Hybridization of Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Nonlinear Programming Problems

Allah, Rizk M Rizk

doi:10.4172/2090-4908.1000134

Cited by 20 publications

(9 citation statements)

References 15 publications

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“…The performance of the proposed MRFO-PSO is compared with some well-known algorithms include the FFA [ 44 ], WOA [ 65 ], DA [ 66 ], GWO [ 21 ], ALO [ 20 ], original MRFO, and other state-of-art methods. The obtained outcomes regarding the studied benchmark problems are tabulated in Table 4 using some central tendency statistical metrics which are the average value of the fitness (mean), best value of the fitness (Min), median value (Median), worst value of the fitness (Max), and the standard deviation (STD) to confirm that the archived results are not happen by chance.…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, researchers chose another pursuit through combining some properties of two or more techniques to improve efficiency and shorten the computational time. In this regard, some several attentions have been developed such as PSO-GA hybrid with Adam optimization [ 40 ], a synergy of the sine–cosine algorithm and particle swarm optimizer (SCA-PSO) [ 41 ], hybrid sine–cosine algorithm with differential evolution (SCA-DE) [ 42 ], hybrid DE and extremal optimization (DE-EO) [ 43 ], hybrid fruit fly optimization algorithm and firefly algorithm (FOA-FA) [ 44 ], hybrid Grey wolf optimization with particle swarm optimization (GWO-PSO) [ 45 ], enhanced tunicate swarm algorithm (ETSA) [ 46 ], hybrid ABC, and PSO [ 47 ]. The traditional methods with their two forms, direct and gradient-based methods, face some serious disadvantages for example, the delay in direct search methods or non-differentiability and discontinuity in gradient-based methods.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, they can avoid being trapped in local optima in sharp and multiple peak problems. Moreover, they avoid computation of the gradients of the objective function and the constraints as well [ 44 ]. Lately, MRFO has gained popularity, since it is deployed in many engineering and other fields for example, Alturki et al presented an MRFO-based optimal control strategy to enhance the proportional-integral (PI) controllers of DC/DC and DC/AC converters for PV grid-connected system [ 48 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On a Novel Hybrid Manta Ray Foraging Optimizer and Its Application on Parameters Estimation of Lithium-Ion Battery

Rizk‐Allah

Zineldin

Mousa

et al. 2022

Int J Comput Intell Syst

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In this paper, we propose a hybrid meta-heuristic algorithm called MRFO-PSO that hybridizes the Manta ray foraging optimization (MRFO) and particle swarm optimization (PSO) with the aim to balance the exploration and exploitation abilities. In the MRFO-PSO, the concept of velocity of the PSO is incorporated to guide the searching process of the MRFO, where the velocity is updated by the first best and the second-best solutions. By this integration, the balancing issue between the exploration phase and exploitation ability has been further improved. To illustrate the robustness and effectiveness of the MRFO-PSO, it is tested on 23 benchmark equations and it is applied to estimate the parameters of Tremblay's model with three different commercial lithium-ion batteries including the Samsung Cylindrical ICR18650-22 lithium-ion rechargeable battery, Tenergy 30209 prismatic cell, Ultralife UBBL03 (type LI-7) rechargeable battery. The study contribution exclusively utilizes hybrid machine learning-based tuning for Tremblay's model parameters to overcome the disadvantages of human-based tuning. In addition, the comparisons of the MRFO-PSO with six recent meta-heuristic methods are performed in terms of some statistical metrics and Wilcoxon’s test-based non-parametric test. As a result, the conducted performance measures have confirmed the competitive results as well as the superiority of the proposed MRFO-PSO.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On a Novel Hybrid Manta Ray Foraging Optimizer and Its Application on Parameters Estimation of Lithium-Ion Battery

Rizk‐Allah

Zineldin

Mousa

et al. 2022

Int J Comput Intell Syst

View full text Add to dashboard Cite

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“…Fruit fly algorithm is one of the newest meta-heuristics algorithm in the class of swarm intelligence algorithms. It was proposed by (Wen-Tsao, 2014), (Rizk, 2016). The inspiration came from the foraging behaviors of the fruit flies in their search for food using their sense of vision and smell (Hazim et al, 2014).…”

Section: Fruit Fly Optimization Algorithm (Foa)mentioning

confidence: 99%

Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization Algorithms

Yinka-Banjo

Agwogie²

2020

Nig. J. Technol. Dev.

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This article presents the implementation and comparison of fruit fly optimization (FOA), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms in solving the mobile robot path planning problem. FOA is one of the newest nature-inspired algorithms while PSO and ACO has been in existence for a long time. PSO has been shown by other studies to have long search time while ACO have fast convergence speed. Therefore there is need to benchmark FOA performance with these older nature-inspired algorithms. The objective is to find an optimal path in an obstacle free static environment from a start point to the goal point using the aforementioned techniques. The performance of these algorithms was measured using three criteria: average path length, average computational time and average convergence speed. The results show that the fruit fly algorithm produced shorter path length (19.5128 m) with faster convergence speed (3149.217 m/secs) than the older swarm intelligence algorithms. The computational time of the algorithms were in close range, with ant colony optimization having the minimum (0.000576 secs). Keywords: Swarm intelligence, Fruit Fly algorithm, Ant Colony Optimization, Particle Swarm Optimization, optimal path, mobile robot.

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“…In this context, researchers have proposed a sequence of intelligent methods inspired by certain rules. Particle swarm optimization (PSO) [16], sine cosine algorithm (SCA) [17], [18], moth-flame optimization algorithm (MFO) [19], ant colony system (ACS) [20], artificial bee colony (ABC) [21], firefly algorithm [22], [23], and gravitational search algorithm (GSA) [24]. These optimization algorithms have been investigated by several researchers to deal with optimization tasks at various fields such as design optimization [25], resource allocation [26], economic dispatch [27], and multi-objective optimization [28].…”

Section: Introductionmentioning

confidence: 99%

Hybridization of Grey Wolf Optimizer and Crow Search Algorithm Based on Dynamic Fuzzy Learning Strategy for Large-Scale Optimization

2020

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A novel optimization algorithm named hybrid grey wolf optimizer with crow search algorithm (GWO-CSA) is developed in this paper for handling large-scale numerical optimization problems. The proposed GWO-CSA algorithm combines the strong points of both grey wolf optimizer (GWO) and crow search algorithm (CSA) with the aim to escape from local optima with faster convergence than the standard GWO and CSA. In this algorithm, GWO operates in enhancing the exploration ability while CSA works as a local searching scheme to emphasize the exploitation capability to achieve global optimal solutions. In this sense, the movement direction and speed of leader grey wolf (alpha) is improved by incorporating the CSA phase. Also, a dynamic fuzzy learning strategy (DFLS) is introduced to enable the occurring of tiny changes in the neighborhood of the best solution to avoid the caught in the local optima and refine the quality of the obtained solution. The robustness and efficiency of the proposed GWO-CSA algorithm are investigated on fifteen CEC 2015 benchmark problems in addition to four large-scale problems and four real applications related to engineering design optimization taken from the literature. The comprehensive comparisons with other algorithms have demonstrated the effectiveness of GWO-CSA to address optimization tasks. INDEX TERMS grey wolf optimizer, crow search algorithm, numerical optimization, hybridization.

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Hybridization of Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Nonlinear Programming Problems

Cited by 20 publications

References 15 publications

On a Novel Hybrid Manta Ray Foraging Optimizer and Its Application on Parameters Estimation of Lithium-Ion Battery

On a Novel Hybrid Manta Ray Foraging Optimizer and Its Application on Parameters Estimation of Lithium-Ion Battery

Mobile Robot Path Planning in an Obstacle-free Static Environment using Multiple Optimization Algorithms

Hybridization of Grey Wolf Optimizer and Crow Search Algorithm Based on Dynamic Fuzzy Learning Strategy for Large-Scale Optimization

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