Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Journal of Global Optimization
Multiobjective optimization using a Pareto differential evolution approach
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Dynamic Crowding Distance?A New Diversity Maintenance Strategy for MOEAs
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 01
Soft Computing - A Fusion of Foundations, Methodologies and Applications
International Journal of Bio-Inspired Computation
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An Improved Multiobjective Generalized Differential Evolution (I- GDE3) approach is proposed in this paper. For maintaining good diversity, the concepts of Simulated Binary Crossover (SBX) based recombination and Dynamic Crowding Distance (DCD) are implemented in GDE3 algorithm. The proposed approach is applied to different sets of classical test problems suggested in the MOEA literature to validate the performance of the I-GDE3. Later, the proposed approach is implemented to Reactive Power Planning (RPP) problem. The objective functions are minimization of combined operating and VAR allocation cost and bus voltage profile improvement. The performance of the proposed approach is tested in standard IEEE 30-bus test systems. The performance of I-GDE3 is compared with respect to multi- objective performance measures namely gamma, spread, minimum spacing and Inverted Generational Distance (IGD). The results show the effectiveness of I-GDE3 and confirm its potential to solve the multi-objective problems.