Improved Fitness-Dependent Optimizer Algorithm

Muhammed, Danial A.; Saeed, Soran; Rashid, Tarik A.

doi:10.1109/access.2020.2968064

Cited by 29 publications

(17 citation statements)

References 60 publications

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“…This problem derived from using fixed value for wf and achieving low results by using pace Equations [30]. Results conducted from literature shows that FDO has poor performance against different algorithms such as PSO, GA, SHADE, CJADE, IPOP-CMA-ES [38]. Table (1) proves that FDO has low exploitation capability in unimodal test functions.…”

Section: Limitations Of Fdomentioning

confidence: 99%

“…Despite of ignoring the low solution, scout bees change their position based on the previous best position if the solution does not improve. Scout bee population is represented as Xi (i=1, 2, ….., n) [31], [38]. Two methods are used to search inside the search space by scout bees: fw and scout bee movement process.…”

Section: Fdo Algorithmmentioning

confidence: 99%

“…Therefore, FDO has limitations because of presence multiple randomized parameter such as fw, wf, pace updating Equation, best updating solution, and Levy flight. Consequently, according to paper [38] FDO has issue in terms of convergence so it has low convergence because of setting wf to zero. Having improper balance between exploration and exploitation is another problem related to FDO since FDO uses fitness of best agent, current fitness, and wf.…”

Section: Limitations Of Fdomentioning

confidence: 99%

“…FDO original use 0 for wf. However, paper [38] used a range value for wf which is [0, 1] . Also, the same researchers used alignment and cohesion method in order to find new position.…”

Section: Ifdomentioning

confidence: 99%

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Modified Fitness Dependent Optimizer for Solving Numerical Optimization Functions

Salih¹,

Mohammed²,

Abdul³

2022

IEEE Access

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The Fitness Dependent Optimizer (FDO) is a recent metaheuristic algorithm that was developed in 2019. It is one of the metaheuristic algorithms that has been used by researchers to solve various applications especially for engineering design problem. In this paper, a comprehensive survey conducted about FDO and its applications. Consequently, despite of having competitive performance of FDO, it has two major problems including low exploitation and slow convergence. Therefore, a modification of FDO (MFDO) is proposed for solving FDO issues. MFDO used two methods to enhance the performance of FDO: firstly, optimizing the range of weight factor between 0 and 0.2 which is used for finding fitness weight. Secondly, using sine cardinal mathematical function to update fitness weight and pace which is referred to the speed of the bees. To evaluate the performance of MFDO, 19 classical benchmark functions and CEC2019 benchmark functions are used. MFDO compared against all the enhancement of FDO and also it is compared with Grey Wolf Optimization (GWO), Chimp Optimization Algorithm (ChOA), Genetic Algorithm (GA), and Butterfly Optimization Algorithm (BOA). Statistical results proved that MFDO achieved significant performance compared to other algorithms. Finally, MFDO is used to solve three applications: Welded Beam Design (WDB), Pressure Vessel Design (PVD), and Spring Design Problem. Results proved that MFDO outperformed well in solving these applications against FDO, Gravitational Search Algorithm (GSA), GA, and Grasshopper Optimization Algorithm (GOA). .

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Section: Limitations Of Fdomentioning

confidence: 99%

Section: Fdo Algorithmmentioning

confidence: 99%

Section: Limitations Of Fdomentioning

confidence: 99%

“…FDO original use 0 for wf. However, paper [38] used a range value for wf which is [0, 1] . Also, the same researchers used alignment and cohesion method in order to find new position.…”

Section: Ifdomentioning

confidence: 99%

See 2 more Smart Citations

Modified Fitness Dependent Optimizer for Solving Numerical Optimization Functions

Salih¹,

Mohammed²,

Abdul³

2022

IEEE Access

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“…It is a PSO-based algorithm and is inspired by the foraging behavior of honeybees whilst selecting a hive. It has been improved and applied by Muhammed et al in 2020 to develop a model for pedestrian evacuation [50].…”

Section: Nature-inspired Metaheuristic Algorithms In Literaturementioning

confidence: 99%

ANA: Ant Nesting Algorithm for Optimizing Real-World Problems

Rashid

Mirjalili

2021

Mathematics

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In this paper, a novel swarm intelligent algorithm is proposed called ant nesting algorithm (ANA). The algorithm is inspired by Leptothorax ants and mimics the behavior of ants searching for positions to deposit grains while building a new nest. Although the algorithm is inspired by the swarming behavior of ants, it does not have any algorithmic similarity with the ant colony optimization (ACO) algorithm. It is worth mentioning that ANA is considered a continuous algorithm that updates the search agent position by adding the rate of change (e.g., step or velocity). ANA computes the rate of change differently as it uses previous, current solutions, fitness values during the optimization process to generate weights by utilizing the Pythagorean theorem. These weights drive the search agents during the exploration and exploitation phases. The ANA algorithm is benchmarked on 26 well-known test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), five modified versions of PSO, whale optimization algorithm (WOA), salp swarm algorithm (SSA), and fitness dependent optimizer (FDO). ANA outperformances these prominent metaheuristic algorithms on several test cases and provides quite competitive results. Finally, the algorithm is employed for optimizing two well-known real-world engineering problems: antenna array design and frequency-modulated synthesis. The results on the engineering case studies demonstrate the proposed algorithm’s capability in optimizing real-world problems.

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A Canis lupus inspired upgraded Harris hawks optimizer for nonlinear, constrained, continuous, and discrete engineering design problem

Nandi

Kamboj

2020

Numerical Meth Engineering

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Recently established Harris hawks optimization (HHO) has natural behavior for finding an optimum solution in global search space without getting trapped in previous convergence. However, the exploitation phase of the current Harris hawks optimizer algorithm is poor. In the present research, an improved version of the HHO algorithm, which combines Harris hawks optimizer with Canis lupus inspire grey wolf optimizer (GWO) and named as hHHO-GWO algorithm, has been proposed to find the solution of various optimization problems such as nonlinear, nonconvex, and highly constrained engineering design problem. In the proposed research, the phase of exploration and exploitation of the existing HHO algorithm has been further improved using GWO algorithm and its performance has been tested for various benchmarks problems including CEC2005 (unimodal, multimodal, and fixed dimensions functions), multimodal functions with variable dimensions, and CEC-BC-2017 test functions. Further, the developed hybrid optimizer has been tested for 11 different engineering design and optimization problems and experimental results of hHHO-GWO have been compared with other optimizer.

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Improved Fitness-Dependent Optimizer Algorithm

Cited by 29 publications

References 60 publications

Modified Fitness Dependent Optimizer for Solving Numerical Optimization Functions

Modified Fitness Dependent Optimizer for Solving Numerical Optimization Functions

ANA: Ant Nesting Algorithm for Optimizing Real-World Problems

A Canis lupus inspired upgraded Harris hawks optimizer for nonlinear, constrained, continuous, and discrete engineering design problem

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