Vortex Swarm Optimization: New Metaheuristic Algorithm

2020 ◽  
pp. 127-136 ◽  
Author(s):  
Ahmed Sabry A. Elrahman ◽  
Hesham A. Hefny
Keyword(s):  
Metaheuristic Algorithm ◽  
Swarm Optimization

scholarly journals Resolving the optimal selection of a natural reserve using the particle swarm optimisation by applying transfer functions

2018 ◽  
Author(s):  
Boris Almonacid
Keyword(s):  
Particle Swarm Optimization ◽  
Transfer Functions ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Selection ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
Natural Reserve ◽  
Transfer Method ◽  
Selection Of

The optimal selection of a natural reserve (OSRN) is an optimisation problem with a binary domain. To solve this problem the metaheuristic algorithm Particle Swarm Optimization (PSO) has been chosen. The PSO algorithm has been designed to solve problems in real domains. Therefore, a transfer method has been applied that converts the equations with real domains of the PSO algorithm into binary results that are compatible with the OSRN problem. Four transfer functions have been tested in four case studies to solve the OSRN problem. According to the tests carried out, it is concluded that two of the four transfer functions are apt to solve the problem of optimal selection of a natural reserve.

scholarly journals Refined Heuristic Swarm Intelligence Algorithm

2020 ◽  
pp. 267-275
Author(s):  
I. I. Aina ◽  
C. N. Ejieji
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

In this paper, a new metaheuristic algorithm named refined heuristic intelligence swarm (RHIS) algorithm is developed from an existing particle swarm optimization (PSO) algorithm by introducing a disturbing term to the velocity of PSO and modifying the inertia weight, in which the comparison between the two algorithms is also addressed.

scholarly journals Resolving the optimal selection of a natural reserve using the particle swarm optimisation by applying transfer functions

2018 ◽  
Author(s):  
Boris L Almonacid
Keyword(s):  
Particle Swarm Optimization ◽  
Transfer Functions ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Selection ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
Natural Reserve ◽  
Transfer Method ◽  
Selection Of

The optimal selection of a natural reserve (OSRN) is an optimisation problem with a binary domain. To solve this problem the metaheuristic algorithm Particle Swarm Optimization (PSO) has been chosen. The PSO algorithm has been designed to solve problems in real domains. Therefore, a transfer method has been applied that converts the equations with real domains of the PSO algorithm into binary results that are compatible with the OSRN problem. Four transfer functions have been tested in four case studies to solve the OSRN problem. According to the tests carried out, it is concluded that two of the four transfer functions are apt to solve the problem of optimal selection of a natural reserve.

Chaos-Enhanced Firefly Algorithm with Automatic Parameter Tuning

2011 ◽  
Vol 2 (4) ◽  
pp. 1-11 ◽  
Author(s):  
Xin-She Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Design Problem ◽  
Firefly Algorithm ◽  
Parameter Tuning ◽  
Population Based ◽  
Metaheuristic Algorithms ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
New Algorithms ◽  
Automatic Parameter Tuning

Many metaheuristic algorithms are nature-inspired, and most are population-based. Particle swarm optimization is a good example as an efficient metaheuristic algorithm. Inspired by PSO, many new algorithms have been developed in recent years. For example, firefly algorithm was inspired by the flashing behaviour of fireflies. In this paper, the author extends the standard firefly algorithm further to introduce chaos-enhanced firefly algorithm with automatic parameter tuning, which results in two more variants of FA. The author first compares the performance of these algorithms, and then uses them to solve a benchmark design problem in engineering. Results obtained by other methods will be compared and analyzed.

scholarly journals The Improved Antlion Optimizer and Artificial Neural Network for Chinese Influenza Prediction

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Hongping Hu ◽  
Yangyang Li ◽  
Yanping Bai ◽  
Juping Zhang ◽  
Maoxing Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Optimization Problems ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
Sine Cosine Algorithm ◽  
Complex Optimization ◽  
Predicted Model ◽  
Moth Flame Optimization Algorithm ◽  
Development Capability

The antlion optimizer (ALO) is a new swarm-based metaheuristic algorithm for optimization, which mimics the hunting mechanism of antlions in nature. Aiming at the shortcoming that ALO has unbalanced exploration and development capability for some complex optimization problems, inspired by the particle swarm optimization (PSO), the updated position of antlions in elitism operator of ALO is improved, and thus the improved ALO (IALO) is obtained. The proposed IALO is compared against sine cosine algorithm (SCA), PSO, Moth-flame optimization algorithm (MFO), multi-verse optimizer (MVO), and ALO by performing on 23 classic benchmark functions. The experimental results show that the proposed IALO outperforms SCA, PSO, MFO, MVO, and ALO according to the average values and the convergence speeds. And the proposed IALO is tested to optimize the parameters of BP neural network for predicting the Chinese influenza and the predicted model is built, written as IALO-BPNN, which is against the models: BPNN, SCA-BPNN, PSO-BPNN, MFO-BPNN, MVO-BPNN, and ALO-BPNN. It is shown that the predicted model IALO-BPNN has smaller errors than other six predicted models, which illustrates that the IALO has potentiality to optimize the weights and basis of BP neural network for predicting the Chinese influenza effectively. Therefore, the proposed IALO is an effective and efficient algorithm suitable for optimization problems.

scholarly journals Bat algorithm (BA): review, applications and modifications

2020 ◽  
Vol 8 (1) ◽  
pp. 1
Author(s):  
Amar Yahya Zebari ◽  
Saman M. Almufti ◽  
Chyavan Mohammed Abdulrahman
Keyword(s):  
Emission Rate ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Search Process ◽  
Metaheuristic Algorithm ◽  
Swarm Optimization ◽  
Timely Review ◽  
Wide Range ◽  
Discrete Spaces ◽  
Almost All

Generally, Metaheuristic algorithms such as ant colony optimization, Elephant herding algorithm, particle swarm optimization, bat algorithms becomes a powerful methods for solving optimization problems. This paper provides a timely review of the bat algorithm and its new variants.Bat algorithm (BA) is a Swarm based metaheuristic algorithm developed in 2010 by Xin-She Yang, BA has been inspired by the foraging behavior of micro bats, algorithm carries out the search process using artificial bats as search agents mimicking the natural pulse loudness and emission rate of real bats. It has become a powerful swarm intelligence method for solving optimization prob-lems over continuous and discrete spaces. Nowadays, it has been successfully applied to solve problems in almost all areas of opti-mization, and it found to be very efficient. As a result, the literature has expanded significantly, a wide range of diverse applications and case studies has been made base on the bat algorithm. 

A Multilevel Image Thresholding Approach Based on Crow Search Algorithm and Otsu Method

2020 ◽  
Vol 19 (02) ◽  
pp. 2050015 ◽  
Author(s):  
Forough Shahabi ◽  
Fereshteh Poorahangaryan ◽  
S. A. Edalatpanah ◽  
Homayoun Beheshti
Keyword(s):  
Image Segmentation ◽  
Firefly Algorithm ◽  
Search Algorithm ◽  
Computational Time ◽  
Metaheuristic Algorithm ◽  
Multilevel Thresholding ◽  
Swarm Optimization ◽  
Variance Minimization ◽  
Image Thresholding ◽  
Otsu Method

Image segmentation is one of the fundamental problems in the image processing, which identifies the objects and other structures in the image. One of the widely used methods for image segmentation is image thresholding that can separate pixels based on the specified thresholds. Otsu method calculates the thresholds to divide two or multiple classes based on between-class variance maximization and within-class variance minimization. However, increasing the number of thresholds, surging the computational time of the segmentation. To combat this drawback, the combination of Otsu and the evolutionary algorithm is usually beneficial. Crow Search Algorithm (CSA) is a novel, and efficient swarm-based metaheuristic algorithm that inspired from the way crows storing and retrieving food. In this paper, we proposed a hybrid method based on employing CSA and Otsu for multilevel thresholding. The obtained results compared with the combination of the Otsu method with three other evolutionary algorithms consisting of improved Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and also the fuzzy version of FA. Our evaluation on the five benchmark images shows competitive/improved results both in time and uniformity.

scholarly journals IMPLEMENTASI ALGORITMA CAT SWARM OPTIMIZATION DALAM MENYELESAIKAN JOB SHOP SCHEDULING PROBLEM (JSSP)

2016 ◽  
Vol 5 (3) ◽  
pp. 90
Author(s):  
I WAYAN RADIKA APRIANA ◽  
NI KETUT TARI TASTRAWATI ◽  
KARTIKA SARI
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Problems ◽  
Scheduling Problem ◽  
Metaheuristic Algorithm ◽  
Job Shop Scheduling Problem ◽  
Optimal Result ◽  
Swarm Optimization ◽  
Shop Scheduling ◽  
Cat Swarm Optimization

Cat Swarm Optimization (CSO) algorithm is a metaheuristic algorithm which is based on two behaviors of cat, seeking and tracing. CSO algorithm is used in solving optimization problems. One of the optimization problems which can be seen in daily life is Job Shop Scheduling Problem (JSSP). This study aimed to observe the performance of CSO algorithm in solving JSSP. This study focused on 5 job-12 machine cases. According to this study, CSO algorithm was effective in solving real case of JSSP in 5 jobs – 12 machines scheduling at CV Mitra Niaga Indonesia agriculture tools industry. In implementing CSO algorithm in JSSP, a correct parameter choosing could lead to an optimal result. On other hand, the greater the number of jobs or machines the more complex and difficult the JSSP that needed to be solved.

scholarly journals Sparse Based Particle Swarm Optimization Algorithm

2021 ◽  
Author(s):  
Lalit Kumar ◽  
Manish Pandey ◽  
Mitul Kumar Ahirwal
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Computation Time ◽  
Pso Algorithm ◽  
Computational Time ◽  
Metaheuristic Algorithm ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Almost All

Abstract Particle Swarm Optimization (PSO) is the well-known metaheuristic algorithm for optimization, inspired from swarm of species.PSO can be used in various problems solving related to engineering and science inclusive of but not restricted to increase the heat transfer of systems, to diagnose the health problem using PSO based on microscopic imaging. One of the limitations with Standard-PSO and other swarm based algorithms is large computational time as position vectors are dense. In this study, a sparse initialization based PSO (Sparse-PSO) algorithm has been proposed. Comparison of proposed Sparse-PSO with Standard-PSO has been done through evaluation over several standard benchmark objective functions. Our proposed Sparse-PSO method takes less computation time and provides better solution for almost all benchmark objective functions as compared to Standard-PSO method.

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