文件列表:

Feature Selection and Classification (0, 2019-03-09)
Feature Selection and Classification\Literatures (0, 2019-03-09)
Feature Selection and Classification\Literatures\A Comparative Analysis of Swarm Intelligence Techniques for Feature Selection in Cancer Classification.pdf (1924257, 2019-03-09)
Feature Selection and Classification\Literatures\A Survey- Spider Monkey Optimization Algorithm.pdf (181437, 2019-03-09)
Feature Selection and Classification\Literatures\ABC (0, 2019-03-09)
Feature Selection and Classification\Literatures\ABC\Classification of DNA microarrays using artificial neural networks and ABC algorithm.pdf (232778, 2019-03-09)
Feature Selection and Classification\Literatures\ABC\Feature selection software development using Artificial Bee Colony on DNA microarray data.pdf (250799, 2019-03-09)
Feature Selection and Classification\Literatures\ABC\Gene selection for cancer classification with the help of bees.pdf (1313027, 2019-03-09)
Feature Selection and Classification\Literatures\ACO (0, 2019-03-09)
Feature Selection and Classification\Literatures\ACO\A Hybrid Approach from ACO and KNN for Classifying Datasets Using Selected Features.pdf (765885, 2019-03-09)
Feature Selection and Classification\Literatures\ACO\A hybrid gene selection approach for microarray data classification using cellular learning automata and ant colony optimization.pdf (776430, 2019-03-09)
Feature Selection and Classification\Literatures\ACO\Gene selection for microarray data classification using a novel ant colony optimization.pdf (712417, 2019-03-09)
Feature Selection and Classification\Literatures\ACO\Pattern Matching based Classification using Ant Colony Optimization based Feature Selection.pdf (228279, 2019-03-09)
Feature Selection and Classification\Literatures\Others (0, 2019-03-09)
Feature Selection and Classification\Literatures\Others\3-Spider_Monkey_Optimization.pdf (1163559, 2019-03-09)
Feature Selection and Classification\Literatures\Others\A Survey- Spider Monkey Optimization Algorithm.pdf (181437, 2019-03-09)
Feature Selection and Classification\Literatures\Others\Ageist Spider Monkey Optimization algorithm.pdf (2232697, 2019-03-09)
Feature Selection and Classification\Literatures\Others\Binary grey wolf optimization approaches for feature selection.pdf (1577219, 2019-03-09)
Feature Selection and Classification\Literatures\Others\Bioinspired_Intelligent_Algorithm_and_Its_Applicat.pdf (2327895, 2019-03-09)
Feature Selection and Classification\Literatures\Others\Designing fuzzy rule base using Spider Monkey Optimization Algorithm in cooperative framework.pdf (576399, 2019-03-09)
Feature Selection and Classification\Literatures\Others\Hybrid Whale Optimization Algorithm with simulated annealing for feature selection.pdf (1254359, 2019-03-09)
Feature Selection and Classification\Literatures\Others\Hybrid monkey algorithm with krill herd algorithm optimization for feature selection.pdf (432966, 2019-03-09)
Feature Selection and Classification\Literatures\Others\Modified Grey Wolf Optimizer for Global Engineering Optimization.pdf (2030885, 2019-03-09)
Feature Selection and Classification\Literatures\Others\Modified Monkey Optimization Algorithm for Solving Optimal Reactive Power Dispatch Problem.pdf (155063, 2019-03-09)
Feature Selection and Classification\Literatures\Others\Performance analysis of feature extraction and selection of region of interest by segmentation in mammogram images between the existing metaheuristic algorithms and Monkey Search Optimization (MSO).pdf (1172865, 2019-03-09)
Feature Selection and Classification\Literatures\PSO (0, 2019-03-09)
Feature Selection and Classification\Literatures\PSO\Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee Colony.pdf (1118376, 2019-03-09)
Feature Selection and Classification\Literatures\PSO\Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine.pdf (1423497, 2019-03-09)
Feature Selection and Classification\Literatures\PSO\Gene selection approach based on improved swarm intelligent optimisation algorithm for tumour classification.pdf (635593, 2019-03-09)
Feature Selection and Classification\Literatures\Review on the Usage of Swarm Intelligence in Gene Expression Data.pdf (229940, 2019-03-09)
SI_Optimization (0, 2019-03-09)
SI_Optimization\1.Grey wolf optimizer.pdf (1753133, 2019-03-09)
SI_Optimization\2.Whale optimization algorithm.pdf (2092842, 2019-03-09)
SI_Optimization\3.Social Spider Optimization .pdf (1343169, 2019-03-09)
SI_Optimization\4.Spider Monkey Optimization.pdf (801180, 2019-03-09)
SI_Optimization\5.Moth search algorithm.pdf (800193, 2019-03-09)
SI_Optimization\6.Monarch Butterfly Optimization.pdf (440319, 2019-03-09)
... ...

# Swarm Intelligence in Bioinformatics #### ● Table of Content - Traditionals - [Particle Swarm Optimization](#-particle-swarm-optimization-pso) - [Ant Colony Optimization](#-ant-colony-optimization-aco) - [Artificial Bee Colony](#-artificial-bee-colony-abc) - New Additions - [Grey Wolf Optimization](#-grey-wolf-optimization-gwo) - [Whale Optimization Algorithm](#-whale-optimization-algorithm-woa) - [Social Spider Optimization](#-social-spider-optimization-sso) - [Spider Monkey Optimization](#-spider-monkey-optimization-smo) - [Moth Search Algorithm](#-moth-search-algorithm-msa) - [Monarch Butterfly Optimization](#-monarch-butterfly-optimization-mbo)

#### ● Particle Swarm Optimization (PSO) ``` Algorithm 1: Particle Swarm Optimization ```

#### ● Ant Colony Optimization (ACO) ``` Algorithm 2: Ant Colony Optimization ```

#### ● Artificial Bee Colony (ABC) Artificial Bee Colony was first proposed by Karaboga in 2005. It is an optimization algorithm inspired by the intelligent foraging behavior of a honey bee swarm for finding an optimal solution. Three essential components of forage selection are: 1) food sources: places with a high amount of nectar 2) employed foragers: employed bees exploit the food source, calculate the nector amount, and carry the optimal path information back to hive. 3) unemployed foragers: unemployed bees consist of two groups of bees – onlookers and scouts. Onlookers wait in the hive for the information that is shared by the employed bees. Scout bees are translated from employed bees and always continues seeking new food sources near the hive.
Comparing Artificial Bee Colony algorithm with Particle Swarm Optimization and Ant Colony Optimization methods, it carries the advantages of fast convergence, high flexibility, and strong robustness. ``` Algorithm 3: Artificial Bee Colony 1: Initialize food sources 2: while termination criteria is not met do 3: for each employed bee 4: Produce new solution 5: Calculate the fitness value 6: Apply greedy selection 7: Calculate the probability value 8: end 9: for each onlooker bee 10: Select a solution 11: Produce new solution 12: Calculate the fitness value 13: Apply greedy selection 14: end 15: if an abandoned solution for the scout exists then 16: Replace it with a new solution at random 17: end 18: Register the best solution 19: end ```

#### ● Grey Wolf Optimization (GWO) Reference: Grey Wolf Optimizer, Advances in Engineering Software, Volume 69, March 2014, Pages 46-61, DOI: http://dx.doi.org/10.1016/j.advengsoft.2013.12.007 ``` Algorithm 4: Grey Wolf Optimization Algorithm Initialize the grey wolves population Xi(i = 1; 2; :::; n) Initialize a, A and C 1: Calculate the fitness of each search agent 2: Xa = the best search agent 3: Xb = the second best search agent 4: Xc = the third best search agent 5: while t < Max number of iterations do 6: for each search agent do 7: Update the position of the current search agent by Eq. (1) 8: end 9: Update a, A, and C 10: Calculate the fitness of all search agents 11: Update Xa, Xa and Xc 12: t = t + 1 13: end 14: return Xa ```

#### ● Whale Optimization Algorithm (WOA) Reference: The Whale Optimization Algorithm, Advances in Engineering Software, in press, 2016. DOI: http://dx.doi.org/10.1016/j.advengsoft.2016.01.008 ``` Algorithm 5: Whale Optimization Algorithm ```

#### ● Social Spider Optimization (SSO) Reference: E. Cuevas et al. Swarm optimization algorithm inspired in the behavior of the social-spider, Expert Systems with Applications, 40 (16), (2013), pp. 6374-6384. DOI: http://arxiv.org/abs/1406.3282 ``` Algorithm 6: Social Spider Optimization ```

#### ● Spider Monkey Optimization (SMO) Spider Monkey Optimization is inspired by fission-fusion social foraging behavior of Spider Monkeys. It can be applied in various areas such as remote sensing image classification, wireless protocol design, and electromagnetics and antenna optimization. The SMO algorithm follows the properties of self-organization and division of labor for obtainning intelligent swarming behaviors. Spider monkeys determine their positions by learning from self experience, local leaders and a global leader. It provides positive feedback of self-organization. Members of each group are spread to multiple directions for seeking a food source. If the global leader gets trapped, the group will divide into smaller subgroups and continue repeating the previous steps. Local and global leaders’ update their decisions according to negative feedback from the local leader limit and global leader limit. This process is known as a division of labor property. The SMO algorithm consists of 6 phases: 1) Local Leader phase (LLP) 2) Global Leader phase (GLP) 3) Local Leader Learning phase (LLL) 4) Global Leader Learning phase (GLL) 5) Local Leader Decision phase (LLD) 6) Global Leader Decision phase (GLD)
``` Algorithm 7: Spider Monkey Optimization ```

#### ● Moth Search Algorithm (MSA) Reference: G. Wang. "Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems." Memetic Computing (2016): 1-14. ``` Algorithm 8: Moth Search Algorithm ```

#### ● Monarch Butterfly Optimization (MBO) Reference: G. Wang, X. Zhao, and S. Deb. "A novel monarch butterfly optimization with greedy strategy and self-adaptive." In Soft Computing and Machine Intelligence (ISCMI), 2015 Second International Conference on, pp. 45-50. IEEE, 2015. ``` Algorithm 9: Monarch Butterfly Optimization ```

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