124,535 research outputs found
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
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
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
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
Obstacle avoiding patterns and cohesiveness of fish school
This paper is devoted to studying obstacle avoiding patterns and cohesiveness
of fish school. First, we introduce a model of stochastic differential
equations (SDEs) for describing the process of fish school's obstacle
avoidance. Second, on the basis of the model we find obstacle avoiding
patterns. Our observations show that there are clear four obstacle avoiding
patterns, namely, Rebound, Pullback, Pass and Reunion, and Separation.
Furthermore, the emerging patterns change when parameters change. Finally, we
present a scientific definition for fish school's cohesiveness that will be an
internal property characterizing the strength of fish schooling. There are then
evidences that the school cohesiveness can be measured through obstacle
avoiding patterns.Comment: 20 pages, 6 figures, Journal of Theoretical Biology, 201
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
Indirect inference through prediction
By recasting indirect inference estimation as a prediction rather than a
minimization and by using regularized regressions, we can bypass the three
major problems of estimation: selecting the summary statistics, defining the
distance function and minimizing it numerically. By substituting regression
with classification we can extend this approach to model selection as well. We
present three examples: a statistical fit, the parametrization of a simple real
business cycle model and heuristics selection in a fishery agent-based model.
The outcome is a method that automatically chooses summary statistics, weighs
them and use them to parametrize models without running any direct
minimization.Comment: Rmarkdown code to replicate the paper is available at
https://www.dropbox.com/s/zk0fi8dp5i18jav/indirectinference.Rmd?dl=
Stochastic Optimization Algorithms
When looking for a solution, deterministic methods have the enormous
advantage that they do find global optima. Unfortunately, they are very
CPU-intensive, and are useless on untractable NP-hard problems that would
require thousands of years for cutting-edge computers to explore. In order to
get a result, one needs to revert to stochastic algorithms, that sample the
search space without exploring it thoroughly. Such algorithms can find very
good results, without any guarantee that the global optimum has been reached;
but there is often no other choice than using them. This chapter is a short
introduction to the main methods used in stochastic optimization.Comment: 16 pages, 4 figures, 2 table
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