Gaussian Quantum Bat Algorithm with Direction of Mean Best Position for Numerical Function Optimization

Huang, Xingwang; Li, Chaopeng; Pu, Yunming; He, Bin

doi:10.1155/2019/5652340

Cited by 11 publications

(4 citation statements)

References 31 publications

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“…The addition of a random vector enforcing the Gaussian distribution to the initial individual's condition is known as Gaussian variation [116]. Adding a Gaussian mutation to optimizations, such as bat algorithm [117], grasshopper optimization algorithm [118], and moth flame optimization algorithm [119], has enhanced the ability of the respective basic version, according to past studies. The position of search agents/population HungerPosition of the HGS algorithm is described as follows.…”

Section: Gaussian and Mutation‐based Hunger Games Search Optimizermentioning

confidence: 99%

Constraint estimation in three‐diode solar photovoltaic model using Gaussian and Cauchy mutation‐based hunger games search optimizer and enhanced Newton–Raphson method

Premkumar

Jangir²,

Kumar³

et al. 2022

IET Renewable Power Gen

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The reliability of the photovoltaic models is strongly reliant on their parameters, which are primarily determined by the optimization algorithm and the objective function. As a result, obtaining the parameters under different environmental conditions is critical for increasing their performance, reliability and significantly lowering cost. Many optimization techniques are reported to address this problem based on the complexity. As a result, an enhanced version of the recently reported Hunger Games Search Optimizer (HGSO) method called Gaussian and Cauchy Mutation‐based HGSO (GCMHGSO) algorithm for defining the requirements of the Three‐Diode equivalent Model (TDeM) by utilizing multiple representations in the algorithm along with an efficient objective function. The Cauchy mutation increases the exploration ability, and Gaussian mutation increases the exploitation ability of the basic HGSO. Furthermore, an Enhanced Newton–Raphson Method (ENRM) is presented to effectively solve the behaviour of the current–voltage relation of the TDeM. The robust optimization is also considered to demonstrate the impact of the measurement error. Comparing the GCMHGSO‐ENRM to other competitors reveals that the proposed GCMHGSO‐ENRM can accurately find the best solution, and its effectiveness is verified in many statistical parameters. It is found that the GCMHGSO‐ENRM algorithm is stable and robust compared to other competitors.

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Section: Gaussian and Mutation‐based Hunger Games Search Optimizermentioning

confidence: 99%

Constraint estimation in three‐diode solar photovoltaic model using Gaussian and Cauchy mutation‐based hunger games search optimizer and enhanced Newton–Raphson method

Premkumar

Jangir²,

Kumar³

et al. 2022

IET Renewable Power Gen

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“…Expanding the search space can improve the ability of the algorithm to jump out of the local optimum. Based on the above findings, this paper proposes the Gaussian quantum bat algorithm with direction of mean best position (GQMBA) for quantum behavior bats using a Gaussian distribution [ 32 ].…”

Section: Optimization Algorithm Based On the Bat Algorithm And The Di...mentioning

confidence: 99%

A Divide-and-Conquer Bat Algorithm with Direction of Mean Best Position for Optimization of Cutting Parameters in CNC Turnings

Huang

Yong

et al. 2022

Computational Intelligence and Neuroscience

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Optimization of machining parameters is an important problem in the modern manufacturing world due to production efficiency and economics. This problem is well known to be complex and is regarded as a strongly nondeterministic polynomial (NP)-hard problem. To reduce the production cost of work-pieces in computer numerical control (CNC) machining, a novel optimization algorithm based on a combination of the bat algorithm and a divide-and-conquer strategy is proposed. First, the basic bat algorithm (BA) is modified with the aim to avoid finding the local optimal solution. In addition, a Gaussian quantum bat algorithm with direction of mean best position is developed. Second, in order to reduce the complexity of the optimization problem, the whole optimization problem is divided into several subproblems by using a divide-and-conquer strategy according to the characteristic of multipass turning operations. Finally, under a large number of machining constraints, the cutting parameters of the two stages of roughing and finishing are simultaneously optimized. Simulation results show that the proposed algorithm can find better combinations of the machining parameters than other algorithms proposed previously to further reduce the production cost. In addition, the outcome of our work presents a novel way to solve the complex optimization problem of machining parameters with a combination of traditional mathematical methods and swarm intelligence algorithms.

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“…The quantum-behaved bat algorithm (QBA) is inspired by [73]- [75]. Some variants of the quantum-behaved bat algorithm are addressed in [76]- [78]. In [76], the frequency equation of the proposed algorithm includes the bats' capability of self-adaptive compensation for Doppler effects in echoes.…”

Section: B Quantum-behaved Bat Algorithmmentioning

confidence: 99%

“…The position of each bat is determined by both the current optimal solution and the mean best position, and the incorporation of quantum-behaved bats enables improvement of the population diversity and prevents the bats from falling into local minima. The improved version of [77] is addressed in [78].…”

Section: B Quantum-behaved Bat Algorithmmentioning

confidence: 99%

A Cauchy-Gaussian Quantum-Behaved Bat Algorithm Applied to Solve the Economic Load Dispatch Problem

Rugema¹,

Yan²,

Mugemanyi³

et al. 2021

IEEE Access

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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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Gaussian Quantum Bat Algorithm with Direction of Mean Best Position for Numerical Function Optimization

Cited by 11 publications

References 31 publications

Constraint estimation in three‐diode solar photovoltaic model using Gaussian and Cauchy mutation‐based hunger games search optimizer and enhanced Newton–Raphson method

Constraint estimation in three‐diode solar photovoltaic model using Gaussian and Cauchy mutation‐based hunger games search optimizer and enhanced Newton–Raphson method

A Divide-and-Conquer Bat Algorithm with Direction of Mean Best Position for Optimization of Cutting Parameters in CNC Turnings

A Cauchy-Gaussian Quantum-Behaved Bat Algorithm Applied to Solve the Economic Load Dispatch Problem

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