FUNDAMENTALS OF COMPUTATIONAL SWARM INTELLIGENCE PDF
Request PDF on ResearchGate | On Jan 1, , Andries Petrus Engelbrecht and others published Fundamentals of Computational Swarm Intelligence. Title: Fundamentals of computational swarm intelligence pdf download, Author: septiandwirivo, Name: Fundamentals of computational swarm. Fundamentals of Computational Swarm Intelligence provides a comprehensive introduction to the new computational paradigm of Swarm Intelligence (SI).
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Swarm Intelligence. Introduction. Swarm Introduction. Hard problems. Well- defined, but computational hard problems Foundations of Bio-Computing. Meanwhile, a perhaps confusing variety of computational techniques are clarify the concept of swarm intelligence and its associations, and we attempt to . stigmergy is the fundamental concept behind one of the main swarm intelligence. network—to examine computational swarm-based systems via the optics of . is fundamental to understand intelligence in these systems.
Anthony Lewis and George A.
Bekey discusses the possibility of using swarm intelligence to control nanobots within the body for the purpose of killing cancer tumors. This was pioneered separately by Dorigo et al. Reinforcement of the route in the forwards, reverse direction and both simultaneously have been researched: backwards reinforcement requires a symmetric network and couples the two directions together; forwards reinforcement rewards a route before the outcome is known but then one would pay for the cinema before one knows how good the film is.
As the system behaves stochastically and is therefore lacking repeatability, there are large hurdles to commercial deployment. Mobile media and new technologies have the potential to change the threshold for collective action due to swarm intelligence Rheingold: , P The location of transmission infrastructure for wireless communication networks is an important engineering problem involving competing objectives.
A minimal selection of locations or sites are required subject to providing adequate area coverage for users. A very different-ant inspired swarm intelligence algorithm, stochastic diffusion search SDS , has been successfully used to provide a general model for this problem, related to circle packing and set covering.
Fundamentals of Computational Swarm Intelligence
It has been shown that the SDS can be applied to identify suitable solutions even for large problem instances. At Southwest Airlines a software program uses swarm theory, or swarm intelligence—the idea that a colony of ants works better than one alone.
Each pilot acts like an ant searching for the best airport gate. Lawson explains. As a result, the "colony" of pilots always go to gates they can arrive at and depart from quickly. The program can even alert a pilot of plane back-ups before they happen. Stanley and Stella in: Breaking the Ice was the first movie to make use of swarm technology for rendering, realistically depicting the movements of groups of fish and birds using the Boids system.
Tim Burton's Batman Returns also made use of swarm technology for showing the movements of a group of bats. The Lord of the Rings film trilogy made use of similar technology, known as Massive , during battle scenes.
Swarm technology is particularly attractive because it is cheap, robust, and simple. Information on hypotheses is diffused across the population via inter-agent communication. Unlike the stigmergic communication used in ACO, in SDS agents communicate hypotheses via a one-to-one communication strategy analogous to the tandem running procedure observed in Leptothorax acervorum.
SDS is both an efficient and robust global search and optimisation algorithm, which has been extensively mathematically described. ACO is a probabilistic technique useful in problems that deal with finding better paths through graphs.
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Artificial 'ants'—simulation agents—locate optimal solutions by moving through a parameter space representing all possible solutions. Natural ants lay down pheromones directing each other to resources while exploring their environment.
The simulated 'ants' similarly record their positions and the quality of their solutions, so that in later simulation iterations more ants locate for better solutions. Hypotheses are plotted in this space and seeded with an initial velocity , as well as a communication channel between the particles.
Over time, particles are accelerated towards those particles within their communication grouping which have better fitness values. The main advantage of such an approach over other global minimization strategies such as simulated annealing is that the large number of members that make up the particle swarm make the technique impressively resilient to the problem of local minima. Applications[ edit ] Swarm Intelligence-based techniques can be used in a number of applications.
The U. The European Space Agency is thinking about an orbital swarm for self-assembly and interferometry. NASA is investigating the use of swarm technology for planetary mapping. A paper by M. Anthony Lewis and George A. Bekey discusses the possibility of using swarm intelligence to control nanobots within the body for the purpose of killing cancer tumors.
This was pioneered separately by Dorigo et al. Reinforcement of the route in the forwards, reverse direction and both simultaneously have been researched: backwards reinforcement requires a symmetric network and couples the two directions together; forwards reinforcement rewards a route before the outcome is known but then one would pay for the cinema before one knows how good the film is. It is worth noting that the aircraft model 3 never had to be linearized in the present work.
Controller training environment in Simulink.
Lower half contains PSO block with y input and xP output. The counter block governs this particle flying time. The PSO block then specifies the position of the next particle according to the PSO algorithm 7 , 8 and 9 , and the controller coefficients Fig. Initial values for the new non-P-control coefficients scattered randomly about zero.
Initial values for proportional coefficient scattered about conventional -2 value. During flight aircraft is subject to sinusoidal pitch disturbance. Particle swarm finds position controller coefficient values for minimum aircraft pitching. With the optimum coefficient values of the cubic controller obtained using PSO Fig.
Plots of aircraft gust response for the two This latter characteristic is also a result of the derivative terms controllers under the conditions specified in Section II are in the cubic control algorithm.
Aircraft equipped with both proportional controller and PSO- optimized cubic controller for comparing wind gust response. The conditions and therefore the disturbance frequency are the same as those that were used to train the PSO-optimized cubic controller.
As evident from Fig. Chattering, however, is evident and P-controller. Coefficients of cubic controller 6 are shown in Fig. However, considering the great sensitivity of the converged coefficient values to the chosen optimization parameters , and , Figures 11 and 13 most assuredly do not portray true optimums.
All that is known is that the PSO-cubic- controlled aircraft responds considerably better to the training disturbance gust than the same aircraft equipped with P- control. This shows that no true optimum was obtained in the study, and that this dynamic PSO application needs to be further explored and modified for practical controller training.
Such an approach may reduce solution sensitivity to the selected convergence parameters. Engelbrecht, Fundamentals of Computational Swarm Intelligence, 1st ed. Eberhart and Y. Shi Kennedy and R.
Eberhart Zhangjun Karimi, I. Saboori and M. Lotfi-Forushani Gaing Wang and B. Wei Chen Gress Jalilvand, A. Kimiyaghalam, A. Ashouri and M.
Mahdavi Such a consideration is very similar to the coupling aircraft hovering in continuous, sinusoidal wind gusts, the PSO acknowledgement usually found in the rule base of a two-input algorithm iterated the coefficient solution towards a minimum fuzzy controller. Each pilot acts like an ant searching for the best airport gate.
Self-propelled particles Vicsek et al. The algorithm cycles through the N particles as many times as necessary to minimize the cost function. Initially, in place of the P-controller, a new controller of the form Fig. Interestingly, this also kept Fig. The terms in 7 are random-number vectors which multiply element-wise, each of their elements having any value from 0 to 1 from a rectangular or uniform distribution.
Nevertheless, PSO does show promise in that it already can improve the behavior of arbitrary, non-linear plants run by non-linear controllers. If then the coefficient would either converge on zero or — more frequently — diverge towards — This behavior may be a peculiarity of the aircraft and not a shortcoming of PSO in such an application.
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