In this paper, the chaotic bat algorithm (CBA) is applied to solve the optimal reactive power dispatch (ORPD) problem taking into account small-scale, medium-scale and large-scale power systems. ORPD plays a key role in the power system operation and control. The ORPD problem is formulated as a mixed integer nonlinear programming problem, comprising both continuous and discrete control variables. The most outstanding benefit of the bat algorithm (BA) is its good convergence for optimal solutions. The BA, however, together with other metaheuristics, often gets stuck into local optima and in order to cope with this shortcoming, the use of the CBA is proposed in this paper. The CBA results from introducing the chaotic sequences into the standard BA to enhance its global search ability. The CBA is utilized to find the optimal settings of generator bus voltages, tap setting transformers and shunt reactive power sources. Three objective functions such as minimization of active power loss, total voltage deviations and voltage stability index are considered in this study. The effectiveness of the CBA technique is demonstrated for standard IEEE 14-bus, IEEE 39 New England bus, IEEE 57-bus, IEEE 118-bus and IEEE 300-bus test systems. The results yielded by the CBA are compared with other algorithms available in the literature. Simulation results reveal the effectiveness and robustness of the CBA for solving the ORPD problem. INDEX TERMS Chaotic bat algorithm, optimal reactive power dispatch, chaotic sequences.

In this paper, a novel Cauchy-Gaussian quantum-behaved bat algorithm (CGQBA) is applied to solve the economic load dispatch (ELD) problem. The bat algorithm (BA) is an acknowledged metaheuristic optimization algorithm owing to its performance. However, the classical BA presents some weaknesses, such as premature convergence. To withstand the drawbacks of the BA, quantum mechanics theories and Gaussian and Cauchy operators are integrated into the standard BA to enhance its effectiveness. Since the economic load dispatch is a nonlinear, complex and constrained optimization problem, its main objective is to reduce the total generation cost while matching the equality and inequality constraints of the system. The validity of the CGQBA is tested on six standard benchmark functions with different characteristics. The numerical results indicate that the CGQBA is effective and superior to many other algorithms. Moreover, the CGQBA is applied to solve the ELD problems on various test systems including 3,6,20, 40,110 and 160 implemented generating units. The simulation results illustrate the strength of the CGQBA compared with other algorithms recently reported in the literature.

In this paper, a novel Cauchy-Gaussian quantum-behaved bat algorithm (CGQBA) is applied to solve the economic load dispatch (ELD) problem. The bat algorithm (BA) is an acknowledged metaheuristic optimization algorithm owing to its performance. However, the classical BA presents some weaknesses, such as premature convergence. To withstand the drawbacks of the BA, quantum mechanics theories and Gaussian and Cauchy operators are integrated into the standard BA to enhance its effectiveness. Since the economic load dispatch is a nonlinear, complex and constrained optimization problem, its main objective is to reduce the total generation cost while matching the equality and inequality constraints of the system. The validity of the CGQBA is tested on six standard benchmark functions with different characteristics. The numerical results indicate that the CGQBA is effective and superior to many other algorithms. Moreover, the CGQBA is applied to solve the ELD problems on various test systems including 3,6,20, 40,110 and 160 implemented generating units. The simulation results illustrate the strength of the CGQBA compared with other algorithms recently reported in the literature.

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The particle swarm optimization (PSO) is a population-based algorithm belonging into metaheuristic algorithms and it has been used since many decades for handling and solving various optimization problems. However, it suffers from premature convergence and it can easily be trapped into local optimum. Therefore, this study presents a new algorithm called multi-mean scout particle swarm optimization (MMSCPSO) which solves reactive power optimization problem in a practical power system. The main objective is to minimize the active power losses in transmission line while satisfying various constraints. Control variables to be adjusted are voltage at all generator buses, transformer tap position and shunt capacitor.  The standard PSO has a better exploitation ability but it has a very poor exploration  ability. Consequently, to maintain the balance between these two abilities during the  search process by helping particles to escape from the local optimum trap, modifications were made where initial population was produced by tent and logistic maps and it was subdividing it into sub-swarms to ensure good distribution of particles within the search space. Beside this, the idle particles (particles unable to improve their personal best) were replaced by insertion of a scout phase inspired from the artificial bee colony in the standard PSO. This algorithm has been applied and tested on IEEE 118-bus system and it has shown a strong performance in terms of active power loss minimization and voltage profile improvement compared to the original PSO Algorithm, whereby the MMSCPSO algorithm reduced the active power losses at 18.681% then the PSO algorithm reduced the active power losses at 15.457%. Hence, the MMSCPSO could be a better solution for reactive power optimization in large-scale power systems.