Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
DE/EDA: a new evolutionary algorithm for global optimization
Information Sciences—Informatics and Computer Science: An International Journal
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
A new design optimization framework based on immune algorithm and Taguchi's method
Computers in Industry
Size optimization of space trusses using Big Bang-Big Crunch algorithm
Computers and Structures
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
A hybrid genetic algorithm with the Baldwin effect
Information Sciences: an International Journal
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Performance assessment of DMOEA-DD with CEC 2009 MOEA competition test instances
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Recent advances in differential evolution: a survey and experimental analysis
Artificial Intelligence Review
Information Sciences: an International Journal
Memetic compact differential evolution for cartesian robot control
IEEE Computational Intelligence Magazine
Optimization Methods & Software - The International Conference on Engineering Optimization (EngOpt 2008)
Truss optimization with dynamic constraints using a particle swarm algorithm
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Differential evolution algorithm with ensemble of parameters and mutation strategies
Applied Soft Computing
Structural and Multidisciplinary Optimization
Differential evolution for parameterized procedural woody plant models reconstruction
Applied Soft Computing
Self-adaptive differential evolution algorithm using population size reduction and three strategies
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
DEMO: differential evolution for multiobjective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Hybrid population-based incremental learning using real codes
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Constrained optimization based on modified differential evolution algorithm
Information Sciences: an International Journal
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
A new evolutionary search strategy for global optimization of high-dimensional problems
Information Sciences: an International Journal
Information Sciences: an International Journal
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This paper proposes a hybrid evolutionary algorithm for multiobjective optimisation of trusses using real-code population-based incremental learning (RPBIL) to solve multiobjective design problems. Differential evolution (DE) operators are integrated into the main procedure of RPBIL leading to a hybrid algorithm. The newly developed optimiser, along with some established multiobjective evolutionary algorithms (MOEAs) is implemented to solve a number of multiobjective design problems of trusses. Comparative performance based upon a hypervolume indicator shows that the new hybrid multiobjective evolutionary algorithm is superior to the other MOEAs particularly in cases involving large-scale truss design problems.