L. Thiele

Publications694
h-index78
Citations51,785
Highly Influential Citations4,732
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SPEA2: Improving the strength pareto evolutionary algorithm
TLDR
An improved version of SPEA, namely SPEA2, is proposed, which incorporates in contrast to its predecessor a fine-grained fitness assignment strategy, a density estimation technique, and an enhanced archive truncation method. Expand
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
TLDR
The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface. Expand
SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization
Performance assessment of multiobjective optimizers: an analysis and review
TLDR
This study provides a rigorous analysis of the limitations underlying this type of quality assessment in multiobjective evolutionary algorithms and develops a mathematical framework which allows one to classify and discuss existing techniques. Expand
Scalable multi-objective optimization test problems
TLDR
Three different approaches for systematically designing test problems for systematic designing multi-objective evolutionary algorithms (MOEAs) showing efficacy in handling problems having more than two objectives are suggested. Expand
Comparison of multiobjective evolutionary algorithms: empirical results
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
TLDR
This paper provides a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions and shows that elitism is shown to be an important factor for improving evolutionary multiobjectives search. Expand
Scalable Test Problems for Evolutionary Multiobjective Optimization
TLDR
Three different approaches for systematically designing test problems for systematically demonstrating the efficacy of multiobjective evolutionary algorithms in handling problems having more than two objectives are suggested. Expand
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
TLDR
In this paper an extensive, quantitative comparison is presented, applying four multiobjective evolutionary algorithms to an extended 0/1 knapsack problem. Expand
Efficient network flooding and time synchronization with Glossy
This paper presents Glossy, a novel flooding architecture for wireless sensor networks. Glossy exploits constructive interference of IEEE 802.15.4 symbols for fast network flooding and implicit time… Expand
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