University of Windsor Scholarship at UWindsor Electronic Theses and Dissertations Theses, Dissertations, and Major Papers 1-1-2019 Digital Filter Design Using Improved Artificial Bee Colony Algorithms Rija Raju University of Windsor Follow this and additional works at: https://scholar.uwindsor.ca/etd Recommended Citation Raju, Rija, "Digital Filter Design Using Improved Artificial Bee Colony Algorithms" (2019). Electronic Theses and Dissertations. 8177. https://scholar.uwindsor.ca/etd/8177 This online database contains the full-text of PhD dissertations and Masters’ theses of University of Windsor students from 1954 forward. These documents are made available for personal study and research purposes only, in accordance with the Canadian Copyright Act and the Creative Commons license—CC BY-NC-ND (Attribution, Non-Commercial, No Derivative Works). Under this license, works must always be attributed to the copyright holder (original author), cannot be used for any commercial purposes, and may not be altered. Any other use would require the permission of the copyright holder. Students may inquire about withdrawing their dissertation and/or thesis from this database. For additional inquiries, please contact the repository administrator via email ([email protected]) or by telephone at 519-253-3000ext. 3208. Digital Filter Design Using Improved Artificial Bee Colony Algorithms By Rija Raju A Dissertation Submitted to the Faculty of Graduate Studies through the Department of Electrical and Computer Engineering in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy at the University of Windsor Windsor, Ontario, Canada 2019 © 2019 Rija Raju Digital Filter Design Using Improved Artificial Bee Colony Algorithms by Rija Raju APPROVED BY: ______________________________________________ W.-K. Ling, External Examiner Guangdong University of Technology ______________________________________________ Z. Kobti School of Computer Science ______________________________________________ H. Wu Department of Electrical and Computer Engineering ______________________________________________ N. Kar Department of Electrical and Computer Engineering ______________________________________________ H. K. Kwan, Advisor Department of Electrical and Computer Engineering December 17, 2019 DECLARATION OF CO-AUTHORSHIP/ PREVIOUS PUBLICATION I. Co-Authorship I hereby declare that this dissertation incorporates material that is result of joint research, as follows: This dissertation also incorporates the outcome of a joint research undertaken under the supervision of and in collaboration with my advisor Dr. H. K. Kwan. The collaborative work is covered mainly in Chapter 3, Chapter 4, Chapter 5 and Chapter 6 of the dissertation. In the dissertation, the contribution of ideas, the experimental designs, the data analysis and interpretation, and the writing were performed by the author, and the contribution of my advisor include the provision of ideas, the formulation of design problems, the analysis of the design results, and the writing and editing help. Dr. A. Jiang provided the MATLAB codes for the partial l1 optimization in [33] and programming advice. I am aware of the University of Windsor Senate Policy on Authorship and I certify that I have properly acknowledged the contribution of other researchers to my dissertation and have obtained written permission from each of the co-author(s) to include the above material(s) in my dissertation. I certify that, with the above qualification, this dissertation, and the research to which it refers, is the product of my own work. II. Previous Publication This dissertation includes 4 original papers that have been previously published in peer reviewed conference proceedings, as follows: iii Dissertation Publication title/full citation Publication chapter status Chapter 3 H. K. Kwan and R. Raju, “Minimax design of linear Published phase FIR differentiators using artificial bee colony algorithm,” in Proc. of 8th International Conference on Wireless Communications and Signal Processing (WCSP 2016), Yangzhou, China, Oct. 13-15, 2016, pp. 1-4. Chapter 4 R. Raju, H. K. Kwan and A. Jiang, “Sparse FIR filter Published design using artificial bee colony algorithm,” in Proc. of IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS 2018), Windsor, Ontario, Canada, Aug. 2018, pp. 956-959. Chapter 5 R. Raju and H. K. Kwan, “FIR filter design using Published multiobjective artificial bee colony algorithm,” in Proc. of 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE 2017), Windsor, Ontario, Canada, Apr. 30-May 3, 2017, pp. 1-4. Chapter 6 R. Raju and H. K. Kwan, “IIR filter design using Published multiobjective artificial bee colony algorithm,” in Proc. of 2018 IEEE 31th Canadian Conference on Electrical and Computer Engineering (CCECE 2018), Quebec City, Quebec, Ontario, Canada, May 13-16, 2018, pp. 1- 4. iv I certify that I have obtained a written permission from the copyright owners to include the above published materials in my dissertation. I certify that the above material describes work completed during my registration as graduate student at the University of Windsor. I declare that, to the best of my knowledge, my dissertation does not infringe upon anyone’s copyright nor violate any proprietary rights and that any ideas, techniques, quotations, or any other material from the work of other people included in my dissertation, published or otherwise, are fully acknowledged in accordance with the standard referencing practices. Furthermore, to the extent that I have included copyrighted material that surpasses the bounds of fair dealing within the meaning of the Canada Copyright Act, I certify that I have obtained a written permission from the copyright owner(s) to include such material(s) in my dissertation and have included copies of such copyright clearances to my appendix. I declare that this is a true copy of my dissertation, including any final revisions, as approved by my dissertation committee and the Graduate Studies office, and that this dissertation has not been submitted for a higher degree to any other University or Institution. v ABSTRACT Digital filters are often used in digital signal processing applications. The design objective of a digital filter is to find the optimal set of filter coefficients, which satisfies the desired specifications of magnitude and group delay responses. Evolutionary algorithms are population-based metaheuristic algorithms inspired by the biological behaviors of species. Compared to gradient-based optimization algorithms such as steepest descent and Newton’s like methods, these bio-inspired algorithms have the advantages of not getting stuck at local optima and being independent of the starting point in the solution space. The limitations of evolutionary algorithms include the presence of control parameters, problem specific tuning procedure, premature convergence and slower convergence rate. The artificial bee colony (ABC) algorithm is a swarm-based search metaheuristic algorithm inspired by the foraging behaviors of honey bee colonies, with the benefit of a relatively fewer control parameters. In its original form, the ABC algorithm has certain limitations such as low convergence rate, and insufficient balance between exploration and exploitation in the search equations. In this dissertation, an ABC-AMR algorithm is proposed by incorporating an adaptive modification rate (AMR) into the original ABC algorithm to increase convergence rate by adjusting the balance between exploration and exploitation in the search equations through an adaptive determination of the number of parameters to be updated in every iteration. A constrained ABC-AMR algorithm is also developed for solving constrained optimization problems. There are many real-world problems requiring simultaneous optimizations of more than one conflicting objectives. Multiobjective (MO) optimization produces a set of feasible vi solutions called the Pareto front instead of a single optimum solution. For multiobjective optimization, if a decision maker’s preferences can be incorporated during the optimization process, the search process can be confined to the region of interest instead of searching the entire region. In this dissertation, two algorithms are developed for such incorporation. The first one is a reference-point-based MOABC algorithm in which a decision maker’s preferences are included in the optimization process as the reference point. The second one is a physical-programming-based MOABC algorithm in which physical programming is used for setting the region of interest of a decision maker. In this dissertation, the four developed algorithms are applied to solve digital filter design problems. The ABC-AMR algorithm is used to design Types 3 and 4 linear phase FIR differentiators, and the results are compared to those obtained by the original ABC algorithm, three improved ABC algorithms, and the Parks-McClellan algorithm. The constrained ABC-AMR algorithm is applied to the design of sparse Type 1 linear phase FIR filters of filter orders 60, 70 and 80, and the results are compared to three state-of-the- art design methods. The reference-point-based multiobjective ABC algorithm is used to design of asymmetric lowpass, highpass, bandpass and bandstop FIR filters, and the results are compared to those obtained by the preference-based multiobjective differential evolution algorithm. The physical-programming-based multiobjective ABC algorithm