Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Evolutionary algorithms in multiply-specified engineering. The MOEAs and WCES strategies
Advanced Engineering Informatics
Advanced Engineering Informatics
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Constraint multi-objective automated synthesis for CMOS operational amplifier
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
A non-dominated sorting bit matrix genetic algorithm for p2p relay optimization
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Computers and Industrial Engineering
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This paper proposes an improvement of an efficient multiobjective optimization algorithm, Non-dominated Sorting Genetic Algorithm II, NSGA-II, that has been here applied to solve the problem of optimal capacitors placement in distribution systems. The studied improvement involves the Crowded Comparison Operator and modifies it in order to handle several constraints. The problem of optimal location and sizing of capacitor banks for losses reduction and voltage profile flattening in medium voltage (MV) automated distribution systems is a difficult combinatorial constrained optimization problem which is deeply studied in literature. In this paper, the efficiency of the proposed Crowded Comparison Operator, CCO1, is compared to the efficiency of another Crowded Comparison Operator, CCO2, whose definition derives from the constraint-domination principle proposed by Deb et al. The two operators are tested on difficult test problems as well as on the optimal capacitors placement problem.