Fitness Dependent Optimizer Algorithm Research Articles

An effective fitness dependent optimizer algorithm for edge server allocation in mobile computing

An effective fitness dependent optimizer algorithm for edge server allocation in mobile computing

Modified PID controller for automatic generation control of multi-source interconnected power system using fitness dependent optimizer algorithm.

In this paper, a modified form of the Proportional Integral Derivative (PID) controller known as the Integral- Proportional Derivative (I-PD) controller is developed for Automatic Generation Control (AGC) of the two-area multi-source Interconnected Power System (IPS). Fitness Dependent Optimizer (FDO) algorithm is employed for the optimization of proposed controller with various performance criteria including Integral of Absolute Error (IAE), Integral of Time multiplied Absolute Error (ITAE), Integral of Time multiplied Square Error (ITSE), and Integral Square Error (ISE). The effectiveness of the proposed approach has been assessed on a two-area network with individual source including gas, hydro and reheat thermal unit and then collectively with all three sources. Further, to validate the efficacy of the proposed FDO based PID and I-PD controllers, comprehensive comparative performance is carried and compared with other controllers including Differential Evolution based PID (DE-PID) controller and Teaching Learning Based Optimization (TLBO) hybridized with Local Unimodal Sampling (LUS-PID) controller. The comparison of outcomes reveal that the proposed FDO based I-PD (FDO-I-PD) controller provides a significant improvement in respect of Overshoot (Osh), Settling time (Ts), and Undershoot (Ush). The robustness of an I-PD controller is also verified by varying parameter of the system and load variation.

An Adaptive Fitness-Dependent Optimizer for the One-Dimensional Bin Packing Problem

In recent years, the one-dimensional bin packing problem (1D-BPP) has become one of the most famous combinatorial optimization problems. The 1D-BPP is a robust NP-hard problem that can be solved through optimization algorithms. This paper proposes an adaptive procedure using a recently optimized swarm algorithm and fitness-dependent optimizer (FDO), named the AFDO, to solve the BPP. The proposed algorithm is based on the generation of a feasible initial population through a modified well-known first fit (FF) heuristic approach. To obtain a final optimized solution, the most critical parameters of the algorithm are adapted for the problem. To the best of our knowledge, this is the first study to apply the FDO algorithm in a discrete optimization problem, especially for solving the BPP. The adaptive algorithm was tested on 30 instances obtained from benchmark datasets. The performance and evaluation results of this algorithm were compared with those of other popular algorithms, such as the particle swarm optimization (PSO) algorithm, crow search algorithm (CSA), and Jaya algorithm. The AFDO algorithm obtained the smallest fitness values and outperformed the PSO, CS, and Jaya algorithms by 16%, 17%, and 11%, respectively. Moreover, the AFDO shows superiority in terms of execution time with improvements over the execution times of the PSO, CS, and Jaya algorithms by up to 46%, 54%, and 43%, respectively. The experimental results illustrate the effectiveness of the proposed adaptive algorithm for solving the 1D-BPP.

Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive Process

In this paper, a novel swarm intelligent algorithm is proposed, known as the fitness dependent optimizer (FDO). The bee swarming reproductive process and their collective decision-making have inspired this algorithm; it has no algorithmic connection with the honey bee algorithm or the artificial bee colony algorithm. It is worth mentioning that FDO is considered a particle swarm optimization (PSO)-based algorithm that updates the search agent position by adding velocity (pace). However, FDO calculates velocity differently; it uses the problem fitness function value to produce weights, and these weights guide the search agents during both the exploration and exploitation phases. Throughout the paper, the FDO algorithm is presented, and the motivation behind the idea is explained. Moreover, FDO is tested on a group of 19 classical benchmark test functions, and the results are compared with three well-known algorithms: PSO, the genetic algorithm (GA), and the dragonfly algorithm (DA), additionally, FDO is tested on IEEE Congress of Evolutionary Computation Benchmark Test Functions (CEC-C06, 2019 Competition) [1]. The results are compared with three modern algorithms: (DA), the whale optimization algorithm (WOA), and the salp swarm algorithm (SSA). The FDO results show better performance in most cases and comparative results in other cases. Furthermore, the results are statistically tested with the Wilcoxon rank-sum test to show the significance of the results. Likewise, FDO stability in both the exploration and exploitation phases is verified and performance-proofed using different standard measurements. Finally, FDO is applied to real-world applications as evidence of its feasibility.