This chapter provides a brief review of bio-inspiring algorithms and their importance to solve complex optimization problems in engineering. In addition, bio-inspired algorithms used throughout the rest of the
Moreover, description of algorithms in more general terms and the most successful variants of these algorithms are provided. Finally, a brief introduction to other bio-inspired computation algorithms such as glowworm swarm optimization (GSM), bacteria foraging optimization (BFO), bat-inspired algorithm...
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The current study investigates the use of several bio-inspired algorithms for identifying and forecasting problems involving lung cancer, such as genetic algorithms, particle swarm optimization, artificial neural networks, and ant colony optimization. These algorithms mimic the behaviour of natural systems ...
These systems are based on gradient-based algorithms that require high computing power and memory storage as well as a large amount of labeled training data. Although these systems are highly effective, their biological plausibility is limited9, and they can be power-hungry10. Hence, there is a...
This paper presents state-of-art of nine of recent bio-inspired algorithms, gap analysis, and its applications namely; Genetic Bee Colony (GBC) Algorithm, Fish Swarm Algorithm (FSA), Cat Swarm Optimization (CSO), Whale Optimization Algorithm (WOA), Artificial Algae Algorithm (AAA), Elephant ...
图书Bio-Inspired Algorithms for the Vehicle Routing Problem 介绍、书评、论坛及推荐
This repository contains the Matlab source code to implemet the optimization of an optical taper (modeled using Comsol Multiphysics). The algorithms used were Genetic Algoritm and Particle Swarm Optimization 点赞(0) 踩踩(0) 反馈 所需:1 积分 电信网络下载 ...
To enhance the efficiency of the disease diagnosis system, various bio-inspired algorithms are presented by researchers [6]. Bio-inspired algorithms are also called as nature-inspired computation methods or nature-motivated techniques or metaheuristic techniques or nature-motivated optimization (NMO) ...
Firstly, statistically finding the optimal probabilistic location of three DG units under load uncertainties in IEEE 33-bus and IEEE 69-bus radial distribution system based on Monte Carlo simulation integrated with different bio-inspired algorithms. Secondly, comparing between the performances of four ...