Designing Parallel Meta-Heuristic Methods

Designing Parallel Meta-Heuristic Methods

Teodor Gabriel Crainic (Département de Management et Technologie, Université du Québec à Montréal, CIRRELT, Canada), Tatjana Davidović (Mathematical Institute, Serbian Academy of Science and Arts, Serbia) and Dušan Ramljak (Center for Data Analytics and Biomedical Informatics, Temple University, USA)
DOI: 10.4018/978-1-4666-5784-7.ch011
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Meta-heuristics represent powerful tools for addressing hard combinatorial optimization problems. However, real life instances usually cannot be treated efficiently in “reasonable” computing times. Moreover, a major issue in meta-heuristic design and calibration is to provide high performance solutions for a variety of problems. Parallel meta-heuristics aim to address both issues. The objective of this chapter is to present a state-of-the-art survey of the main parallelization ideas and strategies, and to discuss general design principles applicable to all meta-heuristic classes. To achieve this goal, the authors explain various paradigms related to parallel meta-heuristic development, where communications, synchronization, and control aspects are the most relevant. They also discuss implementation issues pointing out the characteristics of shared and distributed memory multiprocessors as target architectures. All these topics are illustrated by the examples from recent literature related to the parallelization of various meta-heuristic methods. Here, the authors focus on Variable Neighborhood Search and Bee Colony Optimization.
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Meta-heuristic methods are widely used for solving various combinatorial optimization problems. Computing optimal solutions is intractable for many important industrial and scientific optimization problems. Therefore, meta-heuristic algorithms are used for practical applications, since, in the majority of cases, they provide high quality solutions within reasonable CPU times. However, as the problem size increases, the execution time required by a meta-heuristic to find a high quality solution may become unreasonably long. Parallelization has proven to be an efficient method for overcoming this problem.

Since meta-heuristics usually represent stochastic search processes, it is important to properly define measures to evaluate the performance of their parallelized versions, as we cannot use the standard performance measures (speedup and efficiency) for the evaluation of parallel meta-heuristics. Actually, parallelization changes the original algorithm, and consequently, the evaluation of both the execution time and the quality of the final solution is needed. Indeed, for the majority of parallelization strategies, the sequential and parallel versions of heuristic methods yield solutions that differ in value, composition, or both. Thus, an important objective when parallel heuristics are considered is to design methods that outperform their sequential counterparts in terms of solution quality and, ideally, computational efficiency. More precisely, the parallel method should not require a higher overall computation effort than the sequential method, or should justify the effort by a higher quality of the final solutions. Consequently, we select the execution time and the quality of the final solution as our performance measures.

A significant amount of work has been performed in defining, implementing, and analyzing parallelization strategies for meta-heuristics. According to the survey papers (Verhoeven & Aarts, 1995; Cung, Martins, Ribeiro, & Roucairol, 2002; Crainic & Toulouse, 2010), several main ideas related to the parallelization of meta-heuristic methods can be recognized: starting from the low level parallelization realized by distributing neighborhoods among processors, up to the cooperative multi-thread parallel search (Crainic & Hail, 2005; Crainic & Toulouse, 2010). Many parallelization strategies dealing with various meta-heuristic methods have been proposed in recent literature (Verhoeven & Aarts, 1995; Toulouse, Crainic, & Gendreau, 1996; Ferreira & Morvan, 1997; Cung, Martins, Ribeiro, & Roucairol, 2002; Crainic & Hail, 2005; Crainic & Toulouse, 2010). The rich collection of papers on parallel meta-heuristics (Alba, 2005) is devoted to both theoretical and practical aspects of this topic. Here, we briefly recall important issues, and then focus on two main classes of meta-heuristics: neighborhood-based and population-based methods. We select a representative for each class and give a survey of the existing approaches to their parallelization. As the representative for neighborhood-based meta-heuristic methods we select Variable Neighborhood Search (hereinafter: VNS) because of its numerous successful sequential and parallel applications. Parallelization of population-based methods is illustrated on Bee Colony Optimization (hereinafter: BCO), the nature-inspired meta-heuristic recently on the rise.

The rest of this chapter is organized as follows. The next section contains a brief overview of meta-heuristic methods. Review of recent literature addressing the parallelization strategies for various meta-heuristic methods is presented in Section 3. The parallelization strategies proposed in recent literature for VNS are described in Section 4, while Section 5 contains the review of the parallel BCO method. Section 6 concludes the chapter.

Key Terms in this Chapter

Communication: Data exchange between processing elements.

Parallelization: Division of computational load between multiple processing elements.

Combinatorial Optimization: Finding the optimal solution of a given problem, i.e., the one satisfying feasibility rules and yielding the extreme value of the objective function.

Meta-Heuristics: General computational algorithms using stochastic search processes applied to the various search and optimization problems.

Synchronization: Coordination of computations and communications.

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