Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization

This chapter introduces two Perl programs that implement graphical tools for exploring the performance of stochastic local search algorithms for biobjective optimization problems. These tools are based on the concept of the empirical attainment function (EAF), which describes the probabilistic distribution of the outcomes obtained by a stochastic algorithm in the objective space. In particular, we consider the visualization of attainment surfaces and differences between the first-order EAFs of the outcomes of two algorithms. This visualization allows us to identify certain algorithmic behaviors in a graphical way. We explain the use of these visualization tools and illustrate them with examples arising from practice. © 2010 Springer-Verlag Berlin Heidelberg.SCOPUS: ch.binfo:eu-repo/semantics/publishe

Iterated Greedy

Iterated greedy is a search method that iterates through applications of construction heuristics using the repeated execution of two main phases, the partial destruction of a complete candidate solution and a subsequent reconstruction of a complete candidate solution. Iterated greedy is based on a simple principle, and methods based on this principle have been proposed and published several times in the literature under different names such as simulated annealing, iterative flattening, ruin-and-recreate, large neighborhood search, and others. Despite its simplicity, iterated greedy has led to rather high-performing algorithms. In combination with other heuristic optimization techniques such as a local search, it has given place to state-of-the-art algorithms for various problems. This paper reviews the main principles of iterated greedy algorithms, relates the basic technique to the various proposals based on this principle, discusses its relationship with other optimization techniques, and gives an overview of problems to which iterated greedy has been successfully applied.SCOPUS: ch.binfo:eu-repo/semantics/publishe

Iterated Local Search

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