Open Access
SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization
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The article was published on 2002-01-01 and is currently open access. It has received 1972 citations till now. The article focuses on the topics: Pareto principle & Multi-objective optimization.read more
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Journal ArticleDOI
A two-objective evolutionary approach based on topological constraints for node localization in wireless sensor networks
TL;DR: A two-objective evolutionary algorithm is proposed which takes concurrently into account during the evolutionary process both the localization accuracy and certain topological constraints induced by connectivity considerations, thus manifesting its effectiveness and stability.
Proceedings ArticleDOI
Heuristics for optimizing the calculation of hypervolume for multi-objective optimization problems
TL;DR: It is shown that both heuristics make a substantial difference to the performance of HSO for randomly-generated and benchmark data in 5-9 objectives, and that they both enable HSO to reliably avoid the worst-case performance for those fronts.
Proceedings ArticleDOI
Hybridization of genetic algorithm and local search in multiobjective function optimization: recommendation of GA then LS
TL;DR: The reasons why GA is not suitable for obtaining solutions of high precision are revealed, thereby justifying hybridization ofGA and LS and it is suggested that the hybridization scheme which maximally exploits both GA and LS is GA then LS.
Journal ArticleDOI
A multiobjective memetic algorithm for PPI network alignment
Connor Clark,Jugal Kalita +1 more
TL;DR: Optnetalign is presented, a multiobjective memetic algorithm for the problem of PPI network alignment that uses extremely efficient swap-based local search, mutation and crossover operations to create a population of alignments and optimizes the conflicting goals of topological and sequence similarity using the concept of Pareto dominance.
Journal ArticleDOI
An Evolutionary Algorithm with Double-Level Archives for Multiobjective Optimization
TL;DR: The results verify that the proposed MOEA with double-level archives offers competitive advantages in distance to the PF, solution coverage, and search speed, compared with state-of-the-art MOEAs.