Tasmanian Devil Optimization: A New Bio-Inspired Optimization Algorithm for Solving Optimization Algorithm

Abstract

In this paper, a new bio-inspired metaheuristic algorithm called Tasmanian Devil Optimization (TDO) is designed that mimics Tasmanian devil behavior in nature. The fundamental inspiration used in TDO is simulation of the feeding behavior of the Tasmanian devil, who has two strategies: attacking live prey or feeding on carrions of dead animals. The proposed TDO is described, then its mathematical modeling is presented. TDO performance in optimization is tested on a set of twenty-three standard objective functions. Unimodal benchmark functions have analyzed the TDO exploitation capability, while high-dimensional multimodal and fixed-exploitation multimodal benchmark functions have challenged the TDO exploration capability. The optimization results indicate the high ability of the proposed TDO in exploration and exploitation and create a proper balance between these two indicators to effectively solve optimization problems. Eight well-known metaheuristic algorithms are employed to analyze the quality of the obtained results from TDO. The simulation results show that the proposed TDO, with its strong performance, has a higher capability than the eight competitor algorithms and is much more competitive. For further analysis, TDO is tested in optimizing four engineering design problems. Implementation results show that TDO has an effective performance in solving real-world applications.

A Modified Reptile Search Algorithm for Numerical Optimization Problems

“…In this section, six recently proposed algorithms were evaluated for comparison with MRSA. These state-of-the-art algorithms are BOA [ 38 ], HHO [ 42 ], AOA [ 47 ], SSA [ 45 ], PFA [ 48 ], and TDO [ 46 ]. For a fair comparison, all the algorithm parameters are set the same as those used by the authors of the original literature, as shown in Table 13 .…”

“…Parameters setting BOA [38] a � 0.1, c � 0.01, p � 0.6 HHO [42] β � 1.5, E 0 ∈ [− 1, 1] AOA [47] Mop max � 1, Mop min � 0.2, C � 1, a � 5, Mu � 0.499 SSA [45] c 1 � rand, c 2 � rand PFA [48] u 1 � − 1 + 2rand, u 2 � − 1 + 2rand TDO [46] ∼ 14 Computational Intelligence and Neuroscience BOA [38] HHO [42] AOA [47] SSA [45] PFA [48] TDO [46] Computational Intelligence and Neuroscience…”

“…They provide solutions by sharing information about all individuals in the optimization process. Others include the grey wolf optimizer (GWO) [ 36 ], the whale optimization algorithm (WOA) [ 37 ], the butterfly optimization algorithm (BOA) [ 38 ], the firefly algorithm (FA) [ 39 ], the artificial bee colony (ABC) algorithm [ 40 ], the reptile search algorithm (RSA) [ 41 ], the Harris hawks optimizer (HHO) [ 42 ], the equilibrium optimizer (EO) [ 43 ], the tunicate swarm algorithm (TSA) [ 44 ], the salp swarm algorithm (SSA) [ 45 ], the Tasmanian devil optimization (TDO) [ 46 ], the arithmetic optimization algorithm (AOA) [ 47 ], and the pathfinder algorithm (PFA) [ 48 ].…”

Skill Optimization Algorithm: A New Human-Based Metaheuristic Technique

“…The strategy of pelicans in hunting and trapping prey was the main inspiration of Pelican Optimization Algorithm (POA) [36]. Some other swarm-based algorithms are: Cat and Mouse Based Optimizer (CMBO) [37], Good Bad Ugly Optimizer (GBUO) [38], Marine Predator Algorithm (MPA) [39], Tasmanian Devil Optimization (TDO) [40], Mutated Leader Algorithm (MLA) [41], Tunicate Search Algorithm (TSA) [42], Northern Goshawk Optimization (NGO) [43], Donkey Theorem Optimizer (DTO) [44], Rat Swarm Optimization (RSO) [45], All Members Based Optimizer (AMBO) [46], Red Fox Optimization (RFO) [47], Best Members Based Optimizer (MBMBO) [48], and Mixed Leader Based Optimizer (MLBO) [49].…”

Optimal design of elastic and elastoplastic tuned mass dampers using the Mouth Brooding Fish algorithm for linear and nonlinear structures

“…Consequently, numerous optimization algorithms have been proposed to solve problems. Most of these algorithms are nature-inspired and widely used in engineering applications, thanks to their ability to obtain global optima and fast convergence [38][39][40][41][42]. Inspired by the lifecycle of the mouthbrooding fish, an algorithm called the Mouth Brooding Fish was developed by Jahani and Chizari in 2018 [43].…”