New Initialisation Techniques for Multi-Objective Local Search Application to the Bi-objective Permutation Flowshop

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POPMUSIC

This chapter presents POPMUSIC, a general decomposition-based framework within the realm of metaheuristics and matheuristics that has been successfully applied to various combinatorial optimization problems. POPMUSIC is especially useful for designing heuristic methods for large combinatorial problems that can be partially optimized. The basic idea is to optimize subparts of solutions until a local optimum is reached. Implementations of the technique to various problems show its broad applicability and efficiency for tackling especially largesize instances

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

Deconstructing multi-objective evolutionary algorithms: An iterative analysis on the permutation flow-shop problem

Many studies in the literature have applied multi-objective evolutionary algorithms (MOEAs) to multi-objective combinatorial optimization problems. Few of them analyze the actual contribution of the basic algorithmic components of MOEAs. These components include the underlying EA structure, the fitness and diversity operators, and their policy for maintaining the population. In this paper, we compare seven MOEAs from the literature on three bi-objective and one tri-objective variants of the permutation flowshop problem. The overall best and worst performing MOEAs are then used for an iterative analysis, where each of the main components of these algorithms is analyzed to determine their contribution to the algorithms' performance. Results confirm some previous knowledge on MOEAs, but also provide new insights. Concretely, some components only work well when simultaneously used. Furthermore, a new best-performing algorithm was discovered for one of the problem variants by replacing the diversity component of the best performing algorithm (NSGA-II) with the diversity component from PAES. © 2014 Springer International Publishing.SCOPUS: cp.kinfo:eu-repo/semantics/publishe