Improved Fitness-Dependent Optimizer Algorithm

Danial Abdulkareem Muhammed,Soran A M Saeed,Tarik Ahmed Rashid

doi:10.1109/access.2020.2968064

Abstract

The fitness-dependent optimizer (FDO) algorithm was recently introduced in 2019. An improved FDO (IFDO) algorithm is presented in this work, and this algorithm contributes considerably to refining the ability of the original FDO to address complicated optimization problems. To improve the FDO, the IFDO calculates the alignment and cohesion and then uses these behaviors with the pace at which the FDO updates its position. Moreover, in determining the weights, the FDO uses the weight factor (wf), which is zero in most cases and one in only a few cases. Conversely, the IFDO performs wf randomization in the [0-1] range and then minimizes the range when a better fitness weight value is achieved. In this work, the IFDO algorithm and its method of converging on the optimal solution are demonstrated. Additionally, 19 classical standard test function groups are utilized to test the IFDO, and then the FDO and three other well-known algorithms, namely, the particle swarm algorithm (PSO), dragonfly algorithm (DA), and genetic algorithm (GA), are selected to evaluate the IFDO results. Furthermore, the CECC06 2019 Competition, which is the set of IEEE Congress of Evolutionary Computation benchmark test functions, is utilized to test the IFDO, and then, the FDO and three recent algorithms, namely, the salp swarm algorithm (SSA), DA and whale optimization algorithm (WOA), are chosen to gauge the IFDO results. The results show that IFDO is practical in some cases, and its results are improved in most cases. Finally, to prove the practicability of the IFDO, it is used in real-world applications.

Highlights

Since computers were developed, the focus has been on the aspects of probing unidentified solutions and searching for the best solution
This improved fitness-dependent optimizer’s performance is verified using various standard test functions that exist in the literature; readers who are interested in knowing more about the methods of comparison can see references [21]–[23], [26], [54]
The fitness-dependent optimizer (FDO) implementation that can be found through the link https://github.com/ Jaza-Abdullah/FDO-Java was downloaded; it was coded via the Java language

Summary

INTRODUCTION

The focus has been on the aspects of probing unidentified solutions and searching for the best solution. The papers’ main contributions are briefly presented: 1) The IFDO algorithm is constructed by adding the behaviors of alignment and cohesion in updating the scout location and enhances the FDO algorithm in both the exploration and exploitation phases by considering reasonable covering of the search space to produce earlier convergence in the direction of global optimality. THE IMPROVED FITNESS-DEPENDENT OPTIMIZER The IFDO is developed from the original FDO, which is an evolutionary optimization algorithm that was proposed by Jaza and Tarik [26] The idea of this algorithm is essentially based on the generative process and collective decisionmaking used by bees. Based on the original FDO, our proposed improved fitness-dependent optimizer is introduced, and it includes two phases: the updating of the scout bee position, which is improved by the functionalization of certain parameters, and the randomization of the weight factor (wf ) in the [0, 1] range.

UPDATING THE SCOUT BEE POSITION

RESULTS AND DISCUSSION

ANALYSIS OF THE RESULTS

CONCLUSION