python实现人工蜂群算法实现人工蜂群算法
主要介绍了python如何实现人工蜂群算法，帮助大家更好的利用python进行数据分析，感兴趣的朋友可以了解
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ABSIndividual.py
import numpy as np
import ObjFunction
class ABSIndividual:
'''
individual of artificial bee swarm algorithm
'''
def __init__(self, vardim, bound):
'''
vardim: dimension of variables
bound: boundaries of variables
'''
self.vardim = vardim
self.bound = bound
self.fitness = 0.
self.trials = 0
def generate(self):
'''
generate a random chromsome for artificial bee swarm algorithm
'''
len = self.vardim
rnd = np.random.random(size=len)
self.chrom = np.zeros(len)
for i in xrange(0, len):
self.chrom[i] = self.bound[0, i] + \
(self.bound[1, i] - self.bound[0, i]) * rnd[i]
def calculateFitness(self):
'''
calculate the fitness of the chromsome
'''
self.fitness = ObjFunction.GrieFunc(
self.vardim, self.chrom, self.bound)
ABS.py
import numpy as np
from ABSIndividual import ABSIndividual
import random
import copy
import matplotlib.pyplot as plt
class ArtificialBeeSwarm:
'''
the class for artificial bee swarm algorithm
'''
def __init__(self, sizepop, vardim, bound, MAXGEN, params):
'''
sizepop: population sizepop
vardim: dimension of variables
bound: boundaries of variables
MAXGEN: termination condition
params: algorithm required parameters, it is a list which is consisting of[trailLimit, C]
'''
self.sizepop = sizepop
self.vardim = vardim
self.bound = bound
self.foodSource = self.sizepop / 2
self.MAXGEN = MAXGEN
self.params = params
self.population = []
self.fitness = np.zeros((self.sizepop, 1))
self.trace = np.zeros((self.MAXGEN, 2))
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