1,721,050 research outputs found
Objective reduction in evolutionary multiobjective optimization: Theory and applications
Full text linkMany-objective problems represent a major challenge in the field of evolutionary mul-tiobjective optimization—in terms of search efficiency, computational cost, decision making, visualization, and so on. This leads to various research questions, in particular whether certain objectives can be omitted in order to overcome or at least diminish the difficulties that arise when many, that is, more than three, objective functions are involved. This study addresses this question from different perspectives. First, we investigate how adding or omitting objectives affects the problem charac-teristics and propose a general notion of conflict between objective sets as a theoretical foundation for objective reduction. Second, we present both exact and heuristic algo-rithms to systematically reduce the number of objectives, while preserving as much as possible of the dominance structure of the underlying optimization problem. Third, we demonstrate the usefulness of the proposed objective reduction method in the context of both decision making and search for a radar waveform application as well as for well-known test functions
Optimal μ-Distributions for the Hypervolume Indicator for Problems With Linear Bi-Objective Fronts: Exact and Exhaustive Results
Full text linkcorrected author versionInternational audienceTo simultaneously optimize multiple objective functions, several evolutionary multiobjective optimization (EMO) algorithms have been proposed. Nowadays, often set quality indicators are used when comparing the performance of those algorithms or when selecting ``good'' solutions during the algorithm run. Hence, characterizing the solution sets that maximize a certain indicator is crucial---complying with the optimization goal of many indicator-based EMO algorithms. If these optimal solution sets are upper bounded in size, e.g., by the population size μ, we call them optimal μ-distributions. Recently, optimal μ-distributions for the well-known hypervolume indicator have been theoretically analyzed, in particular, for bi-objective problems with a linear Pareto front. Although the exact optimal μ-distributions have been characterized in this case, not all possible choices of the hypervolume's reference point have been investigated. In this paper, we revisit the previous results and rigorously characterize the optimal μ-distributions also for all other reference point choices. In this sense, our characterization is now exhaustive as the result holds for any linear Pareto front and for any choice of the reference point and the optimal μ-distributions turn out to be always unique in those cases. We also prove a tight lower bound (depending on μ) such that choosing the reference point above this bound ensures the extremes of the Pareto front to be always included in optimal μ-distributions
Optimal μ-Distributions for the Hypervolume Indicator for Problems with Linear Bi-objective Fronts: Exact and Exhaustive Results
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Many-criteria optimisation and decision analysis ontology and knowledge management
Full text linkn this chapter, we present a Many-Criteria Optimisation and Decision Analysis (MACODA) Ontology and MACODA Knowledge Management Web-Based Platform (named MyCODA, available at http://macoda.club) for the research community. The purpose of this initiative is to allow for the collaborative development of an ontology to represent the MACODA knowledge domain and to make available a set of integrated tools for its use by researchers and practitioners. MyCODA is a knowledge-based platform to identify and describe MACODA research constructs, and to explore how these constructs relate to each other. It is designed to model and systematise the knowledge created by the MACODA research community, supporting features such as querying and reasoning, by means of formal logics, and use cases such as training new learners and finding research gaps in the MACODA research domain.info:eu-repo/semantics/acceptedVersio
Parallelization of Evolutionary Algorithms
No full textFifteen weeks ago I began to engage in the principles of evolutionary computation. The acquirement of the required knowledge to do the investigations and to write this thesis was a demanding but valuable experience. The field of evolutionary algorithms is fascinating and the possibilities they offer are nearly boundless. Towards the end of the time reserved for this thesis, I had to detain myself not to do more interesting experiments, in order to have enough time for writing. I would like to thank my two tutors Johannes Bader and Dimo Brockhoff for their competent help in the literature research, the assistance in writing this thesis and the clarifying discussions
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