Re-formulated snowflake optimization algorithm (SFO-R)

Modeling snowflake movements as an optimization algorithm is a recently proposed idea with a straightforward formulation. In this paper, we proposed to use different formulations for the movements of the snowflakes and presented a new nature-inspired optimization algorithm based on the same phenomenon. The algorithm is based on the forces that affect a snowflake during the snowfall and the collisions between them. We tested the algorithm on three benchmark sets, CEC 2017, CEC 2020 and CEC 2011 and compared its performance with PSO, WOA, BBO, GWO, BAT and MFO in solving these three benchmark sets. We evaluated the results both statistically and with the Wilcoxon signed-rank test. The evaluations shown that the proposed algorithm has well-balanced exploration and exploitation abilities and outperformed the other algorithms for all the comparison criteria except the execution time. Also, the pairwise comparisons indicated that it is more stable than the other algorithms in solving CEC 2017 and CEC 2020 benchmark problems

A NOVEL IMAGE CLUSTERING ALGORITHM BASED ON DS and FCM

Medical Technologies National Conference (TIPTEKNO) -- OCT 15-18, 2015 -- Bodrum, TURKEYWOS: 000380505200014Image clustering, the separation process of the image to the clusters which have pixels with similar properties is one of the most important sections of the image processing procedures. In this study we developed a novel image clustering algorithm by combining Differential Search algorithm (DS) which is a new optimization algorithm and Fuzzy Clustering Algorithm (FCM). Combining procedure was realized by minimizing the objective function of FCM by DS. As for testing purposes 3 brain MRI images were clustered by the new algorithm and the classical FCM. Both of the algorithms were compared in terms of objective function values. According to the results, the new algorithm out performs the classical FCM algorithm in terms of image clustering

A novel approach for nature-based optimization algorithms: The threat factor approach

WOS:000644953400001Nature-inspired optimization algorithms especially those based on the hunting behaviors of the creatures assume that the hunting operations are performed in a safe environment. However, generally, there are threats in real-life for the hunter-animals. This paper focuses on these threat factors and proposes that they can be used to improve the searching abilities of the algorithms. Gray wolf optimization (GWO) algorithm was selected to present the proposed approach and it was assumed that there was a mountain lion as the threat factor living in the same habitat with the wolf pack. The relations between the two predators were modeled and used to improve the performance of the algorithm. Five experiments were conducted to test the performance of the proposed method and the results were compared with the GWO and four optimization algorithms from the literature. It is shown that the proposed algorithm obtained best results for 21 of the 50 benchmark functions, while its closest competitor achieved the best results for 16 functions. Besides, the results of the Wilcoxon signed-rank test indicated that the proposed method is superior to all other methods. In addition, it was shown that the threat factor approach does not cause a significant increase in the processing time

Chaos-based Vortex Search algorithm for solving inverse kinematics problem of serial robot manipulators with offset wrist

WOS: 000520042200021Vortex Search (VS) algorithm is a single-solution-based optimization algorithm that requires the high maximum number of iterations (NOI) to solve optimization problems. In this study, two methods were proposed to reduce the required maximum NOI of the VS algorithm. These methods are based on using ten chaos maps with the VS algorithm and provide improvements in the exploration and exploitation abilities of the algorithm for reducing the required maximum NOI. Ten chaos-based VS algorithms (CVSs) were obtained by combining these methods with the VS algorithm. The performances of the CVS algorithms were tested by fifty benchmark functions. The results were evaluated in terms of some statistical values and a pairwise statistical test, Wilcoxon Signed-Rank Test. According to the results, it was found that the CVS algorithm obtained by using the Gauss-Mouse chaos map was the best algorithm. And also, it was shown that the proposed CVS algorithm performs better than the classical VS algorithm, even when its maximum NOI was ten times less than the maximum NOI of the VS algorithm. Additionally, the effects of the proposed methods in the exploration and the exploitation abilities of the VS algorithm were visually shown and a comparison about algorithm processing time was presented. In order to test the performance of the proposed CVS algorithm in solving the real-world optimization problems, the inverse kinematics problem of a six Degrees Of Freedom (DOF) serial robot manipulator with offset wrist was solved with both the proposed CVS algorithm and the VS algorithm for two different types of trajectories. The results showed that the proposed algorithm outperforms the VS algorithm in terms of the objective function values and position errors of the end-effector of the serial robot manipulator. (C) 2020 Elsevier B.V. All rights reserved

Kinematic analysis of a 3-DOF asymmetrical planar parallel robot mechanism

Bu çalışmada üç Serbestlik Derecesine (SD) sahip bir düzlemsel paralel robot mekanizmasının kinematik analizi gerçekleştirilmiştir. Seçilen mekanizmanın diğer düzlemsel mekanizmalardan farkı asimetrik bacak yapısına sahip olmasıdır. Asimetrik yapıyı elde etmek için 3-RPR (R:Dönel eklem, P: Aktif prizmatik eklem) yapısındaki simetrik bir düzlemsel robot mekanizmasının bir bacağı RRR (R: Aktif dönel eklem) tipi bacak ile değiştirilmiş ve bu sayede RPR2RRR1 adını verdiğimiz asimetrik düzlemsel paralel robot mekanizması elde edilmiştir. Bu mekanizma için ters kinematik, Jacobian matrisi ve tekil noktalardan bağımsız çalışma uzayı analizi ile ilgili hesaplamalar gerçekleştirilmiştir. Ayrıca bu mekanizmanın performansı simetrik düzlemsel bir paralel robot mekanizması olan 3-RPR mekanizması ile karşılaştırılmıştır. Elde edilen sonuçlara göre önerilen mekanizmanın çalışma uzayının hem uç işlevci tarafından ulaşılabilinen nokta sayısı hem de yönelim açısının sınır değerleri yönünden 3-RPR mekanizmasından daha iyi olduğu gösterilmiştir.In this study, kinematic analysis of a planar parallel robot mechanism with three degrees of freedom (DOF) was performed. The difference of the selected mechanism from the other planar mechanisms is that it has an asymmetric leg structure. In order to provide the asymmetry, a leg of 3-RPR (R: Revolute joint, P: Active prismatic joint) symmetrical planar robot mechanism was replaced by a RRR (R: Active revolute joint) type leg and the asymmetrical planar parallel robot named RPR2RRR1 mechanism has been obtained. Inverse kinematics, Jacobian matrix and singularity free workspace analysis were performed for the proposed mechanism. In addition, the performance of this mechanism is compared with the 3-RPR mechanism, which is a symmetric planar parallel robot mechanism. According to the obtained results, it has been shown that the workspace of the proposed mechanism is better than the 3-RPR mechanism in terms of both the number of points that can be reached by the end-effector and the limit values of the orientation angle

An improved form of the ant lion optimization algorithm for image clustering problems

Thispaperproposesanimprovedformoftheantlionoptimizationalgorithm(IALO)tosolveimageclustering problem. The improvement of the algorithm was made using a new boundary decreasing procedure. Moreover, a recently proposed objective function for image clustering in the literature was also improved to obtain well-separated clusters while minimizing the intracluster distances. In order to accurately demonstrate the performances of the proposed methods, firstly, twenty-three benchmark functions were solved with IALO and the results were compared with the ALO and a chaos-based ALO algorithm from the literature. Secondly, four benchmark images were clustered by IALO and the obtained results were compared with the results of particle swarm optimization, artificial bee colony, genetic, and K- means algorithms. Lastly, IALO, ALO, and the chaos-based ALO algorithm were compared in terms of image clustering by using the proposed objective function for three benchmark images. The comparison was made for the objective function values, the separateness and compactness properties of the clusters and also for two clustering indexes Davies– Bouldin and Xie–Beni. The results showed that the proposed boundary decreasing procedure increased the performance of the IALO algorithm, and also the IALO algorithm with the proposed objective function obtained very competitive results in terms of image clustering.</jats:p

Design and kinematic analysis of a 6-DOF asymmetric parallel robot manipulator with 4-SPS and 2-CPS Type legs

This paper presents the design, inverse kinematic, Jacobian and workspace analysis of a six Degrees of Freedom (6-DOF) asymmetric parallel robot manipulator. The manipulator was designed by two CPS and four SPS type legs (C: passive cylindrical joint, P: active prismatic joint, S: passive spherical joint). The mechanism includes two passive cylindrical joints at the lower ends of its two legs instead of the spherical joints as in its symmetrical counterparts. Thus, the asymmetry of the mechanism is because of the two cylindrical joints. The basic mathematical formulations for inverse kinematic and Jacobian matrix analyses are given, and the effects of the cylindrical joints on the reachable workspace of the mechanism are presented. Moreover, the dextrous translational workspace of the mechanism is also obtained and compared by the workspace of the classic symmetrical six DOF SPS parallel manipulator. The results show that using cylindrical joints make the mechanism has better workspace characteristics than the classical one

An improved form of the ant lion optimization algorithm for image clustering problems

WOS: 000463355800054This paper proposes an improved form of the ant lion optimization algorithm (IALO) to solve image clustering problem. The improvement of the algorithm was made using a new boundary decreasing procedure. Moreover, a recently proposed objective function for image clustering in the literature was also improved to obtain well-separated clusters while minimizing the intracluster distances. In order to accurately demonstrate the performances of the proposed methods, firstly, twenty-three benchmark functions were solved with IALO and the results were compared with the ALO and a chaos-based ALO algorithm from the literature. Secondly, four benchmark images were clustered by IALO and the obtained results were compared with the results of particle swarm optimization, artificial bee colony, genetic, and Kmeans algorithms. Lastly, IALO, ALO, and the chaos-based ALO algorithm were compared in terms of image clustering by using the proposed objective function for three benchmark images. The comparison was made for the objective function values, the separateness and compactness properties of the clusters and also for two clustering indexes Davies-Bouldin and Xie-Beni. The results showed that the proposed boundary decreasing procedure increased the performance of the IALO algorithm, and also the IALO algorithm with the proposed objective function obtained very competitive results in terms of image clustering