Fish swarm optimization algorithm applied to engineering system design

Lobato, Fran Sérgio; Steffen, Valder

doi:10.1590/s1679-78252014000100009

Cited by 29 publications

(10 citation statements)

References 22 publications

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“…Commonly, optimizations refers to finding the best method for enhancing the functionality of the system in question. [20], teaching learning-based optimization (TLBO) [21], Ant Colony Optimization (ACO) [22], Charged System Search (CSS) [23], Fish Swarm Algorithm (FSA) [24], Big Bang Big Crunch (BB-BC) [25], Lion Algorithm (LA) [26], Krill Herd (KH) [27], Elephant…”

Section: Optimizationmentioning

confidence: 99%

Vibrating Particles System Algorithm performance in solving Constrained Optimization Problem

Almufti¹

2022

ACAD J NAWROZ UNIV

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Metaheuristic algorithms are a collection of sophisticated techniques that mimic natural phenomena and the rational behavior of socially intelligent living organisms like insects and animals. These techniques are employed in the fields of computer science and engineering to address various optimization problems. In this paper, the vibrating particles system(VPS) which is a recently developed metaheuristic algorithm. Generally, an under-damped single degree of freedom (SDOF) free vibration oscillates and slowly comes into a resting or equilibrium position, and this is the inspiration idea of VPS. The tension/compression spring design problem, which is a well-known constrained based optimization problem in engineering fields have been used to evaluate VPS algorithm. The experimental result section shows the result of solving the mentioned problem by VPS with various value for variables.

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Section: Optimizationmentioning

confidence: 99%

Vibrating Particles System Algorithm performance in solving Constrained Optimization Problem

Almufti¹

2022

ACAD J NAWROZ UNIV

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“…QBSBO for constrained structural optimization problems. In this section, the QBSBO algorithm is used to solve some constrained structural problems, including pressure vessel design [31], welded beam design [27], tensile/compression spring design [35,2], cantilever Beam design [4] and reducer design [13]. 4.1.…”

Section: 7mentioning

confidence: 99%

Quantum-inspired satin bowerbird algorithm with Bloch spherical search for constrained structural optimization

Zhang¹,

Zhou²,

Zhou³

et al. 2021

JIMO

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To enhance the optimization ability of the satin bowerbird optimization (SBO) algorithm, in this paper, a novel quantum-inspired SBO with Bloch spherical search is proposed. In this algorithm, satin bowerbirds are encoded using qubits described on the Bloch sphere, each satin bowerbird occupies three locations in the search space and each location represents an optimization solution. Using the search method of general SBO to adjust the two parameters of the qubit, qubit rotation is performed on the Bloch sphere, which simultaneously updates the three locations occupied by a qubit and quickly approaches the global optimal solution. Finally, the experimental results of five examples of structural engineering design show that the proposed algorithm is superior to other state-of-the-art metaheuristic algorithms in terms of the performance measures.

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“…In order to resolve real time problems or huge dimension of data, the existing algorithms, such as K‐Means and its variance with basic optimization methods like PSO (Particle Swarm OPtimization), ACO (Ant Colony Optimization), FSO (Fish Schooling Optimization), Bacterial foraging techniques, and so forth, are used 12‐15 . Like that here also used a novel clustering algorithm for real time data.…”

Section: Motivationmentioning

confidence: 99%

Rainfall flow optimization based K‐Means clustering for medical data

Rani

Pravin

2021

Concurrency and Computation

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SummaryIn the present trend, availability of data increases more and more in all the fields in more complex way. It is very difficult for handling in an effective way with best scalability for proper decision making of knowledge extraction. K‐Means algorithm is the most familiar extensive old‐style partitioned and faster clustering algorithm than other clustering methods. But it is very subtle for initial centroids and it can be simply surrounded. So, in need of effective optimum centroid with faster clustering, this paper proposed Rainfall Flow Optimization (RFFO) based on K‐Means algorithm. RFFO is a new flood optimization technique like other optimization methods such as PSO, ACO, BCO, and so forth. RFFO is based on the nature of rainfall flow with various environments and its behavior of flow from shallow to depth. Scientific calculations are also used to find flow of data from one location to another location by its present location of waterfall, its velocity, and its most neighboring flow depth. This water storage mainly depends on nearest storage location, maximum depth, size of the storage area, and condition of the storage location. RFFO has some of the unique features than other optimization methods like speed of flow, total quantity of flow, location of flow, and so forth, that determines the optimal centroid. The performance of this proposed RFFO technique is measured with Accuracy, Jaccard Coefficient, Random Coefficient, and also compared with other existing methods using medical data set to cluster risk factor of heart disease with 300 data set.

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Fish swarm optimization algorithm applied to engineering system design

Cited by 29 publications

References 22 publications

Vibrating Particles System Algorithm performance in solving Constrained Optimization Problem

Vibrating Particles System Algorithm performance in solving Constrained Optimization Problem

Quantum-inspired satin bowerbird algorithm with Bloch spherical search for constrained structural optimization

Rainfall flow optimization based K‐Means clustering for medical data

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