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decomposition. Each sub-band has a natural orientation.Figure 4.7 QASK data frame.Figure 4.8 Input images.Figure 4.9 Magnitude Response of (a) KARELET low-pass decomposition filter, (b) ...Figure 4.10 Original image before decomposition and reconstruction.Figure 4.11 Simulation results.Figure 4.12 PSNR comparison for different wavelets for Barbara.Figure 4.13 MSE comparison for different wavelets for Barbara.Figure 4.14 LAD comparison for different wavelets for Barbara.Figure 4.15 L2 norm comparison for different wavelets for Barbara.Figure 4.16 SNR vs. BER plot while using Haar.Figure 4.17 SNR vs. BER plot while using Db4.Figure 4.18 SNR vs. BER plot while using Bior4.4.Figure 4.19 SNR vs. BER plot while using RBio4.4.Figure 4.20 SNR vs. BER plot while using Coiflet 4.Figure 4.21 SNR vs. BER plot while using Symlet 4.Figure 4.22 SNR vs. BER plot while using KARELET.Figure 4.23 PSNR vs. iteration curve (for proposed hybrid algorithm on train ima...

      5 Chapter 5Figure 5.1 Process of dataset.Figure 5.2 Data frame of birds.Figure 5.3 Best move of birds.Figure 5.4 Performance measure of classification algorithm through trained data.Figure 5.5 Objects move vs. fitness evaluation.Figure 5.6 Data flow for decision process.Figure 5.7 Preprocessing output for future recommendation system.Figure 5.8 Stages of recommendation rate.Figure 5.9 Graph representation of PSO.Figure 5.10 Comparison on iterations and algorithms.Figure 5.11 Process of case study.Figure 5.12 Process flow of machine design.Figure 5.13 Flow of manufacturing and design process.Figure 5.14 Weight lifting data frame.Figure 5.15 Weight estimation—minimum likelihood.Figure 5.16 Weight estimation—maximum likelihood.Figure 5.17 Working principle of the model.Figure 5.18 Dataflow diagram.Figure 5.19 Design model for harvesting process.Figure 5.20 First phase—cutting the crops.Figure 5.21 Separation process.Figure 5.22 The grains and non-grains are cleaned.Figure 5.23 Process flow of entire estimation.Figure 5.24 Fitness evaluation of the established trained data.

      6 Chapter 6Figure 6.1 Flowchart of a general optimization algorithm.Figure 6.2 The pseudocode for Firefly Algorithm [8].Figure 6.3 Flowchart of Firefly Algorithm [13].Figure 6.4 Decision of movement of firefly [44].Figure 6.5 Landscape of a function with two equal global maxima.Figure 6.6 The initial locations of 25 fireflies (left) and their final location...Figure 6.7 Image segmentation example [51].Figure 6.8 Truss structure in bridges [52].Figure 6.9 Taxonomy of firefly applications [43].

      7 Chapter 7Figure 7.1 Flowers showing cross-pollination. Picture source: https://vivadiffer...Figure 7.2 Flower showing self-pollination process. Picture source: https://viva...Figure 7.3 Flow chart of flower pollination algorithm.Figure 7.4 Fifty steps of Levy flights. Picture source: https://www.researchgate...

      8 Chapter 8Figure 8.1 The flow chart of amalgamation of data mining with nature-inspired co...Figure 8.2 Swarm intelligence: (a) ants discovering two paths, selected the shor...Figure 8.3 The flow chart of swarm intelligence-based algorithms [35].Figure 8.4 Ant colony optimization algorithm processes. N and S depict nest and ...Figure 8.5 The generalized flow chart for particle swarm optimization [11].Figure 8.6 The flow chart of cuckoo algorithm.Figure 8.7 Species depicting migration with the help of islands via floating, fl...Figure 8.8 Cat swarm optimization mainly inspired by the observations of cat. Re...Figure 8.9 The schematic view of evolutionary algorithm. Ref: Internet.Figure 8.10 Genetic programming assisting in converting Darwinism into algorithm...Figure 8.11 The basic structure of artificial neural network.Figure 8.12 Membrane computing model Ref: Internet.Figure 8.13 The multi-layer structure of the immune system Ref: Internet.

      9 Chapter 9Figure 9.1 Flow diagram of optimization-based HWACWMF.Figure 9.2 Sample standard test images. (i) Case 189U1/Cyst (US image 1); (ii) C...Figure 9.3 Simulation results of denoising algorithms at noise density 40% in US...Figure 9.4 Simulation results of denoising algorithms at noise density 40% in MR...

      10 Chapter 10Figure 10.1 K-means centroid computation.Figure 10.2 Flow chart of particle swarm optimization process.Figure 10.3 Flow chart of firefly algorithm process.Figure 10.4 Classification accuracy for various clustering methods.Figure 10.5 Specificity for various clustering methods in Wisconsin dataset.Figure 10.6 Sensitivity for various clustering methods in Wisconsin dataset.Figure 10.7 F-measure for various clustering methods in Wisconsin dataset.

      11 Chapter 11Figure 11.1 Types of meta-heuristic algorithms.Figure 11.2 Types of Cuckoo search algorithm.Figure 11.3 Performance measure of Cuckoo Search Algorithm based on various para...Figure 11.4 Major categories of application of CS.Figure 11.5 Some specific applications of CS [9].Figure 11.6 PV system using MPPT with CSA.

      12 Chapter 12Figure 12.1 Categories of traditional classification of NIAs.Figure 12.2 TRIZ problem-solution approach [19].Figure 12.3 NIA + TRIZ approach [19].Figure 12.4 End goal–based classification of NIA.Figure 12.5 Diagram for fruit fly optimization algorithm FOA.Figure 12.6 Diagram for bat algorithm.Figure 12.7 Procedure of improved genetic algorithm.Figure 12.8 Flow chart of genetic algorithm to solve 0-1 knapsack problem.Figure 12.9 Execution time comparison between dynamic programming and genetic al...

      List of Tables

      1 Chapter 1Table 1.1 List of applications of various algorithms.

      2 Chapter 2Table 2.1 Factor analysis [40].Table 2.2 Performance evaluation of ABO for banking customer profile and cancer ...Table 2.3 Performance evaluation of GA and ABO for banking customer profile and ...

      3 Chapter 3Table 3.1 Size of instances that can be solved by exact algorithms.

      4 Chapter 4Table 4.1 The best chosen parameters for the hybrid bat-genetic algorithm.Table 4.2 QASK modulation setup.Table 4.3 Filter coefficients of the proposed filter bank “KARELET”.Table 4.4 Properties of KARELET filters.Table 4.5 Percentage of energy retained after KARELET decomposition and coeffici...Table 4.6 Results obtained.

      5 Chapter 6Table 6.1 Parameters and notations of Firefly Algorithm [12].

      6 Chapter 7Table 7.1 Characteristics of FPA.Table 7.2 Various types of applications of FPA [19].

      7 Chapter 9Table 9.1 Observation of PSNR values for various filtering algorithms for differ...Table 9.2 Observation of RMSE for various filtering algorithms for different sam...Table 9.3 Observation of SSIM for various filtering algorithms for different sam...Table 9.4 Difference in average PSNR between HWACWMF and optimizationbased algor...

      8 Chapter 10Table 10.1 Performance of various PSO optimized classifiers.

      9 Chapter 12Table 12.1 Acronyms used in this chapter.Table 12.2 Biology-based algorithms.Table 12.3 Non-biology–based algorithms.

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