Abstract
A novel bio-inspired algorithm, namely, Dingo Optimization Algorithm (DOA), is proposed for solving optimization problems. The DOA mimics the social behavior of the Australian dingo dog. The algorithm is inspired by the hunting strategies of dingoes which are attacking by persecution, grouping tactics, and scavenging behavior. In order to increment the overall efficiency and performance of this method, three search strategies associated with four rules were formulated in the DOA. These strategies and rules provide a fine balance between intensification (exploitation) and diversification (exploration) over the search space. The proposed method is verified using several benchmark problems commonly used in the optimization field, classical design engineering problems, and optimal tuning of a Proportional-Integral-Derivative (PID) controller are also presented. Furthermore, the DOA’s performance is tested against five popular evolutionary algorithms. The results have shown that the DOA is highly competitive with other metaheuristics, beating them at the majority of the test functions.
Highlights
Metaheuristics, which are emerging as effective alternatives for solving nondeterministic Polynomial Time hard (NP-hard) optimization problems, are strategies for designing or improving very general heuristics procedures with high performance in order to find optimal solutions. e goal of the metaheuristics is efficient exploration and exploitation of the search space, where an effective algorithm sets a good ratio between this two parameters
Iteration, and β1 is a random number uniformly generated in the interval of [− 2, 2]; it is a scale factor that changes the magnitude and sense of the dingoes’ trajectories. e Group attack pseudocode is shown in Algorithm 1
Unimodal functions, the Dingo Optimization Algorithm (DOA) outperforms all other algorithms, as Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), Gravity Search Algorithm (GSA), and Fast Evolutionary Programming (FEP), in F1, F2, F3, and F7 functions and similar to Differential Evolution (DE), it found the optimal result in F4
Summary
A constrained optimization problem can be described as a nonlinear programming problem (NLP) [1], as shown below. The search process starts with one candidate solution and it is improved in each iteration over the runtime Algorithms such as variable neighborhood search (VNS) [4], Tabu search (TS) [5], Simulated annealing (SA) [6], and Iterated local search [7] are considered as part of the local search metaheuristics group. A novel bio-inspired algorithm, namely Dingo Optimization Algorithm (DOA), is proposed for solving optimization tasks It is based on the simulation of the hunting strategies of Dingoes, which are attacking by chasing, grouping tactics, and scavenging behavior. The mathematical model of Dingoes different hunting strategies is first provided. E hunting strategies considered are attacking by persecution, grouping tactics, and scavenging behavior.
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