Red deer algorithm (RDA): a new nature-inspired meta-heuristic

Fathollahi-Fard, Amir M.; Hajiaghaei–Keshteli, Mostafa; Tavakkoli–Moghaddam, Reza

doi:10.1007/s00500-020-04812-z

Cited by 338 publications

(144 citation statements)

References 77 publications

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“…These algorithms are chosen in such a manner that their distinct characteristics and different advantages help to find out the merits of the proposed BCO algorithm. To maintain an unbiased approach, all competitive algorithms need to be evaluated using either equal number of fitness evaluations or equal processing time [59]. We have adopted the first aproach to conduct the experiments for all participating algorithms.…”

Section: Resultsmentioning

confidence: 99%

Border Collie Optimization

Dutta

Bhattacharyya

Dey³

et al. 2020

IEEE Access

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In recent times, several metaheuristic algorithms have been proposed for solving real world optimization problems. In this paper, a new metaheuristic algorithm, called the Border Collie Optimization is introduced. The algorithm is developed by mimicking the sheep herding styles of Border Collie dogs. The Border Collie's unique herding style from the front as well as from the sides is adopted successfully in this paper. In this algorithm, the entire population is divided into two parts viz., dogs and sheep. This is done to equally focus on both exploration and exploitation of the search space. The Border Collie utilizes a predatory move called eyeing. This technique of the dogs is utilized to prevent the algorithm from getting stuck into local optima. A sensitivity analysis of the proposed algorithm has been carried out using the Sobol's sensitivity indices with the Sobol g-function for tuning of parameters. The proposed algorithm is applied on thirty-five benchmark functions. The proposed algorithm provides very competitive results, when compared with seven state-of-the-art algorithms like Ant Colony optimization, Differential algorithm, Genetic algorithm, Grey-wolf optimizer, Harris Hawk optimization, Particle Swarm optimization and Whale optimization algorithm. The performance of the proposed algorithm is analytically and visually tested by different methods to judge its supremacy. Finally, the statistical significance of the proposed algorithm is established by comparing it with other algorithms by employing Kruskal-Wallis test and Friedman test.

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Section: Resultsmentioning

confidence: 99%

Border Collie Optimization

Dutta

Bhattacharyya

Dey³

et al. 2020

IEEE Access

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“… Developing a new mathematical formulation for a multi-objective stochastic closedloop supply chain network design considering social impacts;  Proposing four different objectives including two conflicting social impacts and downside risk as well as the total cost;  Applying a number of recent and well-known multi-objective metaheuristic techniques: Non-dominated Sorting Genetic Algorithm (NSGA-II) [66], multiobjective of Simulated Annealing (SA) [67] and Red Deer Algorithm (RDA) [20] and also Keshtel Algorithm (KA) [21];  Designing three novel hybrid metaheuristics by using the benefits of applied individual ones;  Comparison of approaches by a number of performance evaluation metrics; The rest of the paper is organized as follows. Section 2 provides the literature review around the gaps mentioned above.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Thus, novel and strong approaches in metaheuristics are proposed to find Pareto-optimal solutions. The Red Deer Algorithm (RDA) [20] and Keshtel Algorithm (KA) [21] are utilized to solve the proposed closed-loop SCND problem as the recent developed optimizers. In addition, two state of art multi-objective optimizers called Non-dominated Sorting Genetic Algorithm (NSGA-II) [66] and multi-objective of Simulated Annealing (SA) [67] are employed, as well.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-objective stochastic closed-loop supply chain network design with social considerations

Fathollahi-Fard

Hajiaghaei–Keshteli

Mirjalili

2018

Applied Soft Computing

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137

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The graphical structure of a simple closed-loop supply chain network utilizing in this paper Suppliers (m) Plants (p) Distributers (n) Customers (i) Forward direction Reverse direction Recover centers (j) Remanufacturing centers (d) Disposal centers (s) Recycling centers (r) Highlights:  Introducing a new multi-objective stochastic closed-loop supply chain network design with social considerations;  Presenting a number of new hybrid metaheuristic algorithms to create a better interaction between the search phases;  Comparison of developed approaches by a set of efficient evaluation metrics of Pareto optimal sets;  Confirming the performance of proposed RDKAGA in practice;

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“…Furthermore, most deterministic or even heuristic optimization is not workable for problems with non-linearity and multi-modality. Therefore, the tremendous developments of Genetic Algorithm (GA) [21], Evolution Strategy (ES) [6], Genetic Programming (GP) [30], and Biogeography-Based Optimizer (BBO) [53] Swarm-based Particle swarm optimization (PSO) [28], Ant colony optimization (ACO) [10], Cuckoo search (CS) [62], Bat algorithm (BA) [64], Ant Lion Optimizer (ALO) [35], Butterfly optimization algorithm (BOA) [3], Dragonfly algorithm (DA) [37], fruit fly optimization algorithm (FOA) [44], Grey wolf optimizer (GWO) [42], Krill herd algorithm (KHA) [17], Red deer algorithm (RDA) [13], Bird mating optimizer (BMO) [4], Flower pollination algorithm (FPA) [61], Monarch butterfly optimization (MBO) [56], Mothflame optimization algorithm (MFO) [36], whale optimization algorithm (WOA) [40], Firefly algorithm (FA) [63], Artifical bee colony (ABC) [26], Salp Swarm Algorithm (SSA) [39], Harris hawks optimization (HHO) [24], and crow search algorithm (CSA) [5] Physical-based Simulated annealing (SA) [29], Multi-verse optimizer (MVO) [41], Sine cosine algorithm (SCA) [38], Water cycle algorithm (WCA) [12], Electromagnetism-like mechanism (EM) [7], Gravitational search algorithm (GSA) [48], Charged system search (CSS) [27], big bang-big crunch (BBBC) [11], and Henry gas solubility optimization (HGSO) [22] Human-Based Fireworks algorithm (FA) [54], Harmony Search Algorithm (HSA) …”

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confidence: 99%

Coronavirus herd immunity optimizer (CHIO)

Al-Betar

Alyasseri

Awadallah

et al. 2020

Preprint

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In this paper, a new nature-inspired human-based optimization algorithm is proposed which called Coronavirus Herd Immunity Optimizer (CHIO). The inspiration of CHIO is originated from the herd immunity concept as a way to tackle coronavirus pandemic (COVID-19). The speed of spreading coronavirus infection depends on how the infected individuals directly contact with other society members. In order to protect other members of society from the disease social distancing is suggested by health experts. Herd immunity is a state the population reach when most of the population is immune which results in the prevention of disease transmission. These concepts are modeled in terms of optimization concepts. CHIO mimics the herd immunity strategy as well as the social distancing concepts. Three types of individual cases are utilized for herd immunity: susceptible, infected, and immuned. This is to determine how the newly generated solution updates its genes with social distancing strategies. CHIO is evaluated using 23 well-known benchmark functions. Initially, the sensitivity of CHIO to its parameters is studied. Thereafter, the comparative evaluation against seven state-of-the-art methods is conducted. The comparative analysis verifies that CHIO is able to yield very competitive results compared to those obtained by other well-established methods. In conclusion, CHIO is a very powerful optimization algorithm that can be used to tackle many optimization problems across a wide variety of optimization domains.

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Red deer algorithm (RDA): a new nature-inspired meta-heuristic

Cited by 338 publications

References 77 publications

Border Collie Optimization

Border Collie Optimization

Multi-objective stochastic closed-loop supply chain network design with social considerations

Coronavirus herd immunity optimizer (CHIO)

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Red deer algorithm (RDA): a new nature-inspired meta-heuristic

Cited by 338 publications

References 77 publications

Border Collie Optimization

Border Collie Optimization

Multi-objective stochastic closed-loop supply chain network design with social considerations

Coronavirus herd immunity&nbsp;optimizer (CHIO)

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Coronavirus herd immunity optimizer (CHIO)