A Novel Hybrid Swarm Intelligence Algorithm Combining Modified Artificial Bee Colony and Firefly Algorithms

2020 ◽  
pp. 355-370
Author(s):  
Sadman Sakib ◽  
Mahzabeen Emu ◽  
Syed Mustafizur Rahman Chowdhury ◽  
Mohammad Shafiul Alam
Keyword(s):  
Swarm Intelligence ◽  
Artificial Bee Colony ◽  
Hybrid Swarm ◽  
Bee Colony ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

Research on Swarm Intelligence Algorithm with an Artificial Bee Colony Algorithm for Lot Streaming Problem in Job Shop

2014 ◽  
Vol 951 ◽  
pp. 239-244 ◽  
Author(s):  
Xiao Qiang Xu ◽  
De Ming Lei
Keyword(s):  
Swarm Intelligence ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Job Shop ◽  
Lot Streaming ◽  
Lot Scheduling ◽  
Bee Colony ◽  
Intelligence Algorithm ◽  
Second Period ◽  
Swarm Intelligence Algorithm

The lot streaming (LS) problem in job shop with equal-size sub-lots and intermittent idling is considered. An effective swarm intelligence algorithm with an artificial bee colony (ABC) algorithm is proposed for the minimization of total penalties of tardiness and earliness. In the first period of ABC, the employed bee phase and the onlooker bee phase are both for lot/sub-lot scheduling. In the second period, the LS conditions are determined in the employed bee phase and the lot/sub-lot is scheduled in the onlooker phase. The worst solution of the swarm is replaced with the elite one every few cycles. Computational results show the promising advantage of ABC.

Protecting ECG Signals with Hybrid Swarm Intelligence Algorithm

2021 ◽  
pp. 47-60
Author(s):  
Ayushi Kirar ◽  
Siddharth Bhalerao ◽  
Om Prakash Verma ◽  
Irshad Ahmad Ansari
Keyword(s):  
Swarm Intelligence ◽  
Hybrid Swarm ◽  
Ecg Signals ◽  
Intelligence Algorithm ◽  
Swarm Intelligence Algorithm

scholarly journals Automatic Threshold Selections by exploration and exploitation of optimization algorithm in Record Deduplication

2016 ◽  
Vol 12 (11) ◽  
pp. 4515-4522
Author(s):  
K. Deepa ◽  
C. Vivek ◽  
S.Palanivel Rajan
Keyword(s):  
Genetic Algorithm ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Artificial Bee Colony ◽  
Similarity Function ◽  
Human Intervention ◽  
Exploration And Exploitation ◽  
Bee Colony ◽  
Threshold Setting ◽  
Swarm Intelligence Algorithm

A deduplication process uses similarity function to identify the two entries are duplicate or not by setting the threshold.  This threshold setting is an important issue to achieve more accuracy and it relies more on human intervention. Swarm Intelligence algorithm such as PSO and ABC have been used for automatic detection of threshold to find the duplicate records. Though the algorithms performed well there is still an insufficiency regarding the solution search equation, which is used to generate new candidate solutions based on the information of previous solutions.  The proposed work addressed two problems: first to find the optimal equation using Genetic Algorithm(GA) and next it adopts an modified  Artificial Bee Colony (ABC) to get the optimal threshold to detect the duplicate records more accurately and also it reduces human intervention. CORA dataset is considered to analyze the proposed algorithm.

scholarly journals A hybrid swarm intelligence algorithm for multiuser scheduling in HSDPA

2013 ◽  
Vol 13 (5) ◽  
pp. 2990-2996 ◽  
Author(s):  
Mehmet E. Aydin ◽  
Raymond Kwan ◽  
Cyril Leung ◽  
Carsten Maple ◽  
Jie Zhang
Keyword(s):  
Swarm Intelligence ◽  
Hybrid Swarm ◽  
Intelligence Algorithm ◽  
Multiuser Scheduling ◽  
Swarm Intelligence Algorithm

scholarly journals A New Compound Swarm Intelligence Algorithms for Solving Global Optimization Problems

2013 ◽  
Vol 10 (9) ◽  
pp. 2010-2020
Author(s):  
Ibrahim M. Hezam ◽  
Osama Abdel Raouf ◽  
Mohey M. Hadhoud
Keyword(s):  
Swarm Intelligence ◽  
Firefly Algorithm ◽  
Research Study ◽  
Optimization Problems ◽  
Cuckoo Search ◽  
Hybrid Swarm ◽  
Swarm Optimization ◽  
Intelligence Algorithm ◽  
Swarm Algorithms ◽  
Swarm Intelligence Algorithm

This paper proposes a new hybrid swarm intelligence algorithm that encompasses the feature of three major swarm algorithms. It combines the fast convergence of the Cuckoo Search (CS), the dynamic root change of the Firefly Algorithm (FA), and the continuous position update of the Particle Swarm Optimization (PSO). The Compound Swarm Intelligence Algorithm (CSIA) will be used to solve a set of standard benchmark functions. The research study compares the performance of CSIA with that of CS, FA, and PSO, using the same set of benchmark functions. The comparison aims to test if the performance of CSIA is Competitive to that of the CS, FA, and PSO algorithms denoting the solution results of the benchmark functions.

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