By Micael Couceiro, Pedram Ghamisi
This booklet examines the bottom-up applicability of swarm intelligence to fixing a number of difficulties, corresponding to curve becoming, photograph segmentation, and swarm robotics. It compares the services of a few of the better-known bio-inspired optimization ways, particularly Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO) and the lately proposed Fractional Order Darwinian Particle Swarm Optimization (FODPSO), and comprehensively discusses their benefits and drawbacks. additional, it demonstrates the prevalence and key benefits of utilizing the FODPSO set of rules, equivalent to its skill to supply a more robust convergence in the direction of an answer, whereas averting sub-optimality. This booklet deals a priceless source for researchers within the fields of robotics, activities technology, trend acceptance and desktop studying, in addition to for college kids of electric engineering and machine science.
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Extra info for Fractional Order Darwinian Particle Swarm Optimization: Applications and Evaluation of an Evolutionary Algorithm
In other words, from a given term r ¼ s À 1, the relevance of all previous events before it would be irrelevant as the robot would be unable to travel with such accuracy. h To clarify the previous result, let us consider the following example. 1 Considering the eSwarBot platform, a resolution of d ¼ 2:76 mm is obtained for a single pulse, taking into account that Rwheel ¼ 21:09 mm and the combination between encoders–wheel provides srev ¼ 48 pulses/revolution. 1 m without any update of the information.
To compare visually the segmented results of different test cases by FODPSO, the segmented images with various threshold levels are given in Fig. 1. As can be seen, images with a higher level of segmentation have more detail than the other images. In contrast, the 3-level segmented image is considered the roughest image in different test cases. It is easy to conclude that by increasing the level of segmentation, the segmented image includes more detail. Therefore, the 6-level segmented image in different test cases is smoother than the 3-level one.
As a conclusion, FODPSO-based image segmentation is able to reach a slightly better ﬁtness solution in less CPU processing time than its alternatives (DPSO and PSO). 2 Experimental Results 37 Fig. 1 The result of segmentation with 2, 3, 4, and 5 thresholds, respectively (from left to right). Illustration taken from (Ghamisi et al. 2012) materials. Moreover, FODPSO is slightly faster than DPSO because its fractional calculus is able to control the convergence rate of the algorithm. A swarm behavior can be divided into two main activities: exploitation and exploration.
Fractional Order Darwinian Particle Swarm Optimization: Applications and Evaluation of an Evolutionary Algorithm by Micael Couceiro, Pedram Ghamisi