Cover page and Table of Contents: PDF (size: 2519KB)
Full Text (PDF, 2519KB), PP.36-53
Views: 0 Downloads: 0
Nature Inspired Algorithms, Meta-heuristic optimization techniques, WOA, Cooperative Strategies, Data Cluster Problems
Computational Intelligence (CI) is an as of emerging area in addressing complex real world problems. The WOA has taken its root from the collective intelligent foraging behavior of humpback whales (Megaptera Novaeangliae). The standard WOA is suffers from the selection of best agent while whales searching and encircling prey. This research paper deals with the multi-swarm cooperative strategies for finding the best agents which balances the two phase’s exploration and exploitation. The performance of invoked Multi-Swarm cooperative strategies into standard WOA i.e, MsWOA is first benchmarked on a set of 23 standard mathematical benchmark function problems which includes 7 Uni-Modal, 6 Multi-modal and 10 fixed dimension multi-modal functions. The obtained graphical and statistical results have been portrayed along with the previously established techniques. The obtained results depicts that the proposed cooperative strategies for WOA outperforms in solving optimization problems of standard benchmark functions. This paper also studies the numerical efficiency of proposed techniques on the problem of data clustering where 10 real data clustering problems have been taken from data repository https://archive.ics.uci.edu.data. Statistical results for the obtained cluster centroids, intra-cluster distances and inter-cluster distances confirms that the cooperative strategies for best whale agent selection improves the performance WOA for function optimization problems as well as data clustering problems.
Ravi Kumar Saidala, Nagaraju Devarakonda, "Multi-Swarm Whale Optimization Algorithm for Data Clustering Problems using Multiple Cooperative Strategies", International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.8, pp.36-53, 2018. DOI:10.5815/ijisa.2018.08.04
Kacprzyk, Janusz, and Witold Pedrycz, eds. Springer handbook of computational intelligence. Springer, 2015.
Amiri, Babak. "Application of teaching-learning-based optimization algorithm on cluster analysis." Journal of Basic and Applied Scientific Research 2.11 (2012): 11795-11802.
Hoang, Duc Chinh, et al. "Real-time implementation of a harmony search algorithm-based clustering protocol for energy-efficient wireless sensor networks." IEEE Transactions on Industrial Informatics 10.1 (2014): 774-783.
Kuo, R. J., et al. "Integration of particle swarm optimization and genetic algorithm for dynamic clustering." Information Sciences 195 (2012): 124-140.
Niknam, Taher, et al. "An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering." Journal of Zhejiang University-SCIENCE A 10.4 (2009): 512-519.
Nanda, Satyasai Jagannath, and Ganapati Panda. "A survey on nature inspired metaheuristic algorithms for partitional clustering." Swarm and Evolutionary computation 16 (2014): 1-18.
Holland JH . Genetic algorithms. Sci Am 1992;267:66–72.
Rahman, Md Anisur, and Md Zahidul Islam. "A hybrid clustering technique combining a novel genetic algorithm with K-Means." Knowledge-Based Systems 71 (2014): 345-365.
Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 12:53–66.
Shelokar, P. S., Valadi K. Jayaraman, and Bhaskar D. Kulkarni. "An ant colony approach for clustering." Analytica Chimica Acta 509.2 (2004): 187-195.
Korürek, Mehmet, and Ali Nizam. "A new arrhythmia clustering technique based on Ant Colony Optimization." Journal of Biomedical Informatics 41.6 (2008): 874-881.
Storn R, Price K (2010) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J lobal Optim 23:689–694.
Das, Swagatam, Ajith Abraham, and Amit Konar. "Automatic clustering using an improved differential evolution algorithm." IEEE Transactions on systems, man, and cybernetics-Part A: Systems and Humans 38.1 (2008): 218-237.
Kwedlo, Wojciech. "A clustering method combining differential evolution with the K-means algorithm." Pattern Recognition Letters 32.12 (2011): 1613-1621.
Kennedy J , Eberhart R . Particle swarm optimization. In: Proceedings of the 1995 IEEE international conference on neural networks; 1995. p. 1942–8.
Ahmed, A., et al. "A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data." The Artificial Intelligence Review 44.1 (2015): 23.
Alam, Shafiq, et al. "Research on particle swarm optimization based clustering: a systematic review of literature and techniques." Swarm and Evolutionary Computation 17 (2014): 1-13.
Clerc M, Kennedy J (2000) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73
Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8:204–210
Peram T, Veeramachaneni K, Mohan CK (2003) Fitness-distance- ratio based particle swarm optimization. In: Proceedings of the IEEE swarm intelligence symposium, pp 174–181
Naik A, Satapathy SC, Parvathi K (2013) A comparative analysis of results of data clustering with variants of particle swarm optimization. In: International conference on swarm, evolutionary, and Memetic computing, pp 180–192
Yohannes, M. S. "Solving economic load dispatch problem using particle swarm optimization technique." International Journal of Intelligent Systems and Applications 4.12 (2012):
Ali Khazaee,"Heart Beat Classification Using Particle Swarm Optimization", International Journal of Intelligent Systems and Applications, vol.5, no.6, pp.25-33, 2013.
Poonam Singhal, S. K. Agarwal, Narendra Kumar,"Advanced Adaptive Particle Swarm Optimization based SVC Controller for Power System Stability", International Journal of Intelligent Systems and Applications, vol.7, no.1, pp.101-110, 2015.
Rashedi E , Nezamabadi-Pour H , Saryazdi S . GSA: a gravitational search algo- rithm. Inf Sci 2009;179:2232–48.
20. Hatamlou, A., Abdullah, S., Nezamabadi-pour, H.: A Combined Approach for Clustering Based on K-means and Gravitational Search Algorithms. Swarm and Evolutionary Computation 6, 47–52 (2012)
Hatamlou, A., Abdullah, S., Nezamabadi-pour, H.: Application of Gravitational Search Algorithm on Data Clustering. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds.) RSKT 2011. LNCS, vol. 6954, pp. 337–346. Springer, Heidelberg (2011)
Mirjalili, Seyedali, and Andrew Lewis. "The Whale Optimization Algorithm."Advances in Engineering Software 95 (2016): 51-67.
Saidala, Ravi Kumar, and Nagaraju Devarakonda. "Improved Whale Optimization Algorithm Case Study: Clinical Data of Anaemic Pregnant Woman." Data Engineering and Intelligent Computing. Springer, Singapore, 2018. 271-281.
Accepted: R. K. Saidala, N. Devarakonda. ―A New Parallel Metaheuristic Optimization Algorithm and It‘s Application in CDM‖, Proceedings of the I2CT IEEE conference 2017, Pune.
Ravi Kumar Saidala, Nagaraju Devarakonda “Improved Whale Optimization Algorithm using Clonal Selection Algorithm for finding optimal structures of data” Under review Information Sciences, Elsevier.
Yevgeniy Bodyanskiy, Olena Vynokurova, Volodymyr Savvo, Tatiana Tverdokhlib, Pavlo Mulesa,"Hybrid Clustering-Classification Neural Network in the Medical Diagnostics of the Reactive Arthritis", International Journal of Intelligent Systems and Applications, Vol.8, No.8, pp.1-9, 2016.