探索蚂蚁群体优化算法：理论与应用全览

需积分: 10 74 浏览量更新于2024-07-18 收藏 4.3MB PDF 举报

《蚁群优化算法》是一本介绍快速发展的蚂蚁群优化（Ant Colony Optimization, ACO）领域的经典著作，由Marco Dorigo和Thomas Stützle合著。本书旨在提供一个全面的视角，从深入剖析ACO的基本理念出发，探讨如何将这种算法广泛应用于各种组合优化问题。作者不仅详细阐述了ACO的核心原理，还涵盖了众多可用的ACO算法及其主要应用场景。书中的主要内容包括： 1. **蚁群行为与数学模型**：作者从自然界的蚂蚁社会行为出发，解释了蚂蚁如何通过信息素（pheromones）进行协作寻找食物源，这为构建计算机算法提供了灵感。他们探讨了信息素的模拟机制以及它在解决复杂问题中的关键作用。 2. **ACO的基本概念**：这部分介绍了ACO算法的工作原理，即如何利用模拟蚂蚁的行为来搜索最优化路径。算法的关键组成部分包括蚂蚁的选择行为、信息素的更新过程以及解空间的探索策略。 3. **算法设计与实现**：书中列举了多种ACO变种，如经典Ant System、Max-Min Ant System、Q-learning Ant System等，每种算法都有其特定的优势和适用场景。作者会详述它们的算法流程和改进策略。 4. **应用领域**：ACO被广泛应用于各种实际问题，如旅行商问题（Traveling Salesman Problem, TSP）、网络路由、调度问题、物流优化、机器学习等领域。书中提供了丰富的案例分析，展示了ACO在解决这些问题上的实际效果。 5. **扩展与未来方向**：随着ACO技术的发展，作者也讨论了该领域的一些前沿研究，如深度学习与ACO的融合、并行化和分布式ACO的挑战以及可能的未来趋势。《蚁群优化算法》是一本既理论性强又实践性突出的书籍，对于想要深入了解这一领域的人来说，无论是研究人员还是工程师，都是极好的学习资源。通过阅读这本书，读者能够掌握蚂蚁行为的数学模拟方法，并将其转化为解决复杂问题的有效工具。

1From Real to Artiﬁcial Ants

Iamlost! Where is the line?!

—A Bug’s Life,Walt Disney, 1998

Ant colonies, and more generally social insect societies, are distributed systems that,

in spite of the simplici ty of their individuals, present a highly structured social orga-

nization. As a result of this organization, ant colonies can accomplish complex tasks

that in some cases far exceed the individual capabilities of a single ant.

The ﬁeld of ‘‘ant algorithms’’ studies models derived from the observation of real

ants’ behavior, and use s these models as a source of inspiration for the design of

novel algorithms for the solution of optimization and distributed control problems.

The main idea is that the self-organizi ng principles which allow the highly coordi-

nated behavior of real ants can be exploited to coordinate populations of artiﬁcial

agents that collaborate to solve computational problems. Several di¤erent aspects of

the behavior of ant colonies have inspired di¤erent kinds of ant algorithms. Ex-

amples are foraging, division of labor, brood sorting, and cooperative transport. In

all these examples, ants coordinate their activities via stigmergy,aform of in direct

communication mediated by modiﬁcations of the environment. For example, a for-

aging ant deposits a chemical on the ground which increases the probability that

other ants will follow the same path. Biologists have shown that many colony-level

behaviors observed in social insects can be explained via rather simple models in

which onl y stigmergic communication is present. In other words, biologists have

shown that it is often su‰cient to consider stigmergic, indirect communication to

explain how social insects can achieve self-organization. The idea behind ant algo-

rithms is then to use a form of artiﬁcial stigmergy to coordinate societies of artiﬁcial

agents.

One of the most successful examples of ant algorithms is known as ‘‘ant colony

optimization,’’ or ACO, and is the subject of this book. ACO is inspired by the for-

aging behavior of ant colonies, and targets discrete optimization problems. This in-

troductory chapter describes how real ants have inspired the deﬁnition of artiﬁcial

ants that can solve discrete optimization problems.

1.1 Ants’ Foraging Behavior and Optimization

The visual perceptive faculty of many ant species is only rudimentarily developed

and there are ant species that are completely blind. In fact, an important insight of

early research on ants’ behavior was that most of the communication among indi-

viduals, or between individuals and the environment, is based on the use of chemicals

produced by the ants. These chemicals are called pheromones. This is di¤erent from,

Download at Boykma.Com

for example, what happens in humans and in other higher species, whose most im-

portant senses are visual or acoustic. Particularly important for the social life of some

ant species is the trail pheromone. Trail pheromone is a speciﬁc type of pheromone

that some ant species, such as Lasius niger or the Argentine ant Iridomyrmex humilis

(Goss, Aron, Deneubourg, & Pasteels, 1989), use for marking paths on the ground,

for example, pat hs from food sources to the nest. By sensing pheromone trails for-

agers can follow the path to food discovered by other ants. This collective trail-laying

and trail-following behavior whereby an ant is inﬂuenced by a chemical trail left by

other ants is the inspiring source of ACO.

1.1.1 Double Bridge Experiments

The foraging behavior of many ant species, as, for example, I. humilis (Goss et al.,

1989), Linepithema humile, and Lasius niger (Bonabeau et al., 1997), is based on in-

direct communication mediated by pheromones. While walking from food sources to

the nest and vice versa, ants deposit pheromones on the ground, forming in this way

a pheromone trail. Ants can smell the pheromone and they tend to choose, proba-

bilistically, paths marked by strong pheromone concentrations.

The pheromone trail-laying and -following behavior of some ant species has been

investigated in controlled experiments by several researchers. One particularly bril-

liant experiment was designed and run by Deneubourg and colleagues (Deneubourg,

Aron, Goss, & Pasteels, 1990; Goss et al., 1989), who used a double bridge connect-

ing a nest of ants of the Argentine ant species I. humilis and a food source. They ran

experiments varying the ratio r ¼ l

between the length of the two branches of the

double bridge, where l

was the length of the longer branch and l

the length of the

shorter one.

In the ﬁrst experiment the bridge had two branches of equal length (r ¼ 1; see

ﬁgure 1.1a). At the start, ants were left free to move between the nest and the food

source and the percentage of ants that chose one or the other of the two branches

were observed over time. The outcome was that (see also ﬁgure 1.2a), although in the

initial phase random choices occurred, eventually all the ants used the same branch.

This result can be explained as follows. When a trial starts there is no pheromone on

the two branches. Hence, the ants do not have a preference and they select with the

same probability any of the branches. Yet, because of random ﬂuctuations, a few

more ants will select one branch over the other. Because ants deposit pheromone

while walking, a larger number of ants on a branch results in a larger amount of

pheromone on that branch; this larger amount of pheromone in turn stimulates more

ants to choose that branch again, and so on until ﬁnally the ants converge to one

2 Chapter 1 From Real to Artiﬁcial Ants

Download at Boykma.Com

single path. This autocatalytic or positive feedback process is, in fact, an example of

a self-organizing behavior of the ants: a macroscopic pattern (corresponding to the

convergence toward one branch) emerges out of processes and interactions taking

place at a ‘‘microscopic’’ level (Camazine, Deneubourg, Fra nks, Sneyd, Thera ulaz,

& Bonabeau, 2001; Hak en, 1983; Nicolis & Prigogine, 1977). In our case the con-

vergence of the ants’ paths to one branch represents the macroscopic collective be-

havior, which can be explained by the microscopic activity of the ants, that is, by the

local interactions among the individuals of the colony. It is also an example of stig-

mergic communication (for a deﬁnition of stigmergy, see section 1.4): ants coordinate

their activities, exploiting indirect communication mediated by modiﬁcations of the

environment in which they move.

In the second experiment, the length ratio between the two branches was set to

r ¼ 2(Goss et al., 1989), so that the long branch was twice as long as the short one

(ﬁgure 1.1b shows the experimental setup). In this case, in most of the trials, after

some time all the ants chose to use only the short branch (see ﬁgure 1.2b). As in the

ﬁrst experiment, ants leave the nest to explore the environment and arrive at a deci-

sion point where they have to choose one of the two branches. Because the two

branches initially appear identical to the ants, they choose randomly. Therefore, it

canbeexpected that, on average, half of the ants choose the short branch and the

other half the long branch, although stochastic oscillations may occasionally favor

one branch over the other. However, this experimental setup presents a remarkable

di¤erence with respect to the previous one: because one branch is shorter than the

other (see ﬁgure 1.1b), the ants choosing the short branch are the ﬁrst to reach the

food and to start their return to the nest. But then, when they must make a decision

between the short and the long branch, the higher level of pheromone on the short

branch will bias their decision in its favor. Therefore, pheromone starts to accumu-

late faster on the short branch, which will eventually be used by all the ants because

of the autocatalytic process described previously. When compared to the experiment

with the two bra nches of equal length, the inﬂuence of initial random ﬂuctuations

is much reduced, and stigmergy, autocatalysis, and di¤erential path length are the

main mechanisms at work. Interestingly, it can be observed that, even when the long

branch is twice as long as the short one, not all the ants use the short branch, but a

small percentage may take the longer one. This may be interpreted as a type of ‘‘path

exploration.’’

It is also interesting to see what happens when the ant colony is o¤ered, after

convergence, a new shorter connection between the nest and the food. This situation

was studied in an additional experiment in which initially only the long branch was

4 Chapter 1 From Real to Artiﬁcial Ants

Download at Boykma.Com

o¤ered to the colony and after 30 minutes the short branch was added (see ﬁgure

1.3). In this case, the short branch was only selected sporadically and the colony was

trapped on the long branch. This can be explained by the high pheromone concen-

tration on the long branch and by the slow evaporation of pheromone. In fact, the

great majority of ants choose the long branch because of its high pherom one con-

centration, and this autocatalytic behavior continues to reinforce the long branch,

even if a shorter one appears. Pheromone evaporation, which could favor explora-

tion of new paths, is too slow: the lifetime of the pheromone is comparable to the

duration of a trial (Goss et al., 1989), which means that the pheromone evaporates

too slowly to allow the ant colony to ‘‘forget’’ the suboptimal pat h to which they

converged so that the new and shorter one can be discovered and ‘‘learned.’’

1.1.2 A Stochastic Model

Deneubourg and colleag ues (Deneubourg et al., 1990; Goss et al., 1989) proposed a

simple stochastic model that adequately describes the dynamics of the ant colony as

observed in the double bridge experiment. In this model, c ants per second cross the

bridge in each direction at a constant speed of v cm/s, depositing one unit of phero-

mone on the branch. Given the lengths l

and l

(in cm) of the short and of the long

branch, an ant choosing the short branch will traverse it in t

¼ l

=v seconds, while

an ant choosing the long branch will use r  t

seconds, where r ¼ l

The probability p

ðtÞ that an ant arriving at decision point i A f1; 2g (see ﬁgure

1.1b) selects branch a A fs; lg,where s and l denote th e short and long branch re-

spectively, at instant t is set to be a function of the total amount of pheromone j

ðtÞ

Nest Food

30 min

Nest Food

(a)

100

0-20 20-40 40-60 60-80 80-100

% of traffic on the short branch

(b)

% of experiments

Figure 1.3

In this experiment initially only the long branch was o¤ered to the colony. After 30 minutes, when a stable

pheromone trail has formed on the only available branch, a new shorter branch is added. (a) The initial

experimental setup and the new situation after 30 minutes, when the short branch was added. (b) In the

great majority of the experiments, once the short branch is added the ants continue to use the long branch.

1.1 Ants’ Foraging Behavior and Optimization 5

Download at Boykma.Com

剩余320页未读，继续阅读

guthrum

粉丝: 0
资源: 5

探索蚂蚁群体优化算法：理论与应用全览

蚁群算法的函数优化matlab

蚁群优化（多里戈）

蚁群算法matlab完整代码

蚁群优化算法原理及Matlab编程实现.rar_9PO_优化 蚁群算法_蚁群优化_蚁群优化算法_蚁群算法优化

蚁群优化算法.rar_python 优化_python蚁群_蚁群优化算法_蚁群算法python

蚁群优化算法蚁群优化算法.pdf

蚁群算法.rar_fewpoh_蚁群优化_蚁群优化算法_蚁群算法_蚁群算法优化

ACATSP.rar_优化 蚁群 TSP_蚁群优化_蚁群优化算法_蚁群算法优化

蚁群算法_粒子群算法_粒子群蚁群_蚁群优化算法_matlab_蚁群优化

包含密文蚁群优化算法和明文蚁群优化算法，Private ACO.zip

最新资源

蚁群优化算法原理及Matlab编程实现.rar_9PO_优化蚁群算法_蚁群优化_蚁群优化算法_蚁群算法优化

ACATSP.rar_优化蚁群 TSP_蚁群优化_蚁群优化算法_蚁群算法优化