  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.
