Plenary lecture 3: the efficiency of parallel metaheuristics for combinatorial optimization - paradigms, models and implementations

Parallel metaheuristics have proved to provide efficient and powerful tools for combinatorial optimization of grand challenge scientific and engineering problems. Metaheuristics offer the opportunity to find out optimal or suboptimal solution of NP-hard problems in reasonable time. Combinatorial optimization based on metaheuristics implies tree major aspects - the search space, the neighborhood relations and the guiding function, the specific forms of which determine the metaphor of the computation. The search strategies for the optimum implied may be trajectory-based or population-based, the latter simulating biological or cultural evolution. The major goal of parallelizing metaheuristics is not only to reduce significantly the computational time but to improve the quality of solutions obtained as well. The motives to utilize parallel metaheuristics are diversification and intensification. The paper focuses on the specifics of designing parallel computational models based on metaheuristics, implementing various parallel algorithmic paradigms and optimizing the correlations architectural space - target parallel computer architecture. Classifications of parallel computational models in respect to the granularity are presented. The aspects of tuning algorithmic parameters to the specifics of the problem being solved are considered. The problems of building up metaheuristics class libraries are under consideration. Parallel performance evaluation and quality of solution estimation on the basis of parallel program implementations are treated. Case studies are presented for trajectory-based and population-based parallel metaheuristics implementations on compact computer cluster of multi-core servers (super-server).