Nature-inspired algorithms are becoming extremely popular for solving various optimization problems . 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 . 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 .
2. Inspiration of RSO
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 . 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 . 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 . 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 
- Speed reducer design problem 
- Welded beam design problem 
- Tension/ co-impressions spring design problem 
- 25-bar truss design problems 
- Rolling element bearing design problems 
4. Mathematical model of RSO Algorithm
This subsection describes the behavior of rat i.e., chasing and fighting 
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 . The following equations are proposed in this mechanism:
5. Pseudo Code of ROA
6. Flowchart of ROA
The steps and flowchart of RSO are represented in the below figure;
7. 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 . 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 . 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 . 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
G. Dhiman, M. Garg, A. Nagar, V. Kumar and M. Dehghani, “A novel algorithm for global optimization: Rat Swarm Optimizer”, Journal of Ambient Intelligence and Humanized Computing, 2020. Available: 10.1007/s12652-020-02580-0.
B. Alatas, “ACROA: Artificial Chemical Reaction Optimization Algorithm for global optimization”, Expert Systems with Applications, vol. 38, no. 10, pp. 13170-13180, 2011. Available: 10.1016/j.eswa.2011.04.126.
E. Alba and B. Dorronsoro, “The Exploration/Exploitation Tradeoff in Dynamic Cellular Genetic Algorithms”, IEEE Transactions on Evolutionary Computation, vol. 9, no. 2, pp. 126-142, 2005. Available: 10.1109/tevc.2005.843751.
P. Anita and B. Kaarthick, “Oppositional based Laplacian grey wolf optimization algorithm with SVM for data mining in intrusion detection system”, Journal of Ambient Intelligence and Humanized Computing, 2019. Available: 10.1007/s12652-019-01606-6.
P. Asghari, A. Rahmani and H. Javadi, “Privacy-aware cloud service composition based on QoS optimization in Internet of Things”, Journal of Ambient Intelligence and Humanized Computing, 2020. Available: 10.1007/s12652-020-01723-7.
A. Askarzadeh and A. Rezazadeh, “A new heuristic optimization algorithm for modeling of proton exchange membrane fuel cell: bird mating optimizer”, International Journal of Energy Research, vol. 37, no. 10, pp. 1196-1204, 2012. Available: 10.1002/er.2915.
A. Khabbazi, E. Gargari and C. Lucas, “Imperialist competitive algorithm for minimum bit error rate beamforming”, International Journal of Bio-Inspired Computation, vol. 1, no. 12, p. 125, 2009. Available: 10.1504/ijbic.2009.022781.
D. Yang, X. Wang, X. Tian and Y. Zhang, “Improving monarch butterfly optimization through simulated annealing strategy”, Journal of Ambient Intelligence and Humanized Computing, 2020. Available: 10.1007/s12652-020-01702-y.
D. Wolpert and W. Macready, “No free lunch theorems for optimization”, IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 67-82, 1997. Available: 10.1109/4235.585893.
D. Wang and K. Yang, “Optimization Algorithm of Fireworks Explosion Based on Genetic Algorithm”, DEStech Transactions on Computer Science and Engineering, no., 2018. Available: 10.12783/dtcse/csse2018/24505.
P. Singh, K. Rabadiya and G. Dhiman, “A four-way decision-making system for the Indian summer monsoon rainfall”, Modern Physics Letters B, vol. 32, no. 25, p. 1850304, 2018. Available: 10.1142/s0217984918503049.
P. Singh, G. Dhiman and A. Kaur, “A quantum approach for time series data based on graph and Schrödinger equations methods”, Modern Physics Letters A, vol. 33, no. 35, p. 1850208, 2018. Available: 10.1142/s0217732318502085.
Quentin Luo and Liyong Tong, “Constitutive Modeling of Photostrictive Materials and Design Optimization of Micro cantilevers”, Journal of Intelligent Material Systems and Structures, vol. 20, no. 12, pp. 1425-1438, 2009. Available: 10.1177/1045389×09103224.
F. Han and J. Zhu, “Improved Particle Swarm Optimization Combined with Back propagation for Feed forward Neural Networks”, International Journal of Intelligent Systems, vol. 28, no. 3, pp. 271-288, 2012. Available: 10.1002/int.21569.
A. Kaveh and S. Talatahari, “Optimal design of skeletal structures via the charged system search algorithm”, Structural and Multidisciplinary Optimization, vol. 41, no. 6, pp. 893-911, 2009. Available: 10.1007/s00158-009-0462-5.
G. Dhiman and V. Kumar, “Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems”, Knowledge-Based Systems, vol. 165, pp. 169-196, 2019. Available: 10.1016/j.knosys.2018.11.024.
M. Dehghani, Z. Montazeri, O. Malik, K. Al-Haddad, J. Guerrero and G. Dhiman, “A NEW METHODOLOGY CALLED DICE GAME OPTIMIZER FOR CAPACITOR PLACEMENT IN DISTRIBUTION SYSTEMS”, Electrical Engineering & Electromechanics, vol. 0, no. 1, pp. 61-64, 2020. Available: 10.20998/2074-272x.2020.1.10.
Y. Zhu, C. Dai and W. Chen, “Seeker Optimization Algorithm for Several Practical Applications”, International Journal of Computational Intelligence Systems, vol. 7, no. 2, pp. 353-359, 2013. Available: 10.1080/18756891.2013.864476.
S. Krenich, “Parallel Evolutionary Algorithm for Computationally Expensive Single Criteria Design Optimization”, Applied Mechanics and Materials, vol. 555, pp. 586-592, 2014. Available: 10.4028/www.scientific.net/amm.555.586.
G. Che, L. Liu and Z. Yu, “An improved ant colony optimization algorithm based on particle swarm optimization algorithm for path planning of autonomous underwater vehicle”, Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 8, pp. 3349-3354, 2019. Available: 10.1007/s12652-019-01531-8.