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Opposition based learning: A literature review
Opposition-based Learning (OBL) is a new concept in machine learning, inspired from the
opposite relationship among entities. In 2005, for the first time the concept of opposition was …
opposite relationship among entities. In 2005, for the first time the concept of opposition was …
Enhanced moth-flame optimizer with quasi-reflection and refraction learning with application to image segmentation and medical diagnosis
J **a, Z Cai, AA Heidari, Y Ye, H Chen… - Current …, 2023 - benthamdirect.com
Background: Moth-flame optimization will meet the premature and stagnation phenomenon
when encountering difficult optimization tasks. Objective: This paper presented a quasi …
when encountering difficult optimization tasks. Objective: This paper presented a quasi …
A novel hybrid bat algorithm for solving continuous optimization problems
Abstract The Bat Algorithm (BA), which is a global optimization method, performs poorly on
complex continuous optimization problems due to BA's disadvantages such as the …
complex continuous optimization problems due to BA's disadvantages such as the …
Chaotic fitness-dependent quasi-reflected Aquila optimizer for superpixel based white blood cell segmentation
The crisp partitional clustering techniques like K-Means (KM) are an efficient image
segmentation algorithm. However, the foremost concern with crisp partitional clustering …
segmentation algorithm. However, the foremost concern with crisp partitional clustering …
Improved chemical reaction optimization with fitness-based quasi-reflection method for scheduling in hybrid cloud-fog environment
With the advent of Internet of Things (IoT) applications, smart IoT devices are ubiquitous.
Executing these devices on cloud data centers can lead to network congestion and …
Executing these devices on cloud data centers can lead to network congestion and …
An enhanced scatter search with combined opposition-based learning for parameter estimation in large-scale kinetic models of biochemical systems
An enhanced scatter search (eSS) with combined opposition-based learning algorithm is
proposed to solve large-scale parameter estimation in kinetic models of biochemical …
proposed to solve large-scale parameter estimation in kinetic models of biochemical …
Optimal design of large-scale dome truss structures with multiple frequency constraints using success-history based adaptive differential evolution algorithm
The success-history based adaptive differential evolution (SHADE) algorithm is an efficient
modified version of the differential evolution (DE) algorithm, and it has been successfully …
modified version of the differential evolution (DE) algorithm, and it has been successfully …
Intelligent salp swarm scheduler with fitness based quasi-reflection method for scientific workflows in hybrid cloud-fog environment
The burgeoning volume of data from the IoT applications and intelligent devices processed
on the cloud data centers can lead to network congestion and transmission delay …
on the cloud data centers can lead to network congestion and transmission delay …
[ספר][B] Evolutionary computation with biogeography-based optimization
H Ma, D Simon - 2017 - books.google.com
Evolutionary computation algorithms are employed to minimize functions with large number
of variables. Biogeography-based optimization (BBO) is an optimization algorithm that is …
of variables. Biogeography-based optimization (BBO) is an optimization algorithm that is …
Success-history based adaptive differential evolution algorithm for discrete structural optimization
Discrete structural optimization is generally regarded as a complicated optimization problem
due to the presence of discrete variables. However, since metaheuristic algorithms do not …
due to the presence of discrete variables. However, since metaheuristic algorithms do not …