Reducing Local Optima in Single-Objective Problems by Multi-objectivization

By Joshua D. Knowles, Richard A. Watson, David Wolfe Corne. International Conference on Evolutionary Multi-Criterion Optimization, 2001

405 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/28e6a8f64853279b8c194037bbfe8710e4fcd2cc

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Citations in the corpus

Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems (2016, background): “Despite the advantages of these methods, optimization of real problems require addressing various difficulties: multiple-objectives [3], constraints [4], uncertainties [5], local solutions [6], deceptive global solutions [7], etc.”
Time–cost optimization: using GA and fuzzy sets theory for uncertainties in cost (2008, methodology): “An efficient approach to a TCTP problem requires a multi-objective optimization algorithm which allows for greater freedom in exploring possible solutions to reduce the likelihood of being trapped in local optima (Knowles et al., 2001).”
Abandoning Objectives: Evolution Through the Search for Novelty Alone (2011, methodology): “Incremental evolution, along with other methods employed in EC to deal with deception (Ficici and Pollack, 1998; Knowles et al., 2001; Mahfoud, 1995; Pelikan et al., 2001; Stewart, 2001), do not fix the underlying pathology of local optima; if local optima are pervasive, search will still likely be ”
Multiobjective Optimization in Bioinformatics and Computational Biology (2007, neutral): “algorithm [21].”
Automated Test Case Generation as a Many-Objective Optimisation Problem with Dynamic Selection of the Targets (2018, background): “In fact, reformulating a complex singleobjective problem as a many-objective problem defined on multiple but simpler objectives can reduce the probability that the search remains trapped in local optima, eventually leading to faster convergence [20], [27].”
Novelty-Based Multiobjectivization (2011, background): “We also compare these results to behavioral diversity [17, 18], another multiobjectivization [13, 11] that uses Pareto dominance to improve the exploration abilities of evolutionary algorithms.”
Statistical strategies for avoiding false discoveries in metabolomics and related experiments (2007, background): “…Deb, 2001; Coello Coello et al., 2002; Handl and Knowles, 2004, 2006a; Knowles and Hughes, 2005; Handl et al., 2006), noting especially that there are many conditions in which even single-objective problems can be given multiple objectives in order to aid in their solution (Knowles et al., 2001).”
Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies (2008, background): “been empirically disclosed in general optimization problems that the number of local optima can be reduced by converting multimodal single-objective problems into multiobjective ones [86], [ 87 ].”