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Title: Performance Analysis of Whale Optimization Algorithm Based on Strategy Parameter
Authors: Singh A.
Deep, K.
Nagar A.K.
Deep K.
Bansal J.C.
Das K.N.
Published in: Advances in Intelligent Systems and Computing
9th International Conference on Soft Computing for Problem Solving, SocProS 2019
Abstract: The performance of heuristic algorithms highly depends on the parameter values of the algorithms. Whale optimization algorithm (WOA) is a newly developed heuristic algorithm which has strategy parameter a that decreases linearly from 2 to 0 as iteration increases. In this paper, two algorithms, modified whale optimization algorithm-1 (MWOA-1) and modified whale optimization algorithm-2 (MWOA-2), have been proposed based on the variation of the strategy parameter a. The experiments are performed on a set of 23 benchmark problems. Results are compared with original WOA, gravitational search algorithm and grasshopper optimization algorithm. Based on the analysis of results, it is concluded that the overall performance of MWOA-1 and MWOA-2 is better than others on scalable unimodal function with dim = 30, scalable multimodal functions with dim = 30 and low-dimensional multimodal functions. © 2020, Springer Nature Singapore Pte Ltd.
Citation: Advances in Intelligent Systems and Computing (2020), 1138: 15-30
Issue Date: 2020
Publisher: Springer
Keywords: Grasshopper optimization algorithm
Heuristic algorithm
Numerical optimization
Whale optimization algorithm
ISBN: 9.78981E+12
ISSN: 21945357
Author Scopus IDs: 57211750941
Author Affiliations: Singh, A., Department of Mathematics, Janki Devi Memorial College, Sir Ganga Ram Hospital Marg, New Delhi, Delhi 110060, India
Deep, K., Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
Corresponding Author: Singh, A.; Department of Mathematics, Sir Ganga Ram Hospital Marg, India; email:
Appears in Collections:Conference Publications [MA]

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