Lion algorithm

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Lion algorithm (LA) is one among the bio-inspired (or) nature-inspired optimization algorithms (or) that are mainly based on meta-heuristic principles. It was first introduced by B. R. Rajakumar in 2012 in the name, Lion’s Algorithm..[1] It was further extended in 2014 to solve the system identification problem.[2] This version was referred as LA, which has been applied by many researchers for their optimization problems.[3] [4]

Inspiration from lion’s social behaviour

Lions form a social system called a "pride", which consists of 1–3 pair of lions. A pride of lions shares a common area known as territory in which a dominant lion is called as territorial lion. The territorial lion safeguards its territory from outside attackers, especially nomadic lions. This process is called territorial defense. It protects the cubs till they become sexually matured. The maturity period is about 2–4 years. The pride undergoes survival fights to protect its territory and the cubs from nomadic lions. Upon getting defeated by the nomadic lions, the dominating nomadic lion takes the role of territorial lion by killing or driving out the cubs of the pride. The lioness of the pride give birth to cubs though the new territorial lion. When the cubs of the pride mature and considered to be stronger than the territorial lion, they take over the pride. This process is called territorial take-over. If territorial take-over happens, either the old territorial lion, which is considered to be laggard, is driven out or it leaves the pride. The stronger lions and lioness form the new pride and give birth to their own cubs [5]

Terminology

In the LA, the terms that are associated with lion’s social system are mapped to the terminology of optimization problems. Few of such notable terms are related here.[3][2][4][1]

  1. Lion: A potential solution to be generated or determined as optimal (or) near-optimal solution of the problem. The lion can be a territorial lion and lioness, cubs and nomadic lions that represent the solution based on the processing steps of the LA.
  2. Territorial lion: The strongest solution of the pride that tends to meet the objective function.
  3. Nomadic lion: A random solution, sometimes termed as nomad, to facilitate the exploration principle
  4. Laggard lion: Poor solutions that are failed in the survival fight.
  5. Pride: A pool of potential solutions i.e. a lion, lioness and their cubs, that are potential solutions of the search problem.
  6. Fertility evaluation: A process of evaluating whether the territorial lion and lioness are able to provide potential solutions in the future generations i.e. It ensures that the lion or lioness converge at every generation.
  7. Survival fight: It is a greedy selection process, which is often carried out between the pride and nomadic lion.

Algorithm

The steps involved in LA are given below:[3][2][4][1]

  1. Pride Generation: Generate [math]\displaystyle{ X^{male} }[/math], [math]\displaystyle{ X^{female} }[/math]and [math]\displaystyle{ X_1^{nomad} }[/math]
  2. Determine [math]\displaystyle{ f(X^{male}) }[/math], [math]\displaystyle{ f(X^{female}) }[/math], [math]\displaystyle{ f(X_1^{nomad}) }[/math]
  3. Initialize [math]\displaystyle{ f^{ref} }[/math] as[math]\displaystyle{ f(X^{male}) }[/math] and [math]\displaystyle{ N_g }[/math] as 0
  4. Memorize [math]\displaystyle{ X^{male} }[/math] and [math]\displaystyle{ X^{female} }[/math]
  5. Apply Fertility evaluation Process
  6. Generation of cubpool by mating
  7. Gender clustering: Define [math]\displaystyle{ X_{cub}^{male} }[/math] and [math]\displaystyle{ X_{cub}^{female} }[/math]
  8. Initialize [math]\displaystyle{ age_{cub} }[/math] as zero
  9. Apply Cub growth function
  10. Territorial defense: If [math]\displaystyle{ X^{male} }[/math] (or pride) fails in the survival fight i.e. [math]\displaystyle{ X_1^{nomad} }[/math] defeats the pride, go to step 4, else continue
  11. Increase [math]\displaystyle{ age_{cub} }[/math] by 1 and check whether cub attains maturity i.e., if [math]\displaystyle{ age_{cub}\gt age_{max} }[/math],  go to Step 9, else continue
  12. Territorial takeover: If [math]\displaystyle{ X_{cub}^{male} }[/math] and [math]\displaystyle{ X_{cub}^{female} }[/math] are found to be closer to optimal solution, update [math]\displaystyle{ X^{male} }[/math] and [math]\displaystyle{ X^{female} }[/math]
  13. Increment [math]\displaystyle{ N_g }[/math] by 1
  14. Repeat from Step 5, if termination criterion is not violated, else return [math]\displaystyle{ X^{male} }[/math] as the near-optimal solution

Variants

The LA has been further taken forward to adopt in different problem areas. According to the characteristics of the problem area, significant amendment has been done in the processes and the models used in the LA. Accordingly, diverse variants have been developed by the researchers. They can be broadly grouped as hybrid LAs[6][7] and non-hybrid LAs.[8][9][10][11][12] Hybrid LAs are the LAs that are amended by the principle of other meta-heuristics,[13][14][15] whereas the Non-hybrid LAs [8] take any scientific amendment inside its operation that are felt to be essential to attend the respective problem area.[16][17]

Applications

LA is applied in diverse engineering applications[1] that range from network security,[15][18][19][20] text mining,[21][22] image processing,[23][24] electrical systems, data mining[10][25][26][27] and many more.[8][28][29][30][31] Few of the notable applications are discussed here.

  1. Networking applications: In WSN, LA is used to solve the cluster head selection problem by determining optimal cluster head.[6][12] Route discovery problem in both the VANET[9] and MANET[16] are also addressed by the LA in the literature. It is also used to detect attacks[15][20] in advanced networking scenarios such as Software-Defined Networks (SDN)[19]
  2. Power Systems: LA has attended generation rescheduling problem in a deregulated environment,[17][32][33] optimal localization and sizing of FACTS devices for power quality enhancement[14] and load-frequency controlling problem[34]
  3. Cloud computing: LA is used in optimal container-resource allocation problem in cloud environment[7][35] and cloud security[13]

References

  1. ↑ 1.0 1.1 1.2 1.3 Rajakumar BR (2012). "The Lion's Algorithm-A New Nature-Inspired Search Algorithm". Procedia Technology 6: 126–135. doi:10.1016/j.protcy.2012.10.016. 
  2. ↑ 2.0 2.1 2.2 Rajakumar BR (2014). "Lion Algorithm for Standard and Large-Scale Bilinear SystemIdentification: A Global Optimization based on Lion's Social Behavior". IEEE Congress on Evolutionary Computation (CEC) (Beijing): 2116–2123. 
  3. ↑ 3.0 3.1 3.2 Rajakumar Boothalingam (2018). "Optimization using lion algorithm: a biological inspiration from lion's social behaviour". Evolutionary Intelligence 11 (1–2): 31–52. doi:10.1007/s12065-018-0168-y. 
  4. ↑ 4.0 4.1 4.2 Rajakumar BR (2020). "Lion Algorithm and Its Applications". Frontier Applications of Nature Inspired Computation. Springer Tracts in Nature-Inspired Computing. Singapore. pp. 100–118. doi:10.1007/978-981-15-2133-1_5. ISBN 978-981-15-2132-4. 
  5. ↑ Bauer H, Longh de HH and Silvestre I (2003). "Lion social behaviourin the West and Central African Savanna belt". Mammalian Biology 68 (4): 239–243. doi:10.1078/1616-5047-00090. 
  6. ↑ 6.0 6.1 Bhardwaj R and Kumar D (2019). "MOFPL: Multi-objective fractional particle lion algorithm for the energy aware routing in the WSN". Pervasive and Mobile Computing 58: 101029. doi:10.1016/j.pmcj.2019.05.010. 
  7. ↑ 7.0 7.1 Vhatkar KN and Bhole GP. "Optimal container resource allocation in cloud architecture: A new hybrid model". Journal of King Saud University - Computer and Information Sciences. 
  8. ↑ 8.0 8.1 8.2 Lin KC, Hung JC and Wei J (2018). "Feature selection with modified lion's algorithms and support vector machine for high-dimensional data". Applied Soft Computing 68: 669–676. doi:10.1016/j.asoc.2018.01.011. 
  9. ↑ 9.0 9.1 Wagh MB and Gomathi N (2018). "Route discovery for vehicular ad hoc networks using modified lion algorithm". Alexandria Engineering Journal 57 (4): 3075–3087. doi:10.1016/j.aej.2018.05.006. 
  10. ↑ 10.0 10.1 Chander S, Vijaya P and Dhyani P (2018). "Multi kernel and dynamic fractional lion optimization algorithm for data clustering". Alexandria Engineering Journal 57 (1): 267–276. doi:10.1016/j.aej.2016.12.013. 
  11. ↑ Yazdani M and Jolai F (2016). "Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm". Journal of Computational Design and Engineering 3 (1): 24–36. doi:10.1016/j.jcde.2015.06.003. 
  12. ↑ 12.0 12.1 Sirdeshpande N and Udupi V (2017). "Fractional lion optimization for cluster head-based routing protocol in wireless sensor network". Journal of the Franklin Institute 354 (11): 4457–4480. doi:10.1016/j.jfranklin.2017.04.005. 
  13. ↑ 13.0 13.1 George A and Sumathi A (2019). "Dyadic product and crow lion algorithm based coefficient generation for privacy protection on cloud". Cluster Computing 22: 1277–1288. doi:10.1007/s10586-017-1589-6. 
  14. ↑ 14.0 14.1 Gaddala K and Raju PS (2020). "Merging Lion with Crow Search Algorithm for Optimal Location and Sizing of UPQC in Distribution Network". Journal of Control, Automation and Electrical Systems 31 (2): 377–392. doi:10.1007/s40313-020-00564-1. 
  15. ↑ 15.0 15.1 15.2 Narendrasinh BG and Vdevyas D (2019). "FLBS: Fuzzy lion Bayes system for intrusion detection in wireless communication network". Journal of Central South University 26 (11): 3017–3033. doi:10.1007/s11771-019-4233-1. 
  16. ↑ 16.0 16.1 Ambekar RK and Kolekar UD (2017). "AFL-TOHIP: Adaptive fractional lion optimization to topology-hiding multi-path routing in mobile ad hoc network". 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). Palladam. pp. 727–732. doi:10.1109/I-SMAC.2017.8058274. ISBN 978-1-5090-3242-6. 
  17. ↑ 17.0 17.1 Tapre PC, Singh DK, Paraskar SR and Zadagaonkar AS (2018). "Implementation of Improved Lion Algorithm for Generator Scheduling in Deregulated Power System using IEEE-30 Bus System". 2018 International Conference on Smart Electric Drives and Power System (ICSEDPS). Nagpur. pp. 233–238. doi:10.1109/ICSEDPS.2018.8536070. ISBN 978-1-5386-5793-5. 
  18. ↑ Selvi M and Ramakrishnan B (2019). "Lion optimization algorithm (LOA)-based reliable emergency message broadcasting system in VANET". Soft Computing: 1–18. 
  19. ↑ 19.0 19.1 Arivudainambi D, VarunKumar KA and SibiChakkaravarthy S (2019). "LION IDS: A meta-heuristics approach to detect DDoS attacks against Software-Defined Networks". Neural Computing and Applications 31 (5): 1491–1501. doi:10.1007/s00521-018-3383-7. 
  20. ↑ 20.0 20.1 Ganeshan R and Rodrigues S (2018). "I-AHSDT: intrusion detection using adaptive dynamic directive operative fractional lion clustering and hyperbolic secant-based decision tree classifier". Journal of Experimental & Theoretical Artificial Intelligence 30 (6): 1–24. doi:10.1080/0952813X.2018.1509379. Bibcode: 2018JETAI..30..887G. 
  21. ↑ Ranjan NM and Prasad RS (2018). "LFNN: Lion fuzzy neural network-based evolutionary model for text classification using context and sense based features". Applied Soft Computing 71: 994–1008. doi:10.1016/j.asoc.2018.07.016. 
  22. ↑ Nihar R and Rajesh P (2017). "Automatic text classification using BPLion-neural network and semantic word processing". The Imaging Science Journal 66: 1–15. 
  23. ↑ Ramesh P and Letitia (2017). "Parallel architecture for cotton crop classification using WLI-Fuzzy clustering algorithm and Bs-Lion neural network model". The Imaging Science Journal 65 (8): 1–19. doi:10.1080/13682199.2017.1367128. 
  24. ↑ Kumar B and Ramanaiah K (2019). "Region of interest-based adaptive segmentation for image compression using hybrid Jaya–Lion mathematical approach". International Journal of Computers and Applications: 1–12. 
  25. ↑ Chander S, Vijaya P and Dhyani P (2018). "MO-ADDOFL: Multi-objective-based adaptive dynamic directive operative fractional lion algorithm for data clustering". Majan International Conference (MIC) (Muscat): 1–6. 
  26. ↑ Chander S, Vijaya P and Dhyani P (2017). "A multi-constraint based objective function and lion optimization for the data clustering". 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS). Dubai. pp. 526–532. doi:10.1109/ICTUS.2017.8286065. ISBN 978-1-5386-0514-1. 
  27. ↑ Chander S, Vijay P and Dhyani P (2016). "ADOFL: Multi-Kernel-Based Adaptive Directive Operative Fractional Lion Optimisation Algorithm for Data Clustering". Journal of Intelligent Systems 27. 
  28. ↑ Babers R, Hassanien AE and Ghali NI (2015). "A nature-inspired metaheuristic Lion Optimization Algorithm for community detection". 11th International Computer Engineering Conference (ICENCO): 217–222. 
  29. ↑ Vijaya P and Chander S (2018). "LionRank: lion algorithm-based metasearch engines for re-ranking of webpages". Science China Information Sciences 61 (12). doi:10.1007/s11432-017-9343-5. 
  30. ↑ Ramaiah VS and Rao RR (2017). "A novel approach for speaker diarization system using TMFCC parameterization and Lion optimization". Journal of Central South University 24 (11): 2649–2663. doi:10.1007/s11771-017-3678-3. 
  31. ↑ Supreetha S, Narayan S and Prabhakar N (2020). "Lion Algorithm- Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India". Applied Computational Intelligence and Soft Computing 2020: 1–8. doi:10.1155/2020/8685724. 
  32. ↑ Paraskar S, Singh DK and Tapre PC (2017). "Lion algorithm for generation rescheduling based congestion management in deregulated power system". International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (Chennai): 401–412. 
  33. ↑ Tapre PC, Singh DK and Paraskar S (2017). "A Novel Algorithm for Generation Rescheduling Based Congestion Management". International Conference on Transforming Engineering Education (ICTEE) (Pune): 1–8. 
  34. ↑ Deepesh S and Naresh Y (2019). "Lion Algorithm with Levy Update: Load frequency controlling scheme for two-area interconnected multi-source power system". Transactions of the Institute of Measurement and Control. 
  35. ↑ Devagnanam J and Elango NM (2019). "Design and development of exponential lion algorithm for optimal allocation of cluster resources in cloud". Cluster Computing 22: 1385–1400. doi:10.1007/s10586-018-1976-7. 



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