A Novel Quantum-Behaved Bat Algorithm with Mean Best Position Directed for Numerical Optimization

Bing-lian, Zhu; Zhu, Wei-Ping; Liu, Zijuan; Duan, Qingyan; Cao, Long Long

doi:10.1155/2016/6097484

Cited by 32 publications

(30 citation statements)

References 29 publications

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“…Wang and Guo [60] integrated the harmony search (HS) method into BA, and developed a hybrid metaheuristic (HSBA) method to increase the convergence speed of BA. Zhu et al [61] improved the exploration capability of BA by modifying its equations. Afrabandpey et al [54] introduced the chaotic sequences into BA in order to escape local convergence.…”

Section: Bat Algorithm Methodsmentioning

confidence: 99%

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

2017

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Advanced global optimization algorithms have been continuously introduced and improved to solve various complex design optimization problems for which the objective and constraint functions can only be evaluated through computation intensive numerical analyses or simulations with a large number of design variables. The often implicit, multimodal, and ill-shaped objective and constraint functions in high-dimensional and "black-box" forms demand the search to be carried out using low number of function evaluations with high search efficiency and good robustness. This work investigates the performance of six recently introduced, nature-inspired global optimization methods: Artificial Bee Colony (ABC), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Flower Pollination Algorithm (FPA) and Grey Wolf Optimizer (GWO). These approaches are compared in terms of search efficiency and robustness in solving a set of representative benchmark problems in smooth-unimodal, non-smooth unimodal, smooth multimodal, and non-smooth multimodal function forms. In addition, four classic engineering optimization examples and a real-life complex mechanical system design optimization problem, floating offshore wind turbines design optimization, are used as additional test cases representing computationally-expensive black-box global optimization problems. Results from this comparative study show that the ability of these global optimization methods to obtain a good solution diminishes as the dimension of the problem, or number of design variables increases. Although none of these methods is universally capable, the study finds that GWO and ABC are more efficient on average than the other four in obtaining high quality solutions efficiently and consistently, solving 86% and 80% of the tested benchmark problems, respectively. The research contributes to future improvements of global optimization methods.

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Section: Bat Algorithm Methodsmentioning

confidence: 99%

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

2017

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“…Xie et al [32] also incorporated the Lévy flight in the velocity update equation, but four randomly selected bats were used to guide the search pattern. Zhu et al [33] replace the swarm historical best position with the mean best position to enhance the convergence speed.…”

Section: (Ii) Formula Adjustmentmentioning

confidence: 99%

A Novel Bat Algorithm with Multiple Strategies Coupling for Numerical Optimization

et al. 2019

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A bat algorithm (BA) is a heuristic algorithm that operates by imitating the echolocation behavior of bats to perform global optimization. The BA is widely used in various optimization problems because of its excellent performance. In the bat algorithm, the global search capability is determined by the parameter loudness and frequency. However, experiments show that each operator in the algorithm can only improve the performance of the algorithm at a certain time. In this paper, a novel bat algorithm with multiple strategies coupling (mixBA) is proposed to solve this problem. To prove the effectiveness of the algorithm, we compared it with CEC2013 benchmarks test suits. Furthermore, the Wilcoxon and Friedman tests were conducted to distinguish the differences between it and other algorithms. The results prove that the proposed algorithm is significantly superior to others on the majority of benchmark functions.

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“…In an attempt to ameliorate its performance, various variants of BA have been proposed in the literature such as K-Means bat algorithm (KMBA) [75], fuzzy logic bat algorithm (FLBA) [77], multi-objective bat algorithm (MOBA) [69], binary bat algorithm (BBA) [82], bat algorithm with differential operator and Lévy flights (DLBA) [83], improved bat algorithm (IBA) [84], quantumbehaved bat algorithm [85]- [87], etc.…”

Section: Introductionmentioning

confidence: 99%

Optimal Reactive Power Dispatch Using Chaotic Bat Algorithm

Mugemanyi

Rugema

et al. 2020

IEEE Access

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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.

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A Novel Quantum-Behaved Bat Algorithm with Mean Best Position Directed for Numerical Optimization

Cited by 32 publications

References 29 publications

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

A Novel Bat Algorithm with Multiple Strategies Coupling for Numerical Optimization

Optimal Reactive Power Dispatch Using Chaotic Bat Algorithm

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