi-objective continuous optimisation problems.
We compare our new algorithm with a leading multi-objective Genetic Algorithm and find it exhibits comparable performance
on standard benchmark problems. In addition, for certain problem types, we expect Tabu Search to outperform other algorithms
and present preliminary results from an aerodynamic shape optimisation problem. This is a real-world, highly constrained,
computationally demanding design problem which requires efficient optimisation algorithms that can be run on parallel computers:
with this approach optimisation algorithms are able to play a part in the design cycle.

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    AIAA Journal 01/2008; 46(3):701-711. · 1.08 Impact Factor
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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. · 2.04 Impact Factor
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    ABSTRACT: Most real world search and optimization problems naturally involve multiple responses. In this paper we investigate a multiple response problem within desirability function framework and try to determine values of input variables that achieve a target value for each response through three meta-heuristic algorithms such as genetic algorithm (GA), simulated annealing (SA) and tabu search (TS). Each algorithm has some parameters that need to be accurately calibrated to ensure the best performance. For this purpose, a robust calibration is applied to the parameters by means of Taguchi method. The computational results of these three algorithms are compared against each others. The superior performance of SA over TS and TS over GA is inferred from the obtained results in various situations.
    Journal of Computational and Applied Mathematics 01/2009; 230(2):463-476. · 0.99 Impact Factor


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Sep 18, 2014