25 research outputs found
A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications
The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological
behaviors of fish schooling in nature, viz., the preying, swarming, following
and random behaviors. Owing to a number of salient properties, which include
flexibility, fast convergence, and insensitivity to the initial parameter
settings, the family of AFSA has emerged as an effective Swarm Intelligence
(SI) methodology that has been widely applied to solve real-world optimization
problems. Since its introduction in 2002, many improved and hybrid AFSA models
have been developed to tackle continuous, binary, and combinatorial
optimization problems. This paper aims to present a concise review of the
family of AFSA, encompassing the original ASFA and its improvements,
continuous, binary, discrete, and hybrid models, as well as the associated
applications. A comprehensive survey on the AFSA from its introduction to 2012
can be found in [1]. As such, we focus on a total of {\color{blue}123} articles
published in high-quality journals since 2013. We also discuss possible AFSA
enhancements and highlight future research directions for the family of
AFSA-based models.Comment: 37 pages, 3 figure
Chaotic Sand Cat Swarm Optimization
In this study, a new hybrid metaheuristic algorithm named Chaotic Sand Cat Swarm Optimization (CSCSO) is proposed for constrained and complex optimization problems. This algorithm
combines the features of the recently introduced SCSO with the concept of chaos. The basic aim of
the proposed algorithm is to integrate the chaos feature of non-recurring locations into SCSO’s core
search process to improve global search performance and convergence behavior. Thus, randomness
in SCSO can be replaced by a chaotic map due to similar randomness features with better statistical
and dynamic properties. In addition to these advantages, low search consistency, local optimum trap,
inefficiency search, and low population diversity issues are also provided. In the proposed CSCSO,
several chaotic maps are implemented for more efficient behavior in the exploration and exploitation
phases. Experiments are conducted on a wide variety of well-known test functions to increase the
reliability of the results, as well as real-world problems. In this study, the proposed algorithm was
applied to a total of 39 functions and multidisciplinary problems. It found 76.3% better responses
compared to a best-developed SCSO variant and other chaotic-based metaheuristics tested. This
extensive experiment indicates that the CSCSO algorithm excels in providing acceptable results
Using MapReduce Streaming for Distributed Life Simulation on the Cloud
Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp
Target allocation of WSN based on Parallel Chaotic Elite Quantum-Inspired Evolutionary Algorithm
© 2015 IEEE. The target allocation problem is one of the important challenges in WSNs as sensor nodes have limited sensing and communication capabilities. In the target allocation problem, a set of targets is selected for each sensor to improve the monitoring quality as well as the energy efficiency. However, the target allocation problem is a combinatorial optimization problem, and the computational complexity is too high to consider all combinations for practical implementation. In this paper, we propose a novel Parallel Chaotic Elite Quantum-Inspired Evolutionary Algorithm (PCEQEA) for target allocation problem in WSNs. The PCEQEA combines the advantages of elite genetic algorithm and quantum-inspired evolutionary algorithm. It achieves high parallel search performance and fast convergence to global optimum solution. Simulation results demonstrate that proposed PCEQEA improves WSN detection coverage by detecting more targets than other existing schemes
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications