Journal of Global Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Analysis of Variance Designs: A Conceptual and Computational Approach with SPSS and SAS
Analysis of Variance Designs: A Conceptual and Computational Approach with SPSS and SAS
Approximating the Volume of Unions and Intersections of High-Dimensional Geometric Objects
ISAAC '08 Proceedings of the 19th International Symposium on Algorithms and Computation
Approximating the Least Hypervolume Contributor: NP-Hard in General, But Fast in Practice
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
The Art of Concurrency: A Thread Monkey's Guide to Writing Parallel Applications
The Art of Concurrency: A Thread Monkey's Guide to Writing Parallel Applications
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Differential evolution versus genetic algorithms in multiobjective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Pareto-, aggregation-, and indicator-based methods in many-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Concurrent implementation of differential evolution
ISTASC'10 Proceedings of the 10th WSEAS international conference on Systems theory and scientific computation
Optimum design of balanced SAW filters using multi-objective differential evolution
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Solving rotated multi-objective optimization problems using differential evolution
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A faster algorithm for calculating hypervolume
IEEE Transactions on Evolutionary Computation
A Fast Incremental Hypervolume Algorithm
IEEE Transactions on Evolutionary Computation
Many-hard-objective optimization using differential evolution based on two-stage constraint-handling
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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A new Multi-Objective Evolutionary Algorithm (MOEA) based on Differential Evolution (DE), i.e., Indicator-Based DE (IBDE) is proposed. IBDE employs a strategy of DE for generating a series of offspring. In order to evaluate the quality of each individual in the population, IBDE uses the exclusive hypervolume as an indicator function. A fast algorithm called Incremental Hypervolume by Slicing Objectives (IHSO) has been reported for calculating the exclusive hypervolume. However, the computational time spent by IHSO increases exponentially with the number of objectives and considered individuals. Therefore, an exclusive hypervolume approximation, in which IHSO can be also used effectively, is proposed. Furthermore, it is proven that the proposed exclusive hypervolume approximation gives an upper bound of the accurate exclusive hypervolume. The procedure of IHSO is parallelized by using the multiple threads of the Java language. By using the parallelized IHSO, not only the exclusive hypervolume but also the exclusive hypervolume approximation can be calculated concurrently on a multi-core processor. By the results of numerical experiments and statistical tests conducted on test problems, the usefulness of the proposed approach is demonstrated.