Circle Detection by Harmony Search Optimization

Bio-inspired computing has demonstrated to be useful in several application areas. Over the last decade, new bio-inspired algorithms have emerged with applications for detection, optimisation and classification for use in computer vision tasks. On the other hand, automatic circle detection in digital images is considered an important and complex task for the computer vision community. Consequently, a tremendous amount of research has been devoted to find an optimal circle detector. This article presents an algorithm for the automatic detection of circular shapes from complicated and noisy images with no consideration of the conventional Hough transform principles. The proposed algorithm is based on newly developed Artificial Immune Optimisation (AIO) technique, known as the Clonal Selection Algorithm (CSA). The CSA is an effective method for searching and optimising following the Clonal Selection Principle (CSP) in the human immune system which generates a response according to the relationship between antigens (Ags), i.e. patterns to be recognised and antibodies (Abs), i.e. possible solutions. The algorithm uses the encoding of three points as candidate circles (x,y,r) over the edge image. An objective function evaluates if such candidate circles (Ab) are actually present in the edge image (Ag). Guided by the values of this objective function, the set of encoded candidate circles are evolved using the CSA so that they can fit to the actual circles on the edge map of the image. Experimental results over several synthetic as well as natural images with varying range of complexity validate the efficiency of the proposed technique with regard to accuracy, speed and robustness.

An improved harmony search algorithm for power economic load dispatch

Harmony search (HS) algorithm is an emerging population oriented stochastic metaheuristic algorithm, which is inspired by the music improvisation process. This paper introduces an Amended harmony search (AHS) algorithm for solving optimization problems. In AHS, an enhanced approach is employed for generating better solution that improves accuracy and convergence speed of harmony search (HS). The effects of various parameters on harmony search algorithm are analyzed in this paper. The proposed approach performs fine tuning of two parameters bandwidth and pitch adjustment rate. The proposed algorithm is demonstrated on complex benchmark functions and results are compared with two recent variants of HS optimization algorithms, improved harmony search (IHS) and highly reliable harmony search (HRHS). Results suggested that the AHS method has strong convergence and has better balance capacity of exploration and exploitation.

Circle detection is a critical issue in computer vision and image processing. Whether in natural images or industrial images, the accuracy of circle detection has a significant impact on advanced vision applications. Conventional methods, such as circle Hough transform, random circle detection, and EDCircles, only reach pixel-level edge accuracy. This article proposes a subpixel circle detection method based on subpixel edges with accuracy of one-tenth of one pixel. All candidate circles are first detected by EDCircles. The circle scoring formula based on polarity, radius, and contour is then proposed to sort the detected circles, and the circle with the highest score is selected as the target. The 2-D subpixel calculation problem is transformed into the 1-D fitting problem, and the subpixel edge region is selected according to the gradient direction of the circular edge. To reduce the error of the step model and the real edge, the blurred edge model is proposed to fit the region. Subsequently, the parameters of the edge model are transformed into subpixel coordinates. To solve the problem that the traditional <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L2$ </tex-math></inline-formula> -loss function is not robust to outliers, the Huber loss function is finally applied to the circle fitting, and the gradient descent method is adopted to calculate the circle parameters. Experiments on natural and industrial images show that the proposed method has good performance on robustness and accuracy.

Segmentation is one of the most important tasks in image processing that endeavors to classify pixels into two or more groups according to their intensity levels and a threshold value. Since traditional image processing techniques exhibit several difficulties when they are employed to segment images, the use of evolutionary algorithms has been extended to segmentation tasks in last years. The Harmony Search Algorithm (HSA) is an evolutionary method which is inspired in musicians improvising new harmonies while playing. Different to other evolutionary algorithms, HSA exhibits interesting search capabilities still keeping a low computational overhead. In this chapter, a multilevel thresholding (MT) algorithm based on the HSA is presented. The approach combines the good search capabilities of HSA with objective functions suggested by the popular MT methods of Otsu and Kapur. The presented algorithm takes random samples from a feasible search space inside the image histogram. Such samples build each harmony (candidate solution) in the HSA context whereas its quality is evaluated considering the objective function that is employed by the Otsu’s or Kapur’s method. Guided by these objective values, the set of candidate solutions are evolved through HSA operators until an optimal solution is found. The approach generates a multilevel segmentation algorithm which can effectively identify threshold values of a digital image in a reduced number of iterations. Experimental results show a high performance of the presented method for the segmentation of digital images.

A method based on harmony search (HS) algorithm for designing an optical comb generator with excellent flatness is proposed. The comb generator with simple structure consists of four phase modulators with a single laser as the input, single radio frequency (RF) bias with different amplitudes, and DC bias for each modulator. No RF phase shifter, filter, or fiber loop is needed. Instead of following a classic mathematical model for finding the values of RF amplitudes and DC voltages for generating an optical comb, HS algorithm is implemented, which is an intelligent algorithm inspired from the improvisation of music players and seeks for the most harmonized answers corresponding to flat optical combs. The simulation results of generating two sets of optical combs by HS algorithm with 43 and 53 lines with flatness of 0.51 and 0.59 dB and then after inclusion of power conversion efficiency in the objective function of HS algorithm, two other sets of combs with 47 and 55 lines, flatness of 1.64 and 1.77 dB, and power conversion efficiency of 28% and 32% are presented. Such a device can be used in wavelength-division multiplexing systems and many other applications.

This work presents the development of multi-objective harmony search (MOHS) algorithm for optimization problem using self-adaptive improved harmony search (SIHS) algorithm which is a variant of recently developed harmony search algorithm (HS) for single objective optimization. The approach used in this work is decomposing of multiple objectives into several single objective functions which are simultaneously optimized in such a way that a nearly uniform distribution of the solutions along the pareto-front is followed. The algorithm upon testing for standard test functions has shown promising results.KeywordsHarmony SearchMulti-objective optimizationPareto front

Software testing is the primary phase, which is performed during software development and it is carried by a sequence of instructions of test inputs followed by expected output.The Harmony Search (HS) algorithm is based on the improvisation process of music.In comparison to other algorithms, the HSA has gain popularity and superiority in the field of evolutionary computation.When musicians compose the harmony through different possible combinations of the music, at that time the pitches are stored in the harmony memory and the optimization can be done by adjusting the input pitches and generate the perfect harmony.The test case generation process is used to identify test cases with resources and also identifies critical domain requirements.In this paper, the role of Harmony search meta-heuristic search technique is analyzed in generating random test data and optimized those test data.Test data are generated and optimized by applying in a case study i.e. a withdrawal task in Bank ATM through Harmony search.It is observed that this algorithm generates suitable test cases as well as test data and gives brief details about the Harmony search method.It is used for test data generation and optimization.

In this paper, a new variant of harmony search algorithm called self-adaptive differential harmony search (SADHS) algorithm is proposed to solve the combined economic emission dispatch (CEED) problems. The proposed algorithm is tested on 6- and 40 unit test systems to show the effectiveness. The simulation results obtained by the proposed algorithm are compared with the results of differential harmony search (DHS) algorithm. The results obtained by the proposed algorithm are found to be better than DHS algorithm in terms of quality of solution, speed of convergence and CPU time.

This paper presents a new modification of Harmony Search (HS) algorithm for solving global continuous optimization problems. The proposed algorithm is named self-adaptive dynamic Harmony Search (SDHS). In this modification, firstly, harmony memory consideration rate (HMCR), pitch adjusting rate (PAR) and bandwidth distance (BW), are dynamically adjusted simultaneously to keep a good balance between exploration and exploitation in the search process and different search spaces. Secondly, we introduced X best (the best value so far) to produce candidate harmony and adjust of the parameter BW. Experimental results tested on sixteen benchmark functions show that SDHS performs more effectively or at least similar to other approaches on most of test functions.

Combinatorial test case generation faces a problem on how to reduce the test cases by uncover the unnecessary test cases. So, there is a need for expert applications or strategies that generate the most optimum test cases keeping in mind the most important combinations. Complementing existing work on combinatorial test case generation strategies, also known as t-way testing strategies (i.e., where t represents interaction degree), this paper presents the design and the implementation of a new combinatorial test list generation strategy based on Harmony Search (HS) algorithm, called General T-way Harmony Search-based Strategy (GTHS). HS has been chosen to be the main engine of test generation because it could balance between intensification and diversification. Benchmarking experimental results show that GTHS produces competitive results as compared to other existing well-known optimization-based strategies and provides the support for high combination degrees (i.e., t ≤ 12).

This paper proposes an improved harmony search (HS) algorithm for the solution of the static transmission expansion planning (TEP) problem with security constraints. The modification of this method in comparison to the original harmony search algorithm includes the introduction of a variable bandwidth in the memory consideration and the pitch adjustment phase for the selection of the new harmony vector (NHV) in the improvisation step of the algorithm. In that way, if previous harmonies are considered for the NHV, the algorithm allows only the best harmonies of the harmony memory in respect of objective function value to participate in the improvisation of the NHV. The results from the application of the proposed method on IEEE 24-bus reliability test system demonstrate the potentials and the practicality of the proposed HS algorithm. (6 pages)

In this paper a new method for dynamic parameter adaptation in harmony search (HS) algorithm is proposed. HS is a music inspired metaheuristic optimization algorithm, in particular we refer to jazz improvisation and is applied to solve complex problems. In this paper we propose an improvement to the convergence of the harmony search algorithm using fuzzy logic. Simulation results show that the propose approach improves the performance of HS, and benchmark mathematical functions are used to illustrate the feasibility of the proposed approach.

Industrial loads reduce the Power Factor (PF) of supply systems, causing increases in power losses, damaging equipment and higher utility bills. Optimization techniques are used in planning reactive sources to improve PF of power systems. However, conventional techniques suffer difficulties in passing over local optimal, divergence risk, constraints handling or computing higher order derivatives. Herein, the hybridization of Particle Swarm and Harmony Search Algorithm (PS – HSA) is developed for optimal capacitor planning to improve PF, and comparison is made with the Enhanced Particle Swarm Optimization (EPSO) and Improved Adaptive Harmony Search Algorithm (IAHSA). The test systems are the Modified IEEE 6 and 16 buses and nodes respectively. To create semblance of industrial load dominated power systems, the test networks were modified by increasing the reactive load demand at all buses of the IEEE 6 and 16 by 50% and 70% respectively. The capacitor is modelled as static shunt-controlled element deployed to inject reactive power at buses/nodes. Results show that for IEEE 6 buses, PF improved from 0.68 to 0.8983, 0.8986 and 0.8992 with EPSO, IAHSA and hybrid PS – HSA respectively. Similarly, in IEEE 16 nodes, PF improved from 0.76 to 0.9439, 0.943, and 0.944 with EPSO, IAHSA and hybrid PS – HSA respectively. Furthermore, real power losses reduced from 16.94 MW to 14.03 MW in IEEE 6 buses, translating to 17.2% reduction with the hybrid PS - HSA. While in IEEE 16 nodes, reduction is from 0.719 MW to 0.69 MW accounting for 4% reduction, also with the hybrid PS - HSA.

-In this paper, Security-Constrained Unit Commitment (SCUC) model is proposed in a restructured power system. This model consists of a closed-loop modified Unit Commitment (UC) and Security-constrained Optimal Power Flow (SCOPF). The objective of this SCUC model is to obtain the maximum social welfare-based system operating cost while maintaining the system security. In conventional power systems, the demand was forecasted before market operation and determined as a fixed constant. Supplying this demand was therefore considered as a constraint. However, in restructured power system which is based on Standard Market Design (SMD), DISCOs offer the demand and their proposed prices; therefore the demand is modeled as an elastic load. Independent System Operator (ISO) is responsible for operating the power market. The ISO performed the power market using the SCUC software to obtain feasible and economical operation as much as possible. In this paper, Harmony Search Algorithm (HSA) has been implemented to solve SCUC problem. Since the SCUC problem is a non-linear, mixed integer, large scale and non-convex problem, harmony search optimization is addressed as an efficient technique to overcome the aforementioned challenges. The simulation results show that the presented method is both satisfactory and consistent with expectation.