This paper presents comparison of Harmony search algorithm (HSA), improved harmony search (IHS) algorithm, biogeographybasedoptimization (BBO) algorithm for solving constrained economic load dispatch problems in the...
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This paper presents comparison of Harmony search algorithm (HSA), improved harmony search (IHS) algorithm, biogeographybasedoptimization (BBO) algorithm for solving constrained economic load dispatch problems in the power system. In the IHS algorithm multiple harmony memory consideration rates and dynamic pitch adjusting rate are used to generate new solution vector. This proposed algorithms have been successfully tested in the test system which consists of twenty generating units with ramp rate limits and valve point loading constraint. The results obtained through the simulation results reveal that IHS algorithm has minimum total fuel cost and has good convergence characteristics when compared to both Harmony search algorithm and biogeography based optimization algorithm.
The direct torque control of the dual star induction motor(DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parame...
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The direct torque control of the dual star induction motor(DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parametric variations. Among the most evoked control strategies adopted in this field to overcome these drawbacks presented in classical drive, it is worth mentioning the use of the second order sliding mode control(SOSMC) based on the super twisting algorithm(STA) combined with the fuzzy logic control(FSOSMC). In order to realize the optimal control performance, the FSOSMC parameters are adjusted using an optimizationalgorithmbased on the genetic algorithm(GA). The performances of the envisaged control scheme, called G-FSOSMC, are investigated against G-SOSMC, G-PI and BBO-FSOSMC algorithms. The proposed controller scheme is efficient in reducing the torque and flux ripples, and successfully suppresses chattering. The effects of parametric uncertainties do not affect system performance.
This paper explores the performance of three evolutionary optimization methods, differential evolution (DE), evolutionary strategy (ES) and biogeography based optimization algorithm (BBO), for nonlinear constrained op...
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This paper explores the performance of three evolutionary optimization methods, differential evolution (DE), evolutionary strategy (ES) and biogeography based optimization algorithm (BBO), for nonlinear constrained optimum design of a cantilever retaining wall. These algorithms are based on biological contests for survival and reproduction. The retaining wall optimization problem consists of two criteria, geotechnical stability and structural strength, while the final design minimizes an objective function. The objective function is defined in terms of both cost and weight. Constraints are applied using the penalty function method. The efficiency of the proposed method is examined by means of two numerical retaining wall design examples, one with a base shear key and one without a base shear key. The final designs are compared to the ones determined by genetic algorithms as classical metaheuristic optimization methods. The design results and convergence rate of the BBO algorithm show a significantly better performance than the other algorithms in both design cases.
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