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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/10484
Title: Opposition based Laplacian Ant Lion Optimizer
Authors: Dinkar S.K.
Deep K.
Published in: Journal of Computational Science
Abstract: Performance of any nature inspired optimization algorithm is subject to appropriate combination of operators used for exploration and exploitation. The lack of this combination inclines an algorithm towards premature convergence, entrapment of local optima and inability to reach global optima. This paper presents a novel algorithm called opposition based Laplacian antlion optimizer (OB-L-ALO) to accelerate the performance of the original ALO. For achieving acceleration, exploration is to be enhanced. Two strategies are used for this purpose: Firstly, Laplace distribution is used in random walk of ALO instead of uniform distribution which ensures exploration of more search area than the original random walk of ALO. Secondly, Opposition Based Learning model which ensures the exploration of original as well as opposite candidate solutions in the search space at the same time to estimate the better candidate solutions while evolution process is in progress. A comprehensive set of 27 benchmark problems including wide range of different characteristics and different dimensions have been employed for verification of results. Also the influence of Laplace distribution random numbers and opposition based new population generation during evolution process has been analysed by behaviour of trajectories, convergence rate, data distribution of objective function values using boxplot and average fitness improvement for certain test suit. The proposed OB-L-ALO is also employed to the set of unconstrained engineering design problems of Gear Train Design and Optimal Capacity of Gas Production Facilities, showing diversity in solving the real world optimization problems. © 2017 Elsevier B.V.
Citation: Journal of Computational Science (2017), 23(): 71-90
URI: https://doi.org/10.1016/j.jocs.2017.10.007
http://repository.iitr.ac.in/handle/123456789/10484
Issue Date: 2017
Publisher: Elsevier B.V.
Keywords: Ant Lion Optimizer
Benchmark functions
Laplace distribution
Opposition based learning
Optimization
ISSN: 18777503
Author Scopus IDs: 57196220500
8561208900
Author Affiliations: Dinkar, S.K., Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Deep, K., Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Funding Details: The first author is thankful to All India Council of Technical Education (AICTE), Government of India for funding this research. Mr. Shail Kumar Dinkar is a Research Scholar at Department of Mathematics, IIT Roorkee, India. He has obtained MTech(Information Technology) from Guru Gobind Singh Indraprastha University, Delhi, India, Master of Computer Application(MCA)from U P Technical University, Lucknow, India and Bachelor of Science from C.C.S. University, Meerut, India. He has obtained national scholarship for earning maximum marks in whole district at 10th standard. He has more than 13 years of teaching experience in the field of Computer Application and Information Technology. He is pursuing Ph.D. under the supervision of Prof. Kusum Deep. He is life member of Soft Computing Research Society (SCRS). His research area includes Nature Inspired Optimization techniques such as Ant Lion Optimizer, Particle Swarm Optimization and Genetic Algorithm etc. Dr. Kusum Deep is a Professor, with the Department of Mathematics, Indian Institute of Technology Roorkee, India. Born on August 15, 1958, she pursued B.Sc. Hons. and M.Sc. Hons. School from Centre for Advanced Studies, Panjab University, Chandigarh. An M.Phil. Gold Medalist, she earned her Ph.D. from IIT Roorkee in 1988. She was awarded UGC National Merit Scholarship and UGC National Education Test Scholarship. She carried out research at Loughborough University, UK during 1993–94, under an International Post Doctorate Bursary funded by Commission of European Communities, Brussels. Fifteen students have been awarded PhD under her supervision and four are in progress. She has nearly 100 research publications in refereed International Journals and more than 77 research papers in International/National Conferences. She is on the editorial board of a number of International and National Journals. She is a Senior Member of Operations Research Society of India, IEEE, Computer Society of India, Indian Mathematical Society and Indian Society of Industrial Mathematics. She is on the Expert Panel of the Department of Science and Technology, Govt. of India. Dr. Deep is having International Collaboration with Liverpool Hope University, Liverpool, UK and Korea University, Korea. Her research interests include Numerical Optimization, Evolutionary Algorithms, Genetic Algorithms, Particle Swarm Optimization, etc.
Corresponding Author: Deep, K.; Department of Mathematics, Indian Institute of Technology RoorkeeIndia; email: kusumfma@iitr.ac.in
Appears in Collections:Journal Publications [MA]

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