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


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.

Download full-text


Available from: Timoleon Kipouros, Sep 18, 2014
27 Reads
  • Source
    • "While the structure of their algorithm is quite different , with elements being tailored to combinatorial optimisation problems, it is a good implementation and it contains some interesting ideas. 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] "
    [Show abstract] [Hide abstract]
    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
  • Source
    • "The other TS algorithms mentioned by Jones et al. [36] in their comprehensive review of the state-of-the-art in multi-objective metaheuristics either use a composite-objective function or are little more than local search algorithms similar to the algorithm of Baykasoglu et al. [35]. Jaeggi et al. [5] developed the original version of the multiobjective TS variant presented in this paper and executed a performance comparison on a set of unconstrained test functions. Its constraint-handling approach and the performance of the algorithm on benchmark-constrained optimization problems are presented in [6] "
    [Show abstract] [Hide abstract]
    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
  • Source
    • "The CCNPGA, CCNSGA and CCCNSGA are still unable to locate the true Pareto optimal solutions of the test problem ZDT5; nonetheless, it is enough to push the solutions from the best deceptive front to the true Pareto front in CCMOGA. It should be noted that the ZDT5 have probably proved to be so problematic such that some discard it when the ZDT test series are used [12] [13]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper studies the effectiveness of incorporating co-operative co-evolutionary strategy into 4 evolutionary multi-objective optimisation algorithms – MOGA, NPGA, NSGA and CNSGA – for continuum topology design. Apart from the co-operative co-evolutionary concept, the algorithms employ the elitism and crowding distance techniques to promote the diversity within the set of preserved non-dominated solutions. Three-related 2D heat conduction problems with two design objectives are used as the case studies. The proposed co-operative co-evolution is found to improve the optimisation effectiveness significantly. The species arrangements and sizes have some impacts; the use of moderately small species barely improves the search performances due to the interference from species coupling. As these effects depend on physical meanings of problems, it is more expedient to estimate the parameters in practice.
    Computer-Aided Design and Applications 01/2005; 2:487-496. DOI:10.1080/16864360.2005.10738398
Show more