24,968 research outputs found
A Brief Review of Nature-Inspired Algorithms for Optimization
Swarm intelligence and bio-inspired algorithms form a hot topic in the
developments of new algorithms inspired by nature. These nature-inspired
metaheuristic algorithms can be based on swarm intelligence, biological
systems, physical and chemical systems. Therefore, these algorithms can be
called swarm-intelligence-based, bio-inspired, physics-based and
chemistry-based, depending on the sources of inspiration. Though not all of
them are efficient, a few algorithms have proved to be very efficient and thus
have become popular tools for solving real-world problems. Some algorithms are
insufficiently studied. The purpose of this review is to present a relatively
comprehensive list of all the algorithms in the literature, so as to inspire
further research
Fish School Search Algorithm for Constrained Optimization
In this work we investigate the effectiveness of the application of niching
able swarm metaheuristic approaches in order to solve constrained optimization
problems. Sub-swarms are used in order to allow the achievement of many
feasible regions to be exploited in terms of fitness function. The niching
approach employed was wFSS, a version of the Fish School Search algorithm
devised specifically to deal with multi-modal search spaces. A base technique
referred as wrFSS was conceived and three variations applying different
constraint handling procedures were also proposed. Tests were performed in
seven problems from CEC 2010 and a comparison with other approaches was carried
out. Results show that the search strategy proposed is able to handle some
heavily constrained problems and achieve results comparable to the
state-of-the-art algorithms. However, we also observed that the local search
operator present in wFSS and inherited by wrFSS makes the fitness convergence
difficult when the feasible region presents some specific geometrical features
Solving Mixed Model Workplace Time-dependent Assembly Line Balancing Problem with FSS Algorithm
Balancing assembly lines, a family of optimization problems commonly known as
Assembly Line Balancing Problem, is notoriously NP-Hard. They comprise a set of
problems of enormous practical interest to manufacturing industry due to the
relevant frequency of this type of production paradigm. For this reason, many
researchers on Computational Intelligence and Industrial Engineering have been
conceiving algorithms for tackling different versions of assembly line
balancing problems utilizing different methodologies. In this article, it was
proposed a problem version referred as Mixed Model Workplace Time-dependent
Assembly Line Balancing Problem with the intention of including pressing issues
of real assembly lines in the optimization problem, to which four versions were
conceived. Heuristic search procedures were used, namely two Swarm Intelligence
algorithms from the Fish School Search family: the original version, named
"vanilla", and a special variation including a stagnation avoidance routine.
Either approaches solved the newly posed problem achieving good results when
compared to Particle Swarm Optimization algorithm
Simultaneously Solving Mixed Model Assembly Line Balancing and Sequencing problems with FSS Algorithm
Many assembly lines related optimization problems have been tackled by
researchers in the last decades due to its relevance for the decision makers
within manufacturing industry. Many of theses problems, more specifically
Assembly Lines Balancing and Sequencing problems, are known to be NP-Hard.
Therefore, Computational Intelligence solution approaches have been conceived
in order to provide practical use decision making tools. In this work, we
proposed a simultaneous solution approach in order to tackle both Balancing and
Sequencing problems utilizing an effective meta-heuristic algorithm referred as
Fish School Search. Three different test instances were solved with the
original and two modified versions of this algorithm and the results were
compared with Particle Swarm Optimization Algorithm
Weight-based Fish School Search algorithm for Many-Objective Optimization
Optimization problems with more than one objective consist in a very
attractive topic for researchers due to its applicability in real-world
situations. Over the years, the research effort in the Computational
Intelligence field resulted in algorithms able to achieve good results by
solving problems with more than one conflicting objective. However, these
techniques do not exhibit the same performance as the number of objectives
increases and become greater than 3. This paper proposes an adaptation of the
metaheuristic Fish School Search to solve optimization problems with many
objectives. This adaptation is based on the division of the candidate solutions
in clusters that are specialized in solving a single-objective problem
generated by the decomposition of the original problem. For this, we used
concepts and ideas often employed by state-of-the-art algorithms, namely: (i)
reference points and lines in the objectives space; (ii) clustering process;
and (iii) the decomposition technique Penalty-based Boundary Intersection. The
proposed algorithm was compared with two state-of-the-art bio-inspired
algorithms. Moreover, a version of the proposed technique tailored to solve
multi-modal problems was also presented. The experiments executed have shown
that the performance obtained by both versions is competitive with
state-of-the-art results
Reconstruction of Electrical Impedance Tomography Using Fish School Search, Non-Blind Search, and Genetic Algorithm
Electrical Impedance Tomography (EIT) is a noninvasive imaging technique that
does not use ionizing radiation, with application both in environmental
sciences and in health. Image reconstruction is performed by solving an inverse
problem and ill-posed. Evolutionary Computation and Swarm Intelligence have
become a source of methods for solving inverse problems. Fish School Search
(FSS) is a promising search and optimization method, based on the dynamics of
schools of fish. In this article the authors present a method for
reconstruction of EIT images based on FSS and Non-Blind Search (NBS). The
method was evaluated using numerical phantoms consisting of electrical
conductivity images with subjects in the center, between the center and the
edge and on the edge of a circular section, with meshes of 415 finite elements.
The authors performed 20 simulations for each configuration. Results showed
that both FSS and FSS-NBS were able to converge faster than genetic algorithms
Detecting phase transitions in collective behavior using manifold's curvature
If a given behavior of a multi-agent system restricts the phase variable to a
invariant manifold, then we define a phase transition as change of physical
characteristics such as speed, coordination, and structure. We define such a
phase transition as splitting an underlying manifold into two sub-manifolds
with distinct dimensionalities around the singularity where the phase
transition physically exists. Here, we propose a method of detecting phase
transitions and splitting the manifold into phase transitions free
sub-manifolds. Therein, we utilize a relationship between curvature and
singular value ratio of points sampled in a curve, and then extend the
assertion into higher-dimensions using the shape operator. Then we attest that
the same phase transition can also be approximated by singular value ratios
computed locally over the data in a neighborhood on the manifold. We validate
the phase transitions detection method using one particle simulation and three
real world examples.Comment: 17 pages, 9 figures, accepted in Journal of Mathematical Bioscience
and Engineerin
Learning to school in the presence of hydrodynamic interactions
Schooling, an archetype of collective behavior, emerges from the interactions
of fish responding to visual and other informative cues mediated by their
aqueous environment. In this context, a fundamental and largely unexplored
question concerns the role of hydrodynamics. Here, we investigate schooling by
modeling swimmers as vortex dipoles whose interactions are governed by the
Biot-Savart law. When we enhance these dipoles with behavioral rules from
classical agent based models we find that they do not lead robustly to
schooling due to flow mediated interactions. In turn, we present dipole
swimmers equipped with adaptive decision-making that learn, through a
reinforcement learning algorithm, to adjust their gaits in response to
non-linearly varying hydrodynamic loads. The dipoles maintain their relative
position within a formation by adapting their strength and school in a variety
of prescribed geometrical arrangements. Furthermore, we identify schooling
patterns that minimize the individual and the collective swimming effort,
through an evolutionary optimization. The present work suggests that the
adaptive response of individual swimmers to flow-mediated interactions is
critical in fish schooling.Comment: 18 pages, 12 figure
Towards Real-Time Advancement of Underwater Visual Quality with GAN
Low visual quality has prevented underwater robotic vision from a wide range
of applications. Although several algorithms have been developed, real-time and
adaptive methods are deficient for real-world tasks. In this paper, we address
this difficulty based on generative adversarial networks (GAN), and propose a
GAN-based restoration scheme (GAN-RS). In particular, we develop a multi-branch
discriminator including an adversarial branch and a critic branch for the
purpose of simultaneously preserving image content and removing underwater
noise. In addition to adversarial learning, a novel dark channel prior loss
also promotes the generator to produce realistic vision. More specifically, an
underwater index is investigated to describe underwater properties, and a loss
function based on the underwater index is designed to train the critic branch
for underwater noise suppression. Through extensive comparisons on visual
quality and feature restoration, we confirm the superiority of the proposed
approach. Consequently, the GAN-RS can adaptively improve underwater visual
quality in real time and induce an overall superior restoration performance.
Finally, a real-world experiment is conducted on the seabed for grasping marine
products, and the results are quite promising. The source code is publicly
available at https://github.com/SeanChenxy/GAN_RS
Segmentation of laterally symmetric overlapping objects: application to images of collective animal behaviour
Video analysis is currently the main non-intrusive method for the study of
collective behavior. However, 3D-to-2D projection leads to overlapping of
observed objects. The situation is further complicated by the absence of stall
shapes for the majority of living objects. Fortunately, living objects often
possess a certain symmetry which was used as a basis for morphological
fingerprinting. This technique allowed us to record forms of symmetrical
objects in a pose-invariant way. When combined with image skeletonization, this
gives a robust, nonlinear, optimization-free, and fast method for detection of
overlapping objects, even without any rigid pattern. This novel method was
verified on fish (European bass, Dicentrarchus labrax, and tiger barbs, Puntius
tetrazona) swimming in a reasonably small tank, which forced them to exhibit a
large variety of shapes. Compared with manual detection, the correct number of
objects was determined for up to almost of overlaps, and the mean
Dice-Sorensen coefficient was around . This implies that this method is
feasible in real-life applications such as toxicity testing.Comment: 17 pages, 4 figure
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