scispace - formally typeset
Search or ask a question
Author

Adis Alihodzic

Bio: Adis Alihodzic is an academic researcher from University of Sarajevo. The author has contributed to research in topics: Swarm intelligence & Metaheuristic. The author has an hindex of 10, co-authored 32 publications receiving 448 citations.

Papers
More filters
Journal ArticleDOI
TL;DR: The new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed.
Abstract: Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed.

135 citations

Proceedings ArticleDOI
01 Jun 2017
TL;DR: This paper adjusted recent elephant herding optimization algorithm for multilevel thresholding by Kapur's and Otsu's method and it was more robust than other approaches from literature and compared with four other swarm intelligence algorithms.
Abstract: Digital images are widely used and numerous application in different scientific fields use digital image processing algorithms where image segmentation is a common task. Thresholding represents one technique for solving that task and Kapur's and Otsu's methods are well known criteria often used for selecting thresholds. Finding optimal threshold values represents a hard optimization problem and swarm intelligence algorithms have been successfully used for solving such problems. In this paper we adjusted recent elephant herding optimization algorithm for multilevel thresholding by Kapur's and Otsu's method. Performance was tested on standard benchmark images and compared with four other swarm intelligence algorithms. Elephant herding optimization algorithm outperformed other approaches from literature and it was more robust.

73 citations

Proceedings ArticleDOI
22 Jun 2015
TL;DR: This paper presents implementation of the recent fireworks algorithm adjusted for solving multilevel image thresholding problem, and employed Kapur's maximum entropy thresholding function on standard benchmark images where the optimal solutions are known from the exhaustive search.
Abstract: This paper presents implementation of the recent fireworks algorithm adjusted for solving multilevel image thresholding problem. This is an important problem since it is often used in image processing for the purpose of image segmentation. Since the number of possible threshold combinations grows exponentially with the number of desirable thresholds, standard deterministic methods could not generate satisfying results when tackling this problem. To test the performance of our proposed approach, we employed Kapur's maximum entropy thresholding function on standard benchmark images where the optimal solutions are known (up to five thresholding points) from the exhaustive search. Results show that our approach has great potential in this field.

67 citations

Proceedings ArticleDOI
01 Nov 2017
TL;DR: This paper has adjusted the elephant herding optimization algorithm for the unmanned aerial vehicle path planning problem and tested the approach using parameters of the battlefield environments from the literature and the comparative analysis has shown that this algorithm outperformed other approaches from the Literature.
Abstract: Unmanned aerial vehicle path planning is a high dimensional NP-hard problem. It is related to optimizing the flight route subject to various constraints inside the battlefield environment. Since the number of control points is large the traditional methods could not produce acceptable results when tackling this problem. Elephant herding optimization algorithm is one of the recent swarm intelligence algorithms which has not been sufficiently researched. In this paper we have adjusted the elephant herding optimization algorithm for the unmanned aerial vehicle path planning problem. We tested our approach using parameters of the battlefield environments from the literature and the comparative analysis has shown that our adjusted elephant herding optimization algorithm outperformed other approaches from the literature.

39 citations

Book ChapterDOI
01 Jan 2015
TL;DR: This chapter presents how cuckoo search and bat algorithm, as well as the modified version of the Bat algorithm, were adjusted and applied to the training of feed-forward neural networks, to search for the optimal synaptic weights of the neural network.
Abstract: Training of feed-forward neural networks is a well-known and important hard optimization problem, frequently used for classification purpose. Swarm intelligence metaheuristics have been successfully used for such optimization problems. In this chapter we present how cuckoo search and bat algorithm, as well as the modified version of the bat algorithm, were adjusted and applied to the training of feed-forward neural networks. We used these three algorithms to search for the optimal synaptic weights of the neural network in order to minimize the function errors. The testing was done on four well-known benchmark classification problems. Since the number of neurons in hidden layers may strongly influence the performance of artificial neural networks, we considered several neural networks architectures for different number of neurons in the hidden layers. Results show that the performance of the cuckoo search and bat algorithms is comparable to other state-of-the-art nondeterministic optimization algorithms, with some advantage of the cuckoo search. However, modified bat algorithm outperformed all other algorithms which shows great potential of this recent swarm intelligence algorithm.

34 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: Experimental results suggest that the proposed GWO method is more stable and yields solutions of higher quality than PSO and BFO based methods, and is found to be faster than BFO but slower than the PSO based method.
Abstract: Multilevel thresholding is one of the most important areas in the field of image segmentation. However, the computational complexity of multilevel thresholding increases exponentially with the increasing number of thresholds. To overcome this drawback, a new approach of multilevel thresholding based on Grey Wolf Optimizer (GWO) is proposed in this paper. GWO is inspired from the social and hunting behaviour of the grey wolves. This metaheuristic algorithm is applied to multilevel thresholding problem using Kapur's entropy and Otsu's between class variance functions. The proposed method is tested on a set of standard test images. The performances of the proposed method are then compared with improved versions of PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based multilevel thresholding methods. The quality of the segmented images is computed using Mean Structural SIMilarity (MSSIM) index. Experimental results suggest that the proposed method is more stable and yields solutions of higher quality than PSO and BFO based methods. Moreover, the proposed method is found to be faster than BFO but slower than the PSO based method.

225 citations

Journal ArticleDOI
TL;DR: An adaptive neuro-fuzzy inference system (ANFIS), with two heuristic-based computation methods namely biogeography-based optimization (BBO) and BAT algorithm (BA) with GIS to map flood susceptibility in a region of Iran shows its great potential by considering higher accuracy and lower computational time, in mapping and assessment of flood susceptibility.
Abstract: This paper couples an adaptive neuro-fuzzy inference system (ANFIS), with two heuristic-based computation methods namely biogeography-based optimization (BBO) and BAT algorithm (BA) with GIS to map...

179 citations

Journal ArticleDOI
TL;DR: In this paper, a novel chaotic bat algorithm (CBA) was proposed for multi-level thresholding in grayscale images using Otsu's between-class variance function.
Abstract: Multi-level thresholding is a helpful tool for several image segmentation applications Evaluating the optimal thresholds can be applied using a widely adopted extensive scheme called Otsu's thresholding In the current work, bi-level and multi-level threshold procedures are proposed based on their histogram using Otsu's between-class variance and a novel chaotic bat algorithm (CBA) Maximization of between-class variance function in Otsu technique is used as the objective function to obtain the optimum thresholds for the considered grayscale images The proposed procedure is applied on a standard test images set of sizes (512 × 512) and (481 × 321) Further, the proposed approach performance is compared with heuristic procedures, such as particle swarm optimization, bacterial foraging optimization, firefly algorithm and bat algorithm The evaluation assessment between the proposed and existing algorithms is conceded using evaluation metrics, namely root-mean-square error, peak signal to noise ratio, structural similarity index, objective function, and CPU time/iteration number of the optimization-based search The results established that the proposed CBA provided better outcome for maximum number cases compared to its alternatives Therefore, it can be applied in complex image processing such as automatic target recognition

178 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a hybrid approach between machine learning, adaptive neuro-fuzzy inference system and enhanced beetle antennae search swarm intelligence metaheuristics to predict the number of the COVID-19 cases.

167 citations