A Bio-Inspired Method for Mathematical Optimization Inspired by Arachnida Salticidade

Peraza-Vázquez, Hernán; Peña-Delgado, Adrián F.; Ranjan, Prakash; Barde, Chetan; Choubey, Arvind; Morales‐Cepeda, Ana Beatriz

doi:10.3390/math10010102

Cited by 40 publications

(20 citation statements)

References 50 publications

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“…MH algorithm is a pseudo‐traversal method based on specific nonlinear iteration laws. Many bio‐inspired population‐based MHs have been applied to solve complex problems such as particle swarm optimization (PSO) [22], the grey wolf algorithm (GWA) [23], the jumping spider optimization algorithm (JSOA) [24], the honey badger algorithm (HBA) [25], the pelican optimization algorithm (POA) [26], the prairie dog optimization algorithm (PDOA) [27], the artificial hummingbird algorithm (AHA) [28], and the carnivorous plant algorithm (CPA) [29]. Each MH has its unique search laws, which create specific steps for its nonlinear iterations.…”

Section: Introductionmentioning

confidence: 99%

A novel global FPPT strategy for PV system under partial shading condition

Xing,

Jia,

Yang

2023

IET Renewable Power Gen

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Flexible power point tracking (FPPT) is a common and significate photovoltaic control problem under partial shading conditions. Based on CPP and MPP distribution analysis, both constant power point tracking and maximum power point tracking could be mathematically described by a single objective optimization. As one of the recent meta‐heuristic algorithms, which avoid the limitations of conventional scanning methods, the carnivorous plant algorithm (CPA) solves non‐convex optimization problems with fast speed, high accuracy, and simple calculation. However, it exhibits several drawbacks such as inappropriate parameters, over‐searching, and feasible solution ignored issues. Therefore, a global FPPT strategy based on modified CPA (mCPA) is proposed, which includes a search objective integration, efficient non‐convex search space skipping strategy, and adaptive tuning parameters according to sensitivity analysis. By statistical experiments compared with eight popular meta‐heuristic algorithms, the mCPA can correctly converge to the global MPP under various scenarios with the highest accuracy of 99.0%. Meanwhile, the observations of dynamic experiments demonstrate the effectiveness of the proposed strategy in terms of fast time response (0.5–3 s) for global FPPT under various uniform and non‐uniform insolation conditions.

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Section: Introductionmentioning

confidence: 99%

A novel global FPPT strategy for PV system under partial shading condition

Xing,

Jia,

Yang

2023

IET Renewable Power Gen

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“…• The parameters of the generator are determined by the optimization algorithms in a supportive way in regards to the user experience, taking into account the determined objective function and inequality criteria. • According to authors' best knowledge for the first time in the literature, INFO, 27 JSOA, 28 TLBO, 29 TLSBO, 30 and POA 31 optimization algorithms were employed to simultaneously consider parameters related to stator slot structure and magnet position and structure, utilizing eight different design parameters as variables. • The level and direction of the relationship between design variables and performance variables has been determined using correlation analysis.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, the design of PMSG was carried out using the INFO, JSOA, TLBO, TLSBO, and POA optimization algorithms. The main contributions of the article: The parameters of the generator are determined by the optimization algorithms in a supportive way in regards to the user experience, taking into account the determined objective function and inequality criteria. According to authors’ best knowledge for the first time in the literature, INFO, 27 JSOA, 28 TLBO, 29 TLSBO, 30 and POA 31 optimization algorithms were employed to simultaneously consider parameters related to stator slot structure and magnet position and structure, utilizing eight different design parameters as variables. The level and direction of the relationship between design variables and performance variables has been determined using correlation analysis. Thus, it has been determined how designers should change which design parameters, by how much, and in which direction to improve efficiency, THD, and output power. In the intended optimization problem, a single objective function, which combines efficiency, the THD and output power, has been proposed. …”

Section: Introductionmentioning

confidence: 99%

Optimum design of slot and magnet structure of permanent magnet synchronous generator with teaching‐learning‐studying‐based optimization

Duman,

Dalcalı

2023

Int J Numerical Modelling

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From the point of production to consumption, electrical energy should be utilized effectively and qualitatively. Efficiency at the electricity consumption stage is not sufficient, and the desired criteria must be met at the production stage. The efficient and high‐quality generation of electrical energy is directly related to the performance of the generator used. In order to achieve the desired criteria for a generator design, many issues should be considered together. In order for this study to do, a 14‐pole and 5 kW permanent magnet synchronous generator (PMSG) was designed using the innovative optimizer named the weighted mean of vectors (INFO), the jumping spider optimization algorithm (JSOA), the teaching–learning‐based optimization (TLBO), the pelican optimization algorithm (POA) and the teaching‐learning‐studying‐based optimization (TLSBO) algorithms. The generator performance was optimized within the determined inequality constraints by the offset and embrace value and six parameters of the stator slot structure. The generator designed with the parameters determined by the TLSBO was more successful than the generators designed according to all the other proposed algorithms. The total harmonic distortion (THD) value decreased by 51.983% compared to the initial design at approximately the same efficiency.

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“…The jumping spider optimization algorithm (JSOA) [22] is a new intelligent optimization algorithm which was proposed in 2021. It has the characteristics of fast convergence and a strong optimization ability.…”

Section: Introductionmentioning

confidence: 99%

Design of Gas Turbine Cooling System Based on Improved Jumping Spider Optimization Algorithm

Liu

Chen³

2022

Machines

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The gas turbine cooling system is a complex MIMO system with a strong coupling, nonlinear, time-varying and large disturbance amplitude. In order to automatically control the target flow, target temperature and pipeline pressure, in this paper, the decoupler and regulator of a gas turbine cooling system are designed. Firstly, the working principle of a gas turbine cooling system and the coupling between the controlled variables of the system are analyzed. The decoupler of the system is designed by using the diagonal matrix decoupling method. The transfer function models of the coupling system are built through system identification, and the decoupling matrix of the system is calculated according to the diagonal matrix decoupling method and transfer function models. Then, the engine cooling control system simulation model is constructed and an improved jumping spider optimization algorithm is proposed. The parameters of the controller are optimized by the improved jumping spider optimization algorithm. Finally, the control system simulation is done and compared with the jumping spider optimization algorithm and the particle swarm optimization algorithm. The simulation results show that the improved jumping spider optimization algorithm is more suitable for the multivariable strong coupling nonlinear engine cooling system. For the flow and pressure control, the transient time and overshoot are reduced, and the steady-state error is less than 1%. For the temperature control, the result of the improved jumping spider optimization algorithm is more smooth, without overshoot, and almost does not exceed the set inlet water temperature. The overshoot, steady-state errors and transient time of the system have been improved, which proves the feasibility and significance of the improved jumping spider optimization algorithm by comparing the control performance and optimization time.

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A Bio-Inspired Method for Mathematical Optimization Inspired by Arachnida Salticidade

Cited by 40 publications

References 50 publications

A novel global FPPT strategy for PV system under partial shading condition

A novel global FPPT strategy for PV system under partial shading condition

Optimum design of slot and magnet structure of permanent magnet synchronous generator with teaching‐learning‐studying‐based optimization

Design of Gas Turbine Cooling System Based on Improved Jumping Spider Optimization Algorithm

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