A memetic animal migration optimizer for multimodal optimization

Taymaz Rahkar Farshi


Unimodal optimization algorithms can find only one global optimum solution, while multimodal ones have the ability to detect all/most existing local/global optima in the problem space. Many practical scientific and engineering optimization problems have multiple optima to be located. There are a considerable number of optimization approaches in the literature to address the unimodal problems. Although multimodal optimization methods have not been studied as much as the unimodal ones, they have attracted an enormous amount of attention recently. However, most of them suffer from a common niching parameter problem. The main difficulty . . . faced by existing approaches is determining the proper niching radius. Determining the appropriate radius of the niche requires prior knowledge of the problem space. This paper proposes a novel multimodal optimization scheme that does not face the dilemma of having prior knowledge of the problem space as it does not require the niching parameter to be determined in advance. This scheme is the extended version of the unimodal animal migration optimization (AMO) algorithm that has the capability of taking advantage of finding multiple solutions. Like other multimodal optimization approaches, the proposed MAMO requires specific modifications to make it possible to locate multiple optima. The local neighborhood policy is modified to adapt the multimodal search by utilizing Coulomb's law. Also, Coulomb's law is also applied to decide the movement direction of the individuals. Hence, instead of moving an individual toward the two randomly chosen individuals, it moves toward the near and good enough two neighborhoods. Additionally, a further local search step is performed to improve the exploitation. To investigate the performance of the MAMO, the comparisons are conducted with five existing multi-modal optimization algorithms on nine benchmarks of the CEC 2013 competition. The experimental results reveal that the MAMO performs success in locating all or most of the local/global optima and outperforms other compared methods. Note that the source codes of the proposed MAMO algorithm are publicly available at Daha fazlası Daha az

A multi-modal bacterial foraging optimization algorithm

Taymaz Rahkar Farshi | Mohanna Orujpour


In recent years, multi-modal optimization algorithms have attracted considerable attention, largely because many real-world problems have more than one solution. Multi-modal optimization algorithms are able to find multiple local/global optima (solutions), while unimodal optimization algorithms only find a single global optimum (solution) among the set of the solutions. Niche-based multi-modal optimization approaches have been widely used for solving multi-modal problems. These methods require a predefined niching parameter but estimating the proper value of the niching parameter is challenging without having prior knowledge of the . . .problem space. In this paper, a novel multi-modal optimization algorithm is proposed by extending the unimodal bacterial foraging optimization algorithm. The proposed multi-odal bacterial foraging optimization (MBFO) scheme does not require any additional parameter, including the niching parameter, to be determined in advance. Furthermore, the complexity of this new algorithm is less than its unimodal form because the elimination-dispersal step is excluded, as is any other phase, like a clustering or local search algorithm. The algorithm is compared with six multi-modal optimization algorithms on nine commonly used multi-modal benchmark functions. The experimental results demonstrate that the MBFO algorithm is useful in solving multi-modal optimization problems and outperforms other methods Daha fazlası Daha az

Multilevel image thresholding with multimodal optimization

Taymaz Rahkar Farshi | Recep Demirci

Makale | 2021 | SPRINGER

Thresholding method is one of the most popular approaches for image segmentation where an objective function is defined in terms of threshold numbers and their locations in a histogram. If only a single threshold is considered, a segmented image with two classes is achieved. On the other hand, multiple classes in the output image are created with multilevel thresholding. Otsu and Kapur's procedures have been conventional steps for defining objective functions. Nevertheless, the fundamental problem with thresholding techniques is the determination of threshold numbers, which must be selected by the user. In that respect, thresholding . . . methods with both techniques are user-dependent, and may not be practical for real-time image processing applications. In this study, a novel thresholding algorithm without any objective function has been proposed. Histogram curve was considered as an objective function. The peaks and valley in histogram have been detected by means of multimodal particle swarm optimization algorithms. Accordingly, valleys between two peaks have been assigned as thresholds. Consequently, the developed scheme does not need any user intervention and finds the number of thresholds automatically. Furthermore, computation time is independent of the number of thresholds, whereas computation time in Otsu and Kapur procedures depends on the number of thresholds Daha fazlası Daha az

A hybrid firefly and particle swarm optimization algorithm applied to multilevel image thresholding

Taymaz Rahkar Farshi | Ahad K. Ardabili

Makale | 2021 | SPRINGER

There are many techniques for conducting image analysis and pattern recognition. This papers explores a way to optimize one of these techniques-image segmentation-with the help of a novel hybrid optimization algorithm. Image segmentation is mostly used for a semantic segmentation of images, and thresholding is one the most common techniques for performing this segmentation. Otsu's and Kapur's thresholding methods are two well-known approaches, both of which maximize the between-class variance and the entropy measure, respectively, in a gray image histogram. Both techniques were developed for bi-level thresholding. However, these tec . . .hniques can be extended to multilevel image thresholding. For this to occur, a large number of iterations are required to account for exact threshold values. However, various optimization techniques have been used to overcome this drawback. In this study, a hybrid firefly and particle swarm optimization algorithm has been applied to yield optimum threshold values in multilevel image thresholding. The proposed method has been assessed by comparing it with four well-known optimization algorithms. The comprehensive experiments reveal that the proposed method achieves better results in term of fitness value, PSNR, SSIM, FSIM, and SD Daha fazlası Daha az

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