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Nature Inspired Computing: Algorithms, Performance and Applications

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Nature Inspired Computing: Algorithms, Performance and Applications Nature Inspired Computing: Algorithms, Performance and Applications Submitted in partial fulfillment of the requirements of the degree of Doctor of Philosophy by Rohit Salgotra (UID: 901606022) Supervisor Dr. Urvinder Singh (Associate Professor) Department of Electronics & Communication Engineering THAPAR INSTITUTE OF ENGG. & TECHNOLOGY July 2020 Dedicated to The Grand Weaver of my life, MY PAREN T S Approval of Doctoral Committee Certified that the thesis entitled "Nature Inspired Computing: Algorithms, Perfor- mance and Applications" submitted by Mr. Rohit Salgotra to the Thapar Institute of Engineering & Technology, Patiala for the award of "Doctor of Philosophy" has been accepted by the doctoral committee members. Dr. Paramartha Dutta Dr. Urvinder Singh External Examiner Supervisor Department of Computer & system Sciences Dept. of Electronics & Communication Visva Bharati University, West Bengal Thapar Institute of Engg. & Tech, Patiala Dr. Alpana Aggarwal Dr. Kulbir Singh Chairperson, Doctoral Committee Member, Doctoral Committee Dept. of Electronics & Communication Dept. of Electronics & Communication Thapar Institute of Engg. & Tech, Patiala Thapar Institute of Engg. & Tech, Patiala Dr. Neeraj Kumar Dr. Vinay Kumar Member, Doctoral Committee Member, Doctoral Committee Dept. of Computer Science & Engg. Dept. of Electronics & Communication Thapar Institute of Engg. & Tech, Patiala Thapar Institute of Engg. & Tech, Patiala Date: July 2020 Place: Patiala, India Statement of Originality This is to certify that: • All the written material submitted in this thesis is my original work and any idea or words which have been added are adequately cited and referenced from the original source. • The work presented in this thesis has never been used for any other degree or diploma course and I adhere to academic integrity and honesty and have not falsified or misrepresented or fabricated any data or fact or idea or source in my submission. • I adhere to the norms and guidelines given in the Ethical Code of Conduct of the Institute and is liable for disciplinary actions from the sources which have thus not been properly cited or whose permission has not been taken. • This this does not contain any studies with animal or human subjects. • I declare that I have no conflict of interest. Rohit Salgotra UID: 901606022 Date: July 2020 Place: Patiala, India Supervisor Declaration Statement Certified that the thesis entitled "Nature Inspired Computing: Algorithms, Per- formance and Applications" submitted by Mr. Rohit Salgotra to the Thapar Institute of Engineering & Technology, Patiala for the award of "Doctor of Philoso- phy" has been thoroughly examined. I have reviewed the content and presentation style of this thesis and declare that it is free from plagiarism and grammatical errors. The re- search work stated in this thesis is the original work of the candidate. I confirm that the investigations have been conducted with the ethical policies of the institute and research is presented without prejudice. Dr. Urvinder Singh Associate Professor Department of Electronics & Communication, Thapar Institute of Engineering & Technology Patiala - 147004 INDIA Abstract Nature Inspired algorithms have served as the backbone of modern computing technol- ogy and over the past three decades, the field has grown enormously. A large number of applications have been solved by these algorithms and are replacing the traditional classical optimization algorithms. In this thesis, some of these nature inspired algorithms such as cuckoo search algorithm (CS), flower pollination algorithm (FPA) and others have been studied. All these algorithms are state-of-the-art algorithms and have proven their worth in terms of competitiveness and application to various domains of research. The aim is to develop new improved algorithms through mitigating well-known prob- lems that these algorithms suffer from, such as local optima stagnation, poor exploration, slow convergence and parametric complexity. Such improvements should help these new variants to solve highly challenging optimization problems in contrast to existing algo- rithms. Different ideas and logic are employed in designing such new versions such as hybridization that combine the strength of different mutation strategies to add diversity in the solution space, adaptive parameter adaptations to converge faster, improved global and local search strategy to enhance the exploration and exploitation respectively. Also self-adaptivity, population size reduction and lower computational complexity methods have been analysed to provide prospective algorithms for the next generation researchers. Apart from these, based on the mating patterns of naked mole-rat, a new algorithm namely naked mole-rat algorithm (NMR) was proposed. To validate the performance of all these developed algorithms, various challenging test suites from the IEEE-CEC bench- marks are used. Each of these benchmarks constitute problems of different characteristics such as ruggedness, multimodality, noise in fitness, ill-conditioning, non-separability and interdependence. Moreover, various real-world optimization problems from diversified fields such as Antenna arrays, frequency modulation, stirred tank reactor and others are also used. The results of comparative study and statistical tests affirm the superior and efficient performance of proposed algorithms. Table of Contents Page Abstract i List of Figures ix List of Tables xi 1 Introduction1 1.1 Motivation....................................2 1.2 Research Question...............................3 1.2.1 Aim & Objectives............................3 1.2.2 Importance of proposed work.....................4 1.3 Major Contribution of the Thesis.......................6 1.4 Organization of the Thesis...........................7 2 Research Background and Literature Review 11 2.1 Numerical Optimization............................ 12 2.1.1 Basics.................................. 12 2.1.2 Test Suites................................ 16 iii Table of Contents 2.2 Algorithms.................................... 17 2.2.1 Cuckoo Search Algorithm....................... 17 2.2.2 Flower Pollination Algorithm..................... 22 2.3 Inferences and Summary............................ 26 Part I : Cuckoo Search Algorithm 28 3 New Cuckoo Search Algorithms 31 3.1 Introduction................................... 32 3.2 The Proposed Approach............................ 35 3.2.1 Cuckoo Version 1.0........................... 35 3.2.2 Cuckoo Version 2.0........................... 37 3.2.3 Cuckoo Version 3.0........................... 38 3.2.4 Complexity analysis of proposed versions............... 38 3.3 Result and Discussion............................. 39 3.3.1 Effect of switch probability....................... 39 3.3.2 Effect of population size........................ 41 3.3.3 Comparative study........................... 44 3.3.4 Statistical testing............................ 45 3.3.5 Effect of dimension size......................... 45 3.3.6 Summary of results........................... 47 3.4 Concluding Remarks.............................. 49 4 Enhanced CVnew Algorithm 51 4.1 Introduction................................... 52 4.2 The Proposed Approach............................ 53 4.3 Result and Discussion............................. 58 4.3.1 Numerical Benchmarks and Parameter Settings........... 58 4.3.2 Algorithm Complexity......................... 60 4.3.3 CEC 2017: Statistical results for 10D, 30D and 50D......... 60 4.3.4 CEC 2017: Comparison results with respect to other algorithms.. 61 4.4 Concluding Remarks.............................. 62 iv Table of Contents 5 Extended CSsin Algorithm 63 5.1 Introduction................................... 64 5.2 The Proposed Approach............................ 65 5.3 Result and Discussion............................. 68 5.3.1 Numerical Benchmarks and Parameter Settings........... 69 5.3.2 Algorithm Complexity......................... 70 5.3.3 CEC 2017: Results for 10D, 30D, 50D and 100D .......... 70 5.3.4 CEC 2017: Results with respect to other algorithms......... 70 5.3.5 CEC 2020: Benchmark Results.................... 71 5.4 Concluding Remarks.............................. 71 6 Self-adaptive Cuckoo Search Algorithm 73 6.1 Introduction................................... 74 6.2 Theoretical Analysis of CS........................... 76 6.2.1 CS as an extension of GA....................... 76 6.2.2 CS as an extension of DE....................... 77 6.2.3 Inferences drawn and motivation behind present work........ 78 6.3 The Proposed Approach............................ 78 6.3.1 Component wise comparison of each modification proposed..... 79 6.3.2 Computational complexity of SACS.................. 86 6.4 Results and Discussion............................. 87 6.4.1 Test Suite and Parameter Settings.................. 88 6.4.2 Effect of switch probability....................... 88 6.4.3 Effect of population adaptation.................... 89 6.4.4 Effect of dimension size......................... 90 6.4.5 Effect of each modification proposed................. 91 6.4.6 Algorithm run-time complexity.................... 91 6.4.7 Comparison with respect to state of the art algorithms....... 92 6.5 Testing on Real world Optimization Problems................ 94 6.5.1 Parameter Estimation for Frequency-Modulated Sound Waves (FM) 94 6.5.2 Lennard-Jones Potential Problem (LJ)................ 96 6.5.3 Optimal Control of a Non-Linear Stirred Tank Reactor (STR)... 98 v Table of Contents 6.5.4 Spread Spectrum Radar
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