A Novel Swarm-Intelligence based Optimization Algorithm: Rat Swarm Optimizer (RSO) for Solving the Challenging Optimization Problems

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1. Introduction

Nature-inspired algorithms are becoming extremely popular for solving various optimization problems [1]. This is due to the fact that these algorithms search for the fittest solution based upon ‘trial and error’ criterion. Also, they are easy to implement due to their simple conceptual model and the least requirement of gradient information [2]. Broadly, these algorithms have been inspired by theories like natural selection and social behavior of living organisms. Hence in Rat Swarm Optimizer (RSO) algorithm the chasing and attacking behaviors of rats in nature is explored. The convergence and computational analysis are also investigated to test exploration, exploitation, and local optima avoidance of the RSO algorithm [3].

Fig 1: Overview about ROA

2. Inspiration of RSO

Fig 2: Inspiration of ROA

The main inspiration of this optimizer is the chasing and attacking behaviors of rats in nature. Rats are long tailed and medium sized rodents which are different in terms of size and weight [4]. There are two main species of rat: Black rat and Brown rat. In rats family, the male rats are called bucks while female rats are called does. Rats are generally socially intelligent by nature. They groom each other and involve in various activities [5]. A novel algorithm for global optimization: Rat Swarm Optimizer such as jumping, chasing, tumbling, and boxing. Rats are territorial animals which live in a group of both males and females. The behavior of rats is very aggressive in many cases which may result in the death of some animals [6]. This aggressive behavior is the main motivation of this work while chasing and fighting with prey.

3. RSO for engineering design problems

In this section, six real-life constrained engineering design problems have been analyzed using RSO. These problems are

  • Pressure vessel problem [7]
  • Speed reducer design problem [8]
  • Welded beam design problem [9]
  • Tension/ co-impressions spring design problem [10]
  •  25-bar truss design problems [11]
  • Rolling element bearing design problems [12]
Fig 3: Applications of ROA
Fig 5: Application of ROA & its objectives

4. Mathematical model of RSO Algorithm

This subsection describes the behavior of rat i.e., chasing and fighting [13]

4.1 Chasing the prey

Generally, rats are social animals that chase the prey in a group through their social agonistic behavior. To define this behavior mathematically, we assume that the best search agent has the knowledge of location of prey. The other search agents can update their positions with respect to best search agent obtained so far [14]. The following equations are proposed in this mechanism:

5. Pseudo Code of ROA

Fig 6: Pseudo Code of ROA

6. Flowchart of ROA

The steps and flowchart of RSO are represented in the below figure;

Fig 7: Flowchart of ROA

7. Rat Models as Biological filters for large scale tumor genome data

Fig 8: Rat Models as Biological filters for large scale tumor genome data

Genetically and pathologically accurate rat models of leukemia and lymphoma have been developed in recent years. Adoptive transfer of genetically modified hematopoietic progenitor cells enables rapid and highly controlled gain- and loss-of-function studies for these types of cancer [18]. In this Commentary, we discuss how these highly versatile experimental approaches can be used as biological filters to pinpoint transformation-relevant activities from complex cancer genome data. We anticipate that the functional identification of genetic ‘drivers’ using mouse models of leukemia and lymphoma will facilitate the development of molecular diagnostics and mechanism-based therapies for patients that suffer from these diseases [19]. Rat models of cancer have an emerging role in enabling the functional annotation of complex cancer genome data obtained from human patients.

Rats models as biological filters for large scale tumor genome data. Genetic studies of highly versatile mouse models of hematopoietic cancers serve as biological filters for genome data, and allow information about biological function and clinical significance of genetic driver mutations to be obtained. In this manner rat models help to translate genomic data into better molecular diagnostics for personalized medicine, array comparative genomic hybridization, and single nucleotide polymorphism [20]. Clearly, there are logistical and conceptual shortfalls with each of these models – for example, the ubiquitous and/or non-physiological expression levels of transgenic, subtle differences in the timing and location of transformation in the models, and general species-specific differences between human and rat cells. Nevertheless, these models are helpful for isolating specific genetic changes and defining their impact on cancer phenotypes.

8. Advantages of ROA

Fig 9: Advantages of ROA

Reference

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