Adaptive modified artificial bee colony algorithms (AMABC) for optimization of complex systems

Abstract

Complex systems are large scale and involve numerous uncertainties, which means that such systems tend to be expensive to operate. Further, it is difficult to analyze systems of this kind in a real environment, and for this reason agent-based modeling and simulation techniques are used instead. Based on estimation methods, modeling and simulation techniques establish an output set against the existing input set. However, as the data set in a given complex systems becomes very large, it becomes impossible to use estimation methods to create the output set desired. Therefore, a new mechanism is needed to optimize data sets in this context. In this paper, the adaptive modified artificial bee colony algorithm is shown to be successful in optimizing the numerical test function and complex system parameter data sets. Moreover, the results show that this algorithm can be successfully adapted to a given problem. Specifically, this algorithm can be more successful in optimizing problem solving than either the artificial bee colony algorithm or the modified artificial bee colony algorithm. The adaptive modified artificial bee colony algorithm performs a search in response to feedback received from the simulation in run-time. Because of its adaptability, the adaptive modified artificial bee colony algorithm is of great importance for its ability to find solutions to multiple kinds of problems across numerous fields.

Artificial Honeybee Colony Algorithm: An Adaptive Optimization Method for Generating Migrating Search SpacesOptimization Method for Generating Migrating Search Spaces

“…Here, the food source has some value of profitability assigned as the bees communicate navigational information. This model robustly locates the optimum of various problems, thus proving the usefulness of such a model for diverse applications (Karaboga 2005;Korkmaz Tan and Bora 2020). Moreover, a Modified Artificial Bee Colony Model (MABC) and Adapted Modified Bee Colony Algorithm (AMABC) build upon the ABC.…”

“…Most artificial bee colony models focus on a single aspect of population dynamics related to age structures, food availability effects, mites/parasites, viruses, and pesticides (Khoury et al 2013;Chen et al 2020Chen et al , 2021Kumar and Mandal 2022;Terry 2015). Generally, food availability plays a central role in the bee population (Karaboga and Akay 2009;Karaboga 2005;Habbi et al 2014;Foss 2017;Becher et al 2014;Korkmaz Tan and Bora 2020;Dennis and Kemp 2016;Lemmens et al 2008;Pham et al 2006;Khoury et al 2013). In reality, though, population dynamics are far more complex and rely on more than food supply alone; real species interactions tend to follow the Allee effect.…”

“…Honeybee colonies are natural examples of complex systems that can be modeled using Agent-based Modeling (ABM) approaches (Karaboga and Akay 2009;Becher et al 2014;Korkmaz Tan and Bora 2020). For such a model, agent classes fall into the following categories: queen, drone (male), worker (female), and environment.…”