Moth flame optimization to solve optimal power flow with non-parametric statistical evaluation validation

Buch, Hitarth; Trivedi, Indrajit N.; Jangir, Pradeep

doi:10.1080/23311916.2017.1286731

Cited by 62 publications

(30 citation statements)

References 39 publications

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“…For solving OPF problems, different metaheuristic algorithms have been successfully employed. These include particle swarm optimization (PSO) [21]- [23], flower pollination algorithm (FPA) [24], moth-flame optimization (MFO) [25], firefly algorithm (FA) [26], whale optimization algorithm (WOA) [27], symbiotic organisms search (SOS) [28], jaya algorithm (JA) [29], grey wolf optimization (GWO) [30], backtracking search algorithm (BSA) [31], and many more. To the best of authors' knowledge, HHO has not been implemented in any optimization problems of this category.…”

Section: Related Workmentioning

confidence: 99%

Long-Term Memory Harris’ Hawk Optimization for High Dimensional and Optimal Power Flow Problems

2019

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Harris' hawk optimization (HHO) is a recent addition to population-based metaheuristic paradigm, inspired from hunting behavior of Harris' hawks. It has demonstrated promising search behavior while employed on various optimization problems, however the diversity of search agents can be further enhanced. This paper represents a novel modified variant with a long-term memory concept, hence called long-term memory HHO (LMHHO), which provides information about multiple promising regions in problem landscape, for improvised search results. With this information, LMHHO maintains exploration up to a certain level even until search termination, thus produces better results than the original method. Moreover, the study proves that appropriate tools for in-depth performance analysis can help improve search efficiency of existing metaheuristic algorithms by making simple yet effective modification in search strategy. The diversity measurement and exploration-exploitation investigations prove that the proposed LMHHO maintains trade-off balance between exploration and exploitation. The proposed approach is investigated on high-dimensional numerical optimization problems, including classic benchmark and CEC'17 functions; also, on optimal power flow problem in power generation system. The experimental study suggests that LMHHO not only outperforms the original HHO but also various other established and recently introduced metaheuristic algorithms. Although, the research can be extended by implementing more efficient memory archive and retrieval approaches for enhanced results. INDEX TERMS Diversity measurement, exploration-exploitation, long-term memory, Harris' hawk optimization, optimal power flow.

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Section: Related Workmentioning

confidence: 99%

Long-Term Memory Harris’ Hawk Optimization for High Dimensional and Optimal Power Flow Problems

2019

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“…However, the same is not possible if the distance is very less and moreover, these flies get trapped in an unwanted spiral path when they get attracted towards artificial lights. In this algorithm [16][17][18], the moths are considered as the solutions (amplitudes and phases) and the positions of the moths as variables used in the problem. Moreover, both the moths and the flames are treated as solutions.…”

Section: Mfo Algorithmmentioning

confidence: 99%

“…Moth-flame optimization (MFO) algorithm [16][17][18] is used in this synthesis process. It is duly compared to wellknown algorithms, namely Artificial Bee Colony algorithm (ABC) [19][20][21] and Imperialist Competitive algorithm (ICA) [22][23][24] for verifying its performance.…”

Section: Introductionmentioning

confidence: 99%

Design of phase-only reconfigurable planar array antenna in selected phi cuts using various meta-heuristic optimization algorithms

2019

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In this paper, a pattern synthesis based on a multiobjective optimization algorithm is proposed for the generation of a reconfigurable pencil/flat top dual-beam planar antenna array built using isotropic antenna elements in selected phi cuts. These beams claim the same amplitude excitations and differ from each other in phase excitations. Zero-phase excitations are used in pencil beam and these phases are updated with optimum phases for the flat top beam. All the excitations are obtained using Moth-flame optimization algorithm. With the support of the fitness functions, care is taken to control the expected values of the radiation pattern parameters to remain under certain fixed limit. In addition, synthesis is also done for the provision of a null in a particular direction for rejection of interference in the pencil beam in two different phi cuts. To suppress the mutual coupling effects, dynamic range ratio is kept under a threshold limit. Simulation results show the effectiveness of this proposed synthesis for phi cut planes. This algorithm is compared and proved to be better in many aspects over the standard meta-heuristic algorithms like Artificial Bee Colony and Imperialist Competitive algorithms in terms of performance parameters.

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“…The metaheuristic optimization algorithms are inspired based on animals' behavior and physical phenomena have become widespread popular due to their flexibility, simplicity, ability to get global solutions, and prevent local optimal solutions [18]. The essence of metaheuristic techniques is based on the iterative correction solutions concept through generating new populations with implementing stochastic search operators [19]. Over recent years, there are growing attention on population-based and metaheuristics techniques for solving different power system optimization problems.…”

Section: Introductionmentioning

confidence: 99%

An Improved Heap-Based Optimizer for Optimal Reactive Power Dispatch

et al. 2021

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Optimal reactive power dispatch (ORPD) in a typical power system is a complicated multi-objective optimization problem. The proper modeling of the multi-objective optimization problem has a significant impact on system operation and control. In this paper, an Improved Heap-based optimizer (IHBO) is proposed to improve the performance of a recently published technique called Heap-based optimizer (HBO). In addition, two algorithms based on the original HBO and IHBO are developed for solving OPRD problem. Pareto front approach is utilized in the proposed OPRD algorithm with the aim of solving two or three objective functions simultaneously. The performance of HBO is improved by utilizing the chaotic sequences with the aim of improving its global search capability and avoiding getting stuck in a local optimum. Both original HBO and proposed IHBO are applied to determine the optimal settings of the generator's voltages, shunt capacitor reactive power, and tap settings of transformers. Therefore, this study aims for minimizing three most objective functions of the real power loss, total voltage deviation (TVD) and voltage stability index (VSI), with satisfying different operational constraints. The effectiveness of the IHBO is tested on three test systems IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus test systems. The results of the proposed IHBO are compared with recently published algorithms in the literature. The simulation results proven the superiority and robustness of IHBO in solving the ORPD problem.INDEX TERMS Reactive Power Dispatch, optimization, Heap-based optimizer, chaotic sequence.

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Moth flame optimization to solve optimal power flow with non-parametric statistical evaluation validation

Cited by 62 publications

References 39 publications

Long-Term Memory Harris’ Hawk Optimization for High Dimensional and Optimal Power Flow Problems

Long-Term Memory Harris’ Hawk Optimization for High Dimensional and Optimal Power Flow Problems

Design of phase-only reconfigurable planar array antenna in selected phi cuts using various meta-heuristic optimization algorithms

An Improved Heap-Based Optimizer for Optimal Reactive Power Dispatch

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