Abstract
Optimization is a buzzword, whenever researchers think of engineering problems. This paper presents a new metaheuristic named dingo optimizer (DOX) which is motivated by the behavior of dingo (Canis familiaris dingo). The overall concept is to develop this method involving the collaborative and social behavior of dingoes. The developed algorithm is based on the hunting behavior of dingoes that includes exploration, encircling, and exploitation. All the above prey hunting steps are modeled mathematically and are implemented in the simulator to test the performance of the proposed algorithm. Comparative analyses are drawn among the proposed approach and grey wolf optimizer (GWO) and particle swarm optimizer (PSO). Some of the well-known test functions are used for the comparative study of this work. The results reveal that the dingo optimizer performed significantly better than other nature-inspired algorithms.
Highlights
E optimization can, be done based on single or multiple objective functions [2,3,4]. Keeping this in the mind, there is a requirement of new metaheuristic-based solution to reduce the burden of any of the model designing. e objective of this paper is to develop a nature-based algorithm called dingo optimizer, which can be abbreviated as DOX
Experimental Setup. e overall simulation is done in MATLAB, taking into account the various parameters which will be explained in the setup of the simulation. e proposed DOX is implemented in Windows 10 with memory 8 GB RAM and processor Intel CPU 2.50 GHz
As per the comparison of DOX with other popular metaheuristic algorithms such as particle swarm optimizer (PSO) and DSO, DOX provides well competitive outcomes as presented in the results. e DOX is analyzed for the exploration and exploitation activity of agents using twenty-three test functions. e concise results, which are based on comparative analysis between the proposed DOX and other optimization algorithms, demonstrate that the approach suggested will cope with different kinds of constraints and provide stronger alternatives than any other optimizer. e suggested methodology is inspired by the real-life problems, which required less computational or mathematical efforts to find the best available optima
Summary
In the past few years, the problems related to real-life have increased, and it is motivating researchers to develop a better metaheuristic technique with the concept of randomization and local search. Hunting behavior and the social arrangements of dingo are modeled mathematically, to develop DOX to perform nature-inspired optimization. Designing the dingoes hunting plan mathematically, we assume that all the pack members including alpha, beta, and others have good knowledge about the potential location of prey. It can be understood that dingoes (alpha, beta, and others) update their positions randomly and calculate the position of the prey in the search space. E DOX assists its quest agents in changing their location based on the positioning of α, β, others, and the targeted prey. Even, with these operators, the DOX can inactivate local solutions.
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