Abstract
In this paper, a novel swarm intelligent algorithm is proposed called ant nesting algorithm (ANA). The algorithm is inspired by Leptothorax ants and mimics the behavior of ants searching for positions to deposit grains while building a new nest. Although the algorithm is inspired by the swarming behavior of ants, it does not have any algorithmic similarity with the ant colony optimization (ACO) algorithm. It is worth mentioning that ANA is considered a continuous algorithm that updates the search agent position by adding the rate of change (e.g., step or velocity). ANA computes the rate of change differently as it uses previous, current solutions, fitness values during the optimization process to generate weights by utilizing the Pythagorean theorem. These weights drive the search agents during the exploration and exploitation phases. The ANA algorithm is benchmarked on 26 well-known test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), five modified versions of PSO, whale optimization algorithm (WOA), salp swarm algorithm (SSA), and fitness dependent optimizer (FDO). ANA outperformances these prominent metaheuristic algorithms on several test cases and provides quite competitive results. Finally, the algorithm is employed for optimizing two well-known real-world engineering problems: antenna array design and frequency-modulated synthesis. The results on the engineering case studies demonstrate the proposed algorithm’s capability in optimizing real-world problems.
Highlights
Both our professional and private life is a sequence of decisions and each decision involves selecting between at least two options, and the fact is we are always in search of finding the best option
Modelling the entities of the Leptothorax ant behavior while building a new nest, the worker ants represent artificial search agents; each position around the queen ant that a worker ant exploits to drop grain, represents a potential solution exploited by an artificial search agent, and the best position to deposit among all the possible positions exploited by all the worker ants represents the global optimum solution
In addition to the standard benchmark functions, a set of 10 modern CEC benchmark functions are used as an extra evaluation of the ant nesting algorithm (ANA) algorithm, and the results are compared to three other remarkable metaheuristic algorithms that are dragonfly algorithm (DA), whale optimization algorithm (WOA), and salp swarm algorithm (SSA)
Summary
Both our professional and private life is a sequence of decisions and each decision involves selecting between at least two options (it will not be considered a decision otherwise), and the fact is we are always in search of finding the best option. Deterministic algorithms [3,4] such as linear programming, nonlinear programming, and mixed-integer nonlinear programming guarantee optimal or near-optimal solutions by adopting repeated design variables and functions firmly They have a fast convergence rate and are simple and easy to implement and understand. Stochastic algorithms are more flexible and efficient than deterministic algorithms [3,6,7] as they are stochastic, i.e., they all have some level of randomness; for the same set of inputs, the same output is not always obtained They are considered to be quite efficient in obtaining near-optimal solutions to all types of problems because they do not assume the underlying fitness landscape. They have developed algorithms based on swarm intelligence, biological systems, physical and chemical systems These types of algorithms are called nature-inspired, and they contain a big set of novel problem-solving methodologies and approaches. The rest of the paper contains a brief history of the most prominent swarm algorithms in the literature, the inspiration of the algorithm with its modelling, testing and evaluation of the algorithm, and the conclusion and recommendation of a few future works
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
