Modified-Improved Fitness Dependent Optimizer for Complex and Engineering Problems

TL;DR

This paper introduces M-IFDO, an enhanced version of the FDO algorithm, which improves optimization performance on complex problems by modifying scout bee movement and parameter settings, outperforming several state-of-the-art algorithms.

Contribution

The paper proposes M-IFDO, a modified FDO algorithm that overcomes limitations related to agent count and search efficiency by updating scout bee movement and replacing parameters.

Findings

M-IFDO outperforms competitors on benchmark functions.

M-IFDO achieves better results on real-world problems.

The algorithm demonstrates improved convergence and efficiency.

Abstract

Fitness dependent optimizer (FDO) is considered one of the novel swarm intelligent algorithms. Recently, FDO has been enhanced several times to improve its capability. One of the improvements is called improved FDO (IFDO). However, according to the research findings, the variants of FDO are constrained by two primary limitations that have been identified. Firstly, if the number of agents employed falls below five, it significantly diminishes the algorithm's precision. Secondly, the efficacy of FDO is intricately tied to the quantity of search agents utilized. To overcome these limitations, this study proposes a modified version of IFDO, called M-IFDO. The enhancement is conducted by updating the location of the scout bee to the IFDO to move the scout bees to achieve better performance and optimal solutions. More specifically, two parameters in IFDO, which are alignment and cohesion, are removed. Instead, the Lambda parameter is replaced in the place of alignment and cohesion. To verify the performance of the newly introduced algorithm, M-IFDO is tested on 19 basic benchmark functions, 10 IEEE Congress of Evolutionary Computation (CEC-C06 2019), and five real-world problems. M-IFDO is compared against five state-of-the-art algorithms: Improved Fitness Dependent Optimizer (IFDO), Improving Multi-Objective Differential Evolution algorithm (IMODE), Hybrid Sampling Evolution Strategy (HSES), Linear Success-History based Parameter Adaptation for Differential Evolution (LSHADE) and CMA-ES Integrated with an Occasional Restart Strategy and Increasing Population Size and An Iterative Local Search (NBIPOP-aCMAES). The verification criteria are based on how well the algorithm reaches convergence, memory usage, and statistical results. The results show that M-IFDO surpasses its competitors in several cases on the benchmark functions and five real-world problems.