自适应分级人工免疫系统：一种RFID阅读器防撞优化策略

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"这篇研究论文探讨了自适应分级人工免疫系统(Adaptive Hierarchical Artificial Immune System, AHAIS)及其在RFID阅读器防撞中的应用。文章着重介绍了AHAIS如何解决复杂的全局优化问题，并通过构建具有顶层和底层级别的层次结构来组织抗体群体，其中高亲和力的抗体成为顶层精英抗体，低亲和力的则成为底层普通抗体。该系统利用精英学习策略，使得优秀抗体得以保留，从而实现自我学习和优化。" 在RFID技术中，阅读器与标签之间的通信可能存在冲突，即所谓的“阅读器碰撞”问题，这会导致数据传输效率下降和系统的不稳定。传统的防撞算法如ALOHA或CSMA/CD等可能无法有效处理这种复杂环境下的冲突。因此，研究人员提出了一种基于人工免疫系统的新型解决方案——AHAIS。人工免疫系统(AIS)是受到生物免疫机制启发的一种计算模型，它能够进行高效搜索和优化。在AHAIS中，模仿生物体内的免疫反应，通过选择、变异和克隆等机制来寻找最优解。论文提出的自适应分级策略，将抗体群体分为精英层和普通层，这种分层结构有助于快速发现和保持优质解决方案，同时通过动态调整，可以应对不断变化的RFID环境。精英学习策略是AHAIS的核心特点之一，它确保了在优化过程中最优秀的抗体（代表解决方案）能够被保留下来，从而避免过早收敛到局部最优解。此外，AHAIS还引入了自我学习机制，允许系统根据环境反馈自我调整，提高其适应性和解决问题的能力。在RFID阅读器防撞应用中，AHAIS能够有效地分配阅读器的工作时间或频率，以避免多个阅读器同时识别同一标签，减少冲突。通过这种方式，系统性能得到提升，数据传输的可靠性和效率显著增强。这篇研究论文详细阐述了AHAIS的原理、设计和实现，并将其成功应用于RFID阅读器防撞问题，展示了人工免疫系统在解决复杂工程问题上的潜力和优势。这一工作对理解生物免疫系统在计算领域的应用以及优化RFID网络的性能具有重要意义。

122 Z. Li et al. / Applied Soft Computing 21 (2014) 119–138

Fig. 2. Dynamic level updating strategy of the proposed AHAIS optimization.

3.4. Suppression operator

In the proposed AHAIS optimization, a concentration-based sup-

pressor is employed, where the threshold Th

can be determined

=  ×(max{D

|i /= j}−min{D

|i /= j}), (6)

where the controlling coefﬁcient  ∈(0, 1). This is aimed at sup-

pressing those similar candidate antibodies.

3.5. Dynamic level updating strategy

After learning from TLEA, some candidate antibodies in BLCA

will probably become better in afﬁnity than those in TLEA. Thus,

it is necessary to dynamically update the elements of candidate

antibodies in both BLCA and TLEA. We design a dynamic level updat-

ing strategy which allows those candidate antibodies with higher

afﬁnity in BLCA to transfer into TLEA and allows those with lower

afﬁnity in TLEA to return to BLCA. As shown in Fig. 2, the detailed

procedures are explained as follows.

Step 1. Sort the candidate antibodies in BLCA by their afﬁnity in a

descending order.

Step 2. Make the ﬁrst p (p = N

×b, b ∈(0, 1)) candidate antibodies

in BLCA transfer into TLEA.

Step 3. Sort the candidate antibodies in TLEA by their afﬁnity in a

descending order.

Step 4. Make the last p candidate antibodies in TLEA retrograde

into BLCA.

The ﬂowchart of the proposed AHAIS optimization is shown in

Fig. 3. The revised or redesigned operators are placed in the red

dashed-line boxes, respectively. In the initialization, the randomly

produced candidate antibodies are separated into TLEA and BLCA

based on their afﬁnity. For TLEA, the candidate antibodies experi-

ence the afﬁnity-based cloning and the afﬁnity-based self-learning

mutation where the searching step length is adjusted adaptively by

a well-designed mechanism. On the other hand, for BLCA, the candi-

date antibodies undergo the elitist-learning mutation from the best

candidate antibody in TLEA after the common clone process. After

the evolutionary process, if the level updating condition is met,

the level updating strategy is triggered. Next, if the suppression

condition is met, the concentration-based suppression operator is

excited for the candidate antibodies in BLCA. Repeat this process

until it is terminated.

For the sake of clear explanation, the pseudocode of the pro-

posed AHAIS optimization is shown in Fig. 4.

4. Numerical simulations and results

In this section, a series of numerical simulations are arranged

to analyze the sensitivity of the key parameters and to examine

the solution quality and the convergence velocity of the proposed

AHAIS optimization by comparisons with DE, PSO, opt-aiNet and IA-

AIS. Furthermore, the proposed AHAIS optimization is also applied

to schedule the communication resource of an RFID reader collision

avoidance problem.

4.1. Performance experiments

4.1.1. Benchmark functions

Eight classical benchmark functions are chosen from [25] to

examine the proposed AHAIS optimization, which are listed in

Table 1. The Sphere function, the weighted Sphere function and the

Schwefel’s Problem 1.2 are simple unimodal functions and easy to

solve. The Griewank function, the Rastrigin function and the gener-

alized Schwefel’s Problem 2.26 are multimodal functions, and they

are difﬁcult to reach the global optimum. The Rosenbrock function,

with a narrow valley from the perceived local optima to the global

optimum, can be treated as a multimodal function. The Ackley func-

tion, whose global optimum is on the bound, can be also regarded

as a multimodal function.

4.1.2. Parameter sensitivity analysis

In the proposed AHAIS optimization, three parameters, i.e., the

antibody population scale of TLEA (p

Elitist

), the controlling coefﬁ-

cient for suppression () and the percentage of updating antibodies

(b), need to be tuned. In this subsection, eight benchmark functions

in 10D are used to discuss the impact of these three parameters. All

the numerical simulations are run for 25 replications. The antibody

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