Topic
Extremal optimization
About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.
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08 Jul 2009
TL;DR: This paper proposes the concept "Multi-Objectivization via Segmentation" (MOS), at which the original problem is reassembled, and experiments reveal that this new strategy clearly outperforms both the traditional genetic algorithm (GA) and the algorithms based on existing multiobjective approaches even without changing objectives.
Abstract: This paper studies the multi-objectivization of single-objective optimization problems (SOOP) using evolutionary multi-objective algorithms (EMOAs). In contrast to the single-objective case, diversity can be introduced by the multi-objective view of the algorithm and the dynamic use of objectives. Using the travelling salesman problem as an example we illustrate that two basic approaches, a) the addition of new objectives to the existing problem and b) the decomposition of the primary objective into sub-objectives, can improve performance compared to a single-objective genetic algorithm when objectives are used dynamically. Based on decomposition we propose the concept "Multi-Objectivization via Segmentation" (MOS), at which the original problem is reassembled. Experiments reveal that this new strategy clearly outperforms both the traditional genetic algorithm (GA) and the algorithms based on existing multiobjective approaches even without changing objectives.
40 citations
TL;DR: A time-variant reliability method for an industrial robot rotate vector (RV) reducer with multiple failure modes using a Kriging model that combines multiple response Gaussian process model and Monte Carlo simulation.
40 citations
09 Nov 2015
TL;DR: This paper attempts at proposing and evaluating from a bi-criteria perspective several multi-objective ACSs to tackle SD-MTSP when two objectives need to be simultaneously optimized: minimizing the total cost of traveled subtours while achieving balanced subtours.
Abstract: The single-depot multiple TSP (SD-MTSP) is a simple extension of the standard TSP, in which more than one salesman is allowed to visit the set of interconnected cities, such that each city is visited exactly once (by a single salesman) and the total cost of the traveled subtours is minimized. Although Ant Colony Systems (ACSs) are a natural choice for shortest-path problems, with TSP at its core, the application of ACS on this straightforward extension is not properly explored. The reasons may lie in the bi-criteria nature of the problem (shortest cost versus balanced subtours) and the lack of dedicated benchmarks exposing optimal solutions. This paper attempts at proposing and evaluating from a bi-criteria perspective several multi-objective ACSs to tackle SD-MTSP when two objectives need to be simultaneously optimized: minimizing the total cost of traveled subtours while achieving balanced subtours. Experiments are conducted towards investigating the efficiency of the algorithms in a multi-objective setting.
40 citations
Journal Article•
39 citations
TL;DR: A version of the extremal optimization (EO) algorithm introduced by Boettcher and Percus is tested on two- and three-dimensional spin glasses with Gaussian disorder, finding exact ground states with a speedup of order 10(4) (10(2) ) for 16-2 - (8(3) -) spin samples.
Abstract: A version of the extremal optimization (EO) algorithm introduced by Boettcher and Percus is tested on twoand three-dimensional spin glasses with Gaussian disorder. EO preferentially flips spins that are locally “unfit”; the variant introduced here reduces the probability of flipping previously selected spins. Relative to EO, this adaptive algorithm finds exact ground states with a speedup of order 10 4 s10 2 d for 16 2 - s8 3 -dspin samples. This speedup increases rapidly with system size, making this heuristic a useful tool in the study of materials with quenched disorder.
39 citations