Red deer algorithm (RDA): a new nature-inspired meta-heuristic
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METHODOLOGIES AND APPLICATION
Red deer algorithm (RDA): a new nature-inspired meta-heuristic Amir Mohammad Fathollahi-Fard1 • Mostafa Hajiaghaei-Keshteli1 • Reza Tavakkoli-Moghaddam2,3
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Nature has been considered as an inspiration of several recent meta-heuristic algorithms. This paper firstly studies and mimics the behavior of Scottish red deer in order to develop a new nature-inspired algorithm. The main inspiration of this meta-heuristic algorithm is to originate from an unusual mating behavior of Scottish red deer in a breading season. Similar to other population-based meta-heuristics, the red deer algorithm (RDA) starts with an initial population called red deers (RDs). They are divided into two types: hinds and male RDs. Besides, a harem is a group of female RDs. The general steps of this evolutionary algorithm are considered by the competition of male RDs to get the harem with more hinds via roaring and fighting behaviors. By solving 12 benchmark functions and important engineering as well as multi-objective optimization problems, the superiority of the proposed RDA shows in comparison with other well-known and recent metaheuristics.
Communicated by V. Loia. & Mostafa Hajiaghaei-Keshteli [email protected] 1
Department of Industrial Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran
2
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
3
Arts et Me´tiers Paris Tech, LCFC, Metz, France
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A. M. Fathollahi-Fard et al.
Graphic abstract
Start
Initialize Red Deers Roar male RDs
Select γ percent of best male RDs as male commanders
Fight between male commanders and stags Form rm harems harems Mate commander with α percent of hinds in his harem Mate commander with β percent of hinds in another harem Mate stag with the nearest hind Select the next generation No
Stop condition satisfied? Yes
The end
Keywords Red deer algorithm Meta-heuristics Real-world applications Multi-objective optimization
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Red deer algorithm (RDA): a new nature-inspired meta-heuristic
1 Introduction Over the last three decades, an enormous growth in the size and complexity of industrial organization occurred and consequently, the size and importance of the optimization problems increased (Cheraghalipour et al. 2018). Accordingly, managers want to find better ways or solutions, which help them to handle the whole organization. Metaheuristic algorithms gave easy, cost-efficient and important tools to both researchers and managers to solve this dilemma (Mirjalili et al. 2014). In 1975, John Holland has developed the genetic algorithm (GA) to solve huge and complex problems, for the first time (Holland 1975). Consequently, a numerous meta-heuristics were developed which mostly have been inspired by nature or artificial processes, such as simulated annealing (SA) based on annealing process of metals (Kirkpatrick et al. 1983), ant c
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