Systems, Man & Cybernetics - January 2016 - 29

Multicore
Architecture

Dynamic
Mechanism

Core 1
Core 2
Core n

Dynamic
Mechanism

Presentation
Pop 1
AIN-Based
Reinforcement
Learning for
Pbest

Pop 1

Memory
and Enhanced

AIN-Based
Reinforcement
Learning for
Pbest

Pop 2

Pop 2

Migration
Pop M

Pop M

Dynamic
Mechanism
Dynamic
Mechanism

ICLS

Dynamic
Mechanism

Dynamic
Mechanism

Figure 2. the model of PICDPSO-m.

m = m max -(m max - m min) t T where m max and m min are the
upper a nd lower boundar ies of m (fixed m max = 0.5 ,
m min = 0.01 in this article), which is derived by (9) as follows:
Gfi =

i
i
(t - k) - F best
(t)
F best
,
i
F best (t) + f

(9)

i
(t) (with i > 0) indicates
where f is a smooth factor, F best
that the best fitness of the ith particle in tth generation,
k is an integer and set to be two according to the experiment statistics.

Memory Enhancement Using Immune
Comprehensive Learning Strategy
The best individuals have an important role in promoting evolutionary advancement of the population. Individuals possessing the best fitness are selected from
subpopulations and are conserved in the memory and
regarded as antibody set A = {A1d, A2d,..., AKd} (K being
the memory size) in artificial immune systems (AISs). A
novel ICLS is designed to update the memory so that it
will explore better sea rch regions, a nd then the
improved individual will be migrated into the subswarms where it will replace the worst individual. The
ICLS is given as follows:
Alid = A id + h * (A {[d] - A id),

where T is the maximum evolution generation and t is the
current generation, coefficient b is a formal parameter
(usually fixed to two), r is randomly generated and uniformly distributed in (0, 1).

Pbests Reinforcement Learning
with an Artificial Immune Network
To strengthen the Pbest individual's convergence speed,
inspired by the phenomenon that immune cells in AIS
can dynamically interact with each other and collectively accomplish the assigned task [13], 20% of the most
inferior Pbest particles are selected randomly to be
advanced toward the gBest position from a different
subpopulation by using an artificial immune-network
(AIN)-based reinforcement learning mechanism. Figure 3
shows the principium of the AIN. The AIN-based reinforcement learning for Pbest can be derived as follows:
Pbest id(j) = Pbest id(j) - r1 . (Pbest id(j) - gBest Pr(d))
+ r2 . cauchy . Pbest id(j),

Inhibiting

(10)

Ag

where { is the randomly selected antibody, and where
{ = 6rand * K @ ; in addition, { simultaneously satisfies
the term A {[d] - A id > d, where d within (0, 1); in this
case, the redundant antibodies will be suppressed. Where
h is a learning factor and given as follows:
b

h = 1 - r [1 - (t/T)] ,

(11)

(12)

1
Activating

2
3

Figure 3. a schematic diagram for dynamic immune

networks.

Ja nu a r y 2016

IEEE SyStEmS, man, & CybErnEtICS magazInE

29



Table of Contents for the Digital Edition of Systems, Man & Cybernetics - January 2016

Systems, Man & Cybernetics - January 2016 - Cover1
Systems, Man & Cybernetics - January 2016 - Cover2
Systems, Man & Cybernetics - January 2016 - 1
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Systems, Man & Cybernetics - January 2016 - Cover3
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