Hybrid metaheuristic algorithm using butterfly and flower pollination base on mutualism mechanism for global optimization problems

Wang, Zhongmin; Luo, Qifang; Zhou, Yongquan

doi:10.1007/s00366-020-01025-8

Cited by 64 publications

(12 citation statements)

References 110 publications

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“…(34) V = −2cz ̇z Figures 11,12 and 13 depict the response curves (power, voltage, current and duty cycle), resulting from the simulated standalone PV system shown in Fig. 8, using CSA, PSO and P&O methods under three various shading cases (zero, weak and severe shading), detailed in Table 2 of Sect.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Recent researches proposed several metaheuristic optimization methods that are widely used to solve difficult optimization problems in engineering and many other important areas. These recent metaheuristic methods guarantee high efficiency and reasonable computational cost in the resolution process [12]. Some of the most used metaheuristic optimization methods in literature are Particle Swarm Optimization (PSO), Cuckoo Search (CS), Bat Algorithm (BA), Artificial Bee Colony (ABC), Grey Wolf Optimization (GWO), Firefly Algorithm (FA), Flower Pollination Algorithm (FPA), Mine Blast Optimization (MBO), Salp Swarm Algorithm (SSA) and Dragonfly Optimization Algorithm (DFO).…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Metaheuristic Technique to Control the Maximum Power Point of a Partially Shaded Photovoltaic System Using Crow Search Algorithm (CSA)

Houam

Terki

Bouarroudj

2020

J. Electr. Eng. Technol.

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The field of research in maximum power point tracking (MPPT) methods is experiencing great progress with a wide range of techniques being suggested, ranging from simple but ineffective methods to more effective but complex ones. Therefore, it is very important to propose a strategy that is both simple and effective in controlling the global maximum power point (GMPP) for a photovoltaic (PV) system under changing weather conditions, especially in partial shading cases (PSCs). This paper proposes a new design of an MPPT controller based on a metaheuristic optimization technique called Crow Search Algorithm (CSA) to attenuate the undesirable effects of partial shading on the tracking performances of standalone PV systems. CSA is a nature-inspired method based on the intelligent skills of the crow in the search process of hidden food places. CSA technique combines efficiency and simplicity using only two tuning parameters. The stability analysis of the proposed system is performed using a Lyapunov function. The simulation results for three different partial shading cases that are zero, weak and severe shading confirm the superior performance of CSA compared to PSO and P&O techniques in term of easy implementation, high efficiency and low power loss, decreasing considerably the convergence time by an average of 38.53%.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient Metaheuristic Technique to Control the Maximum Power Point of a Partially Shaded Photovoltaic System Using Crow Search Algorithm (CSA)

Houam

Terki

Bouarroudj

2020

J. Electr. Eng. Technol.

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“…Although some FPA algorithm research has achieved good results in most optimization problems [14][15][16], there are still problems such as easy to fall into local optimum [17][18], the low search precision, and insufficient development ability [19]. To solve more optimization problems, many scholars improve the performance of FPA mainly from 5 aspects, the improvement of the initial population [20][21], the improvement in population diversity [22][23], the improvement in parameter settings [24][25], the improvement in search capabilities [26][27] and designing hybrid algorithms [28][29].…”

Section: Related Workmentioning

confidence: 99%

A hybrid optimized multi-population flower pollination algorithm for web service composition problem

Zhou²,

Luo³

2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

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With the rapid development of Service-Oriented Computing (SOC), Web services have become the preferred technology for realizing Service-oriented computing problems and other related goals. How to find a cheap and high-quality Web service composition from a large number of Web services that provide the same function, that is, the research based on Quality of Service (QoS) is the most important problem in the Web service composition optimization model, which is also very important to improve the efficiency of the service composition. In this work, we use the intelligence optimization algorithms to search for the best combination of web services to achieve the functionality of the workflow’s tasks. And we propose a novel approach, called A Hybrid Optimized Multi-Population Flower Pollination Algorithm (AHOMFPA) to solve this problem. Empirical comparisons demonstrate AHOMFPA has advantages over other existing algorithms in efficiency and feasibility.

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“…Because the metaheuristic algorithm's characterized like flexible structure, simple working because of non-derivative process, and don't suffer early converge which is ensure better optimizer than deterministic algorithm [3]. In the previous works have been studied on three bar truss design optimization problem optimized by metaheuristic algorithms [4][5][6]. While it has been demonstrated in this study that the artificial gorilla troops algorithm (GTO) is a competitive algorithm by testing it through the CEC2019 benchmark functions, the same results have been achieved with different functions in previous studies [7].…”

Section: Introductionmentioning

confidence: 99%

Assessment of GTO: Performance evaluation via constrained benchmark function, and Optimized of Three Bar Truss Design Problem

Eker

2023

DÜMF MD

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The aim of this paper is to show that the artificial gorilla troops optimization (GTO) algorithm, as an optimizer, can cope with test functions such as CEC2019, and also to best optimize the three bar truss design problem as a constrained optimization problem. As a method, two statistical measures such as the best values provided by the algorithms and the standard deviation showing the distance between the values were studied. At the same time, the convergence rate of the algorithms compared by the convergence curves were examined. For this purpose, it has been competed against two other swarm-based algorithms, sine-cosine algorithm (SCA) and golden eagle optimization (GEO). The optimization of the three bar truss design problem, which is another side of the study, has been made. The GTO algorithm reached the best values in the optimization of the parameters of the problem. In addition to the convergence curve, statistical results have examined, and the advantages of GTO are revealed through box-plot figures that evaluate the relationship between median and quartiles and the distribution among all results.

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Hybrid metaheuristic algorithm using butterfly and flower pollination base on mutualism mechanism for global optimization problems

Cited by 64 publications

References 110 publications

An Efficient Metaheuristic Technique to Control the Maximum Power Point of a Partially Shaded Photovoltaic System Using Crow Search Algorithm (CSA)

An Efficient Metaheuristic Technique to Control the Maximum Power Point of a Partially Shaded Photovoltaic System Using Crow Search Algorithm (CSA)

A hybrid optimized multi-population flower pollination algorithm for web service composition problem

Assessment of GTO: Performance evaluation via constrained benchmark function, and Optimized of Three Bar Truss Design Problem

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