Load balancing energy power plants with high-performance data analytics (HPDA) using machine learning (ML)

Introduction Part I Challenges HPC Initiatives EuroHPC Vega &,Deploying DAPHNE CI DIFFERENTIAL EVOLUTION HTS Conclusion
Superpower for the power grid
Webinar for companies and research institutions from the power-grid sector
29-30 March 2023 through Zoom
Organised by EuroCC Austria and VSC Research Center (TU Wien, Vienna University of Technology)
Load Balancing
Energy Power Plants with
High-Performance Data
Analytics (HPDA)
using Machine Learning (ML)
Aleš Zamuda <ales.zamuda@um.si>
Aleš Zamuda 7@aleszamuda Load balancing energy power plants w/ HPDA ML, 30 March 2023 1/ 71

Introduction & Outline: Aims of this Talk are:
1 (15 minutes) Part I: Backgrounds
• Background A: HPC (3 subparts),
• Background B: ML workloads
2 (15 minutes) Part II: Hydro and Thermal Powerplants
Grid Scheduling (3 subparts)
3 (1 minute) Part III: Conclusion, Appendix
4 (Other) Discussions, Misc
Real examples: science and HPC

Introduction: Overview (Focus, Use, Scope)
• This series focuses on speeding
up of vectorized
benchmarking, which
• includes optimization
algorithms [1].
• These algorithms are used within
Machine Learning (ML)
workloads together with
benchmarking
• in order to compute the
performance of instances of
such algorithms
on a whole benchmark [2].
• Such performance measure
provides a more general
evaluation of an algorithm’s
applicability,
• i.e. an intelligence generality.
• However, the computational
time for whole benchmark
evaluation extends signiﬁcantly
• compared to a single instance
evaluation
• and hence speeding up might
be required
• under time-constrained
conditions, especially when
e.g. used for robotic deep sea
underwater missions [3] or
power grid scheduling [4]
Real examples: science and HPC
[1] A. Zamuda, E. Lloret, Optimizing Data-Driven Models for Summarization as Parallel Tasks. Journal of Computational Science 42, 101101 (2020).
[2] A. Zamuda, J. Brest, Self-adaptive control parameters’ randomization frequency and propagations in differential evolution. Swarm and Evolutionary
Computation 25C, 72-99 (2015).
[3] A. Zamuda, J. D. Hernández Sosa, Success history applied to expert system for underwater glider path planning using differential evolution. Expert Systems
with Applications 119, 155-170 (2019).
[4] A. Glotić, A. Zamuda. Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution.Applied Energy,
1 March 2015, vol. 141, pp. 42-56. DOI: 10.1016/j.apenergy.2014.12.020

Introduction: Vectorized Benchmarking
Opportunities
A closer observation of execution
times for workloads processed in [2] is
provided in Fig. 1, where it is seen that
the execution time (color of the
patches) changes for different
benchmark executions.
Fig. 1: Execution time of full
benchmarks for different instances of
optimization algorithms. Each patch
presents one full benchmark
execution to evaluate an optimization
algorithm.
• Therefore, it is useful to consider speeding up of
benchmarking through vectorization of the tasks that a
benchmark is comprised of.
• These include e.g.,
• parallell data cleaning part of an
individual ML tile [1] or
• synchronization between tasks when
executing parallell geospatial processing
[3].
• To enable the possibilities of data cleaning
(preprocessing) as well as geospatial processing in
parallell, such opportunities ﬁrst need to be found or
designed, if none yet exist for a problem tackled.
• Therefore, this contribution will highlight some
experiences with ﬁnding and designing parallell ML
pipelines for vectorization and observe speedup gained
from that.
• The speeding up focus will be on optimization
algorithms within such ML pipelines, but some
more future work possibilities will also be provided.
[2] Zamuda, A, Brest, J., Self-adaptive control parameters’ randomization frequency and propagations in differential evolution. Swarm and
Evolutionary Computation 25C, 72-99 (2015).
[3] Zamuda, A., Hernández Sosa, J. D., Success history applied to expert system for underwater glider path planning using differential
evolution. Expert Systems with Applications 119, 155-170 (2019).

Warmup Highlights on Generative AI
w/ ChatGPT+Synthesia: visiting Canaries (ULPGC)
Photo/video: 1) Me at ULPGC EEI in the Erasmus+ cabinet (2012–); 2) with underwater
glider at ULPGC SIANI; 3) infront SIANI; 4) with autonomous sailboat at SIANI; 5)
rebooting in March 2023 (digital green) 6) HPC generated introduction

Load Balancing
Analytics (HPDA)
—
Part I: Backgrounds
—

Background A:
HPC Workloads and
Cloud Computing

Challenges
(First subpart)
Faced 5 types of challenges, leading to the needs to apply HPC
architectures for benchmarking state-of-the-art topics in
1 forest ecosystem modeling, simulation, and
visualization,
2 underwater robotic mission planning,
3 energy production scheduling for hydro-thermal power
plants,
4 understanding evolutionary algorithms, and
5 text summarization.

Challenges 1: Forest Ecosystem Modeling,
Simulation, and Visualization
• HPC need to process spatial data and add procedural
content.
Videos: https://www.youtube.com/watch?list=PL7pmTW8neV7tZf2qx1wV5zbD74sUyHL3B&v=V9YJgYO_sIA

Challenges 2: Underwater Robotic Mission Planning
• Computational Fluid Dynamics (CFD) spatio-temporal model of the
ocean currents for autonomous vehicle navigation path planning.
• Constrained Differential Evolution Optimization
for Underwater Glider Path Planning
in Sub-mesoscale Eddy Sampling.
• Corridor-constrained optimization: eddy
border region sampling — new challenge
for UGPP & DE.
• Feasible path area is constrained —
trajectory in corridor around the border of
an ocean eddy.
The objective of the glider here is to sample the
oceanographic variables more efﬁciently,
while keeping a bounded trajectory.
HPC: develop new methods and evaluate them.
Video: https://www.youtube.com/watch?v=4kCsXAehAmU

Challenges 3: Energy Production Scheduling for
Hydro-thermal Power Plants
A. Glotić, A. Zamuda. Short-term combined economic and emission
hydrothermal optimization by surrogate differential evolution.
Applied Energy, 1 March 2015, vol. 141, pp. 42-56.
DOI: 10.1016/j.apenergy.2014.12.020

Challenges 4: Understanding Evolutionary Algorithms
• Evolutionary algorithms benchmarking to
understand computational intelligence of
these algorithms (→ storage requirement!),
• aim: Machine Learning to design
an optimization algorithm
(learning to learn).
• Example CI Algorithm Mechanism Design:
Control Parameters Self-Adaptation (in DE).
Video: https://www.youtube.com/watch?v=R244LZpZSG0
Application stacks for real code:
inspired by previous computational
optimization competitions in
continuous settings that used
test functions for
optimization application domains:
• single-objective: CEC 2005,
2013, 2014, 2015
• constrained: CEC 2006, CEC 2007, CEC 2010
• multi-modal: CEC 2010, SWEVO 2016
• black-box (target value): BBOB 2009, COCO
2016
• noisy optimization: BBOB 2009
• large-scale: CEC 2008, CEC 2010
• dynamic: CEC 2009, CEC 2014
• real-world: CEC 2011
• computationally expensive: CEC 2013, CEC
2015
• learning-based: CEC 2015
• 100-digit (50% targets): 2019 joined CEC,
SEMCCO, GECCO
• multi-objective: CEC 2002, CEC 2007, CEC
2009, CEC 2014
• bi-objective: CEC 2008
• many objective: CEC 2018
Tuning/ranking/hyperheuristics use. → DEs as usual
winner algorithms.

Challenges 5: Text Summarization
For NLP, part of ”Big Data”.
Terms across sentences are determined
using a semantic analysis using both:
• coreference resolution (using WordNet) and
• a Concept Matrix (from Freeling).
INPUT
CORPUS
NATURAL
LANGUAGE
PROCESSING
ANALYSIS
CONCEPTS
DISTRIBUTION
PER
SENTENCES
MATRIX OF
CONCEPTS
PROCESSING
CORPUS
PREPROCESSING
PHASE
OPTIMIZATION
TASKS
EXECUTION
PHASE
ASSEMBLE
TASK
DESCRIPTION
SUBMIT
TASKS
TO PARALLEL
EXECUTION
OPTIMIZER
+
TASK DATA
ROUGE
EVALUATION
The detailed new method called
CaBiSDETS is developed in the HPC
approach comprising of:
• a version of evolutionary algorithm
(Differential Evolution, DE),
• self-adaptation, binarization,
constraint adjusting, and some more
pre-computation,
• optimizing the inputs to deﬁne the
summarization optimization model.
Aleš Zamuda, Elena Lloret. Optimizing Data-Driven
Models for Summarization as Parallel Tasks. Journal
of Computational Science, 2020, vol. 42, pp. 101101.
DOI: 10.1016/j.jocs.2020.101101

Challenges 6: new DAPHNE Use Cases
Example Use Cases
• DLR Earth Observation
• ESA Sentinel-1/2 datasets  4PB/year
• Training of local climate zone classifiers on So2Sat LCZ42
(15 experts, 400K instances, 10 labels each, ~55GB HDF5)
• ML pipeline: preprocessing,
ResNet-20, climate models
• IFAT Semiconductor Ion Beam Tuning
• KAI Semiconductor Material Degradation
• AVL Vehicle Development Process (ejector geometries, KPIs)
• ML-assisted simulations, data cleaning, augmentation
• Cleaning during exploratory query processing
[So2Sat LC42: https://mediatum.ub.tum.de/1454690]
[Xiao Xiang Zhu et al: So2Sat LCZ42: A
Benchmark Dataset for the Classification of
Global Local Climate Zones. GRSM 8(3) 2020]

HPC Initiatives
(Second subpart)
Timeline (as member) of recent impactful HPC initiatives including Slovenia:
• SLING: Slovenian national supercomputing network, 2010-05-03–,
• SIHPC: Slovenian High-Performance Computing Centre, 2016-03-04–
• ImAppNIO: Improving Applicability of Nature-Inspired Optimisation
by Joining Theory and Practice, 2016-03-09–2020-10-31
• cHiPSet: High-Performance Modelling and Simulation for Big Data
Applications, 2015-04-08–2019-04-07,
• HPC RIVR: UPGRADING OF NATIONAL RESEARCH INFRASTRUCTURES,
Investment Program, 2018-03-01–2020-09-15,
• TFoB: IEEE CIS Task Force on Benchmarking, January 2020–,
• EuroCC: National Competence Centres in the framework of EuroHPC,
2020-09-01–(2022-08-31),
• DAPHNE: Integrated Data Analysis Pipelines for Large-Scale Data
Management, HPC, and Machine Learning, 2020-12-01–(2024-11-30).

Initiatives: SLING, SIHPC, HPC RIVR, EuroCC
• There is a federated and orchestrated aim
towards HPC infrastructure in Slovenia,
especially through:
• SLING: Slovenian national supercomputing network
→ has federated the initiative push towards
orchestration of HPC resources across the country.
• SIHPC: Slovenian High-Performance Computing Centre
→ has orchestrated the ﬁrst EU funds application
towards HPC Teaming in the country
(and Participation of Slovenia in PRACE 2).
• HPC RIVR: UPGRADING OF NATIONAL RESEARCH
INFRASTRUCTURES, Investment Program
→ has provided an investment in experimental HPC
infrastructure.
• EuroCC: National Competence Centres in the framework of
EuroHPC
→ has secured a National Competence Centre (EuroHPC).
Vega supercomputer online
Consortium Slovenian High-Performance Computing Centre
Aškerčeva ulica 6
SI-1000 Ljubljana
Slovenia
Ljubljana, 22. 2. 2017
prof. dr. Anwar Osseyran
PRACE Council Chair
Subject: Participation of Slovenia in PRACE 2
Dear professor Osseyran,
In March 2016 a consortium Slovenski superračunalniški center (Slovenian High-Performance
Computing Centre - SIHPC) was established with a founding act (Attachment 1) where article 6
claims that the consortium will join PRACE and that University of Ljubljana, Faculty of
mechanical engineering (ULFME) will represent the consortium in the PRACE. The legal
representative of ULFME in the consortium and therefore, also in the PRACE (i.e., the delegate
in PRACE Council with full authorization) is prof. dr. Jožef Duhovnik, as follows from
appointment of the dean of ULFME (Attachment 2).
The consortium has also agreed that it joins PRACE 2 optional programme as contributing GP.
With best regards,
assist. prof. dr. Aleš Zamuda, vice-president of SIHPC

Initiatives: ImAppNIO, cHiPSet, TFoB, DAPHNE
Aim towards software to run HPC and improve capabilities:
• ImAppNIO: Improving Applicability of Nature-Inspired Optimisation
by Joining Theory and Practice,
→ improve capabilities through benchmarking (to understand (and to
learn to learn)) CI algorithms
• cHiPSet: High-Performance Modelling and Simulation for Big Data
Applications,
→ include HPC in Modelling and Simulation (of the process to be
learned)
• TFoB: IEEE CIS Task Force on Benchmarking,
→ includes CI benchmarking opportunities, where HPC would enable
new capabilities.
• DAPHNE: Integrated Data Analysis Pipelines for Large-Scale Data
Management, HPC, and Machine Learning.
→ to deﬁne and build an open and extensible system infrastructure
for integrated data analysis pipelines, including data management and
processing, high-performance computing (HPC), and machine learning
(ML) training and scoring https://daphne-eu.github.io/

EuroHPC Vega &
Deploying DAPHNE
(Third subpart)
MODA (Monitoring and Operational Data Analytics) tools for
• collecting, analyzing, and visualizing
• rich system and application data, and
• my opinion on how one can make sense of the data for
actionable insights.
• Explained through previous examples:
from a HPC User Perspective.

MODA Actionable Insights, Explained From a HPC
User Perspective, Through the Example of
Summarization
Most interesting findings of summarization on HPC example
are
• the fitness of the NLP model keeps increasing with prolonging
the dedicated HPC resources (see below) and that
• the fitness improvement correlates with ROUGE evaluation in
the benchmark, i.e. better summaries.
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
1 10 100 1000 10000
ROUGE-1R
ROUGE-2R
ROUGE-LR
ROUGE-SU4R
Fitness (scaled)
Hence, the use of HPC
significantly
contributes to
capability of this NLP
challenge.
However, the MODA insight also provided the
useful task running times and resource
usage.

Running the Tasks on HPC: ARC Job Preparation
Parallel summarization tasks on grid through ARC.

Running the Tasks on HPC: ARC Job Submission,
Results Retrieval & Merging [JoCS2020]
Through an HPC
approach and by
parallelization of tasks,
a data-driven
summarization model
optimization yields
improved benchmark
metric results (drawn
using gnuplot merge).
MODA is needed
to run again and
improve upon, to
forecast how to
set required task
running time and
resources
(predicting system
response).

Monitoring and Operational Data Analytics
• Monitor used (jobs, CPU/wall time, etc.):
Smirnova, Oxana. The Grid Monitor. Usage manual,
Tech. Rep. NORDUGRID-MANUAL-5, The NorduGrid
Collaboration, 2003.
http://www.nordugrid.org/documents/
http://www.nordugrid.org/manuals.html
http://www.nordugrid.org/documents/monitor.pdf
• Deployed at:
www.nordugrid.org/monitor/
• NorduGrid Grid Monitor
Sampled: 2021-06-28 at 17-57-08
• Nation-wide in Slovenia:
https://www.sling.si/gridmonitor/loadmon.php
http://www.nordugrid.org/monitor/index.php?
display=vo=Slovenia

MODA Example From: ARC at Jost
Example experiments from DOI: 10.1016/j.swevo.2015.10.007 (SPSRDEMMS)
– job YYULDmGOXpmnmmR0Xox1SiGmABFKDmABFKDmrtMKDmABFKDm66faPo.
Sample ARC ﬁle gridlog/diag (2–3 day Wall Times).
runtimeenvironments=APPS/ARNES/MPI=1.6=R ;
CPUUsage=99%
MaxResidentMemory=5824kB
AverageUnsharedMemory=0kB
AverageUnsharedStack=0kB
AverageSharedMemory=0kB
PageSize=4096B
MajorPageFaults=4
MinorPageFaults=1213758
Swaps=0
ForcedSwitches=36371494
WaitSwitches=170435
Inputs=45608
Outputs=477168
SocketReceived=0
SocketSent=0
Signals =0
nodename=wn003 . arnes . s i
WallTime=148332s
Processors=16
UserTime=147921.14s
KernelTime =2.54 s
AverageTotalMemory=0kB
AverageResidentMemory=0kB
LRMSStartTime=20150906104626Z
LRMSEndTime=20150908035838Z
exitcode=0

EuroCC HPC: Vega (TOP500 #106, HPCG #56 — June 2021)
• Researchers apply to EuroHPC JU calls for access.
• Regular calls opened in 2021 fall (Benchmark & Development).
• https://prace-ri.eu/benchmark-and-development-access-information-for-applicants/
• 60% capacities for national share (70% OA, 20% commercial, 10% host (community, urgent
priority of national importance, maintenance)) + 35% EuroHPC JU share (approved applications)
• Has a SLURM dev partition for SSH login (SLURM partitions w/ CPUs: login[0001-0004]=128;
login[0005-0008]=64; cn[0001-384,0577-0960]=256; cn[0385-0576]=256; gn[01-60]=256).
Listing 1: Setting up at Vega — slurm dev partition access (login).
1 [ ales . zamuda@vglogin0007 ˜]$ s i n g u l a r i t y pull qmake . s i f docker : / / ak352 /qmake=opencv
2 [ ales . zamuda@vglogin0007 ˜]$ s i n g u l a r i t y run qmake . s i f bash
3 cd sum; qmake ; make clean ; make
4
5 [ ales . zamuda@vglogin0007 ˜]$ cat runme . sh
6 # ! / bin / bash
7 cd sum && time mpirun
8 ==mca btl openib warn no device params found 0
9 . / summarizer
10 ==useBinaryDEMPI ==i n p u t f i l e mRNA=1273=t x t
11 ==withoutStatementMarkersInput
12 ==printPreprocessProgress calcInverseTermFrequencyndTermWeights
13 ==printOptimizationBestInGeneration
14 ==summarylength 600 ==NP 200
15 ==GMAX 400
16 > summarizer . out . $SLURM PROCID
17 2> summarizer . err . $SLURM PROCID
Text summarization/generation systems
are getting more and more useful
and accessible on deployed systems
(e.g. OpenAI’s ChatGPT, Microsoft’s Bing AI part,
NVIDIA’s (Fin)Megatron, BLOOM,
LaMDA, BERT, VALL-E, Point-E, etc.). -0.65
-0.6
-0.55
-0.5
-0.45
-0.4
-0.35
-0.3
-0.25
-0.2
-0.15
-0.1
1 10 100
Evaluation

MODA at First EuroCC HPC Vega Supercomputer
Listing 2: Runnig at Vega & MODA.
1 ===================================================================== GMAX=200 =====
2 [ ales . zamuda@vglogin0002 ˜]$ srun ==cpu=bind=cores ==nodes=1 ==ntasks=per=node=101
3 ==cpus=per=task=2 ==mem=180G s i n g u l a r i t y run qmake . s i f bash
4 srun : job 4531374 queued and waiting for resources
5 srun : job 4531374 has been allocated resources
6 [ ”$SLURM PROCID” = 0 ] && . / runme . sh
7 real 5m22.475 s
8 user 484m42.262 s
9 sys 1m38.304 s
10 ===================================================================== NODES=51 =====
16 real 13m57.851 s
17 user 431m25.833 s
18 sys 0m29.272 s
19 ===================================================================== GMAX=400 =====
25 real 6m14.687 s
26 user 590m45.641 s
27 sys 1m40.930 s

More Output: SLURM accounting
Listing 3: Example accounting tool at Vega: sacct.
[ ales . zamuda@vglogin0002 ˜]$ sacct
4531374. ext+ extern vega=users 202 COMPLETED 0:0
[ ales . zamuda@vglogin0002 ˜]$ sacct =j 4531374 =j 4531746 =j 4532697
=o MaxRSS , MaxVMSize , AvePages
MaxRSS MaxVMSize AvePages
==============================
0 217052K 0
26403828K 1264384K 22
0 217052K 0
13325268K 1264380K 0
0 217052K 0
26404356K 1264384K 30
Future MODA testings:
• testing the web interface for job analysis (as available from HPC RIVR);
• proﬁling MPI inter-node communication;
• use proﬁlers and monitoring tools available
— in the context of heterogeneous setups, like e.g.
• TAU Performance System — http://www.cs.uoregon.edu/research/tau/home.php,
• LIKWID Performance Tools — https://hpc.fau.de/research/tools/likwid/.

Deploying DAPHNE on Vega
Main documentation ﬁle:
Deploy.md

SLURM

More HPC User Perspective Nation-wide in Slovenia
More: at University of Maribor, Bologna study courses for
teaching (training) of Computer Science at cycles — click URL:
• level 1 (BSc)
• year 1: Programming I – e.g. C++ syntax
• year 2: Computer Architectures – e.g. assembly, microcode, ILP
• year 3: Parallell and Distributed Computing – e.g. OpenMP, MPI, CUDA
• level 2 (MSc)
• year 1: Cloud Computing Deployment and Management – e.g. arc, slurm,
Hadoop, containers (docker, singularity) through
virtualization
• level 3 (PhD)
• EU and other national projects research:
HPC RIVR, EuroCC, DAPHNE, ... – e.g. scaling new systems
of CI & Operational Research of ... over HPC
• IEEE Computational Intelligence Task Force on Benchmarking
• Scientiﬁc Journals (e.g. SWEVO, TEVC, JoCS, ASOC, INS)
These contribute towards Sustainable Development of HPC.

Background B:
ML workloads

Optimization Beginnings - Optimization is
”Everywhere”
• Time: optimizing distribution of what is matter and what is not
(anti-matter), what is energy and what is not (dark energy), etc.:
according to the function of Nature, the system is propelled through
optimizing its constituents dynamics.
• Organic systems combination and propulsion: life (optimization).
• Optimality and optimization modeling (human builds tools).
• Describing ways of acchieving optimality.
• Mathematical optimization procedure deﬁned (Kepler).
• Stepping towards optimum (Newton), gradient method (Lagrange).
• Multi-objective optimization (Pareto):
• meta-criterion (A ⪯ B): make criteria ordered by
dominance.
f′
(x) =
∆f(x)
∆x
,
f∗
(x) = f(x) + ∆xf′
(x).
1
2 2
f
x
x 1
f
( )
A
B
C
D
f x
f(B)
(A)
f
f(D)
0
0
E
f(E)
F
G f
(C)
f
f(F)
(G)

Introduction to Optimization Algorithms
and Mathematical Programming
• Global optimization, mathematical programming, digital computers.
• Computing Machines + Intelligence = Artiﬁcial Intelligence.
• Computational Intelligence.
• Simplistic numerical optimization algorithms:
hill climbing, Nelder-Mead, supervised random search,
simulated annealing, tabu search.
• Optimization: constrained, inseparable, multi-modal, multi-objective,
dynamic, noisy, high dimensional/large-scale/big-data, deceptive, etc.
• multi-objective: f(x)): Pareto optimal approximation set.

Evolutionary Computation and Algorithms
• Evolution theory: C. Darwin (1859), Weismann, Mendel.
• Popularization: darwinism (Huxley), neodarwinism
(Romanes).
• Generational: reproduction, mutation, competition,
selection.
• Evolutionary Computation: Evolutionary Algorithms (EAs)
• population generations (reproduction-based),
• mutation, crossover, selection (evolutionary operators),
• EAs comprised of different mechanisms.
• These algorithms share several common
mechanisms/operators,
• good conﬁgured DEs were prevalent at the winning
positions of all (CEC, including ICEC 1996) competitions on
optimization.

Evolutionary Computation and Algorithms: Given
Names
• Simulated Annealing (SA),
• Tabu Search (TS),
• Genetic Algorithms (GA),
• Genetic Programming (GP),
• Evolutionary Programming (EP),
• Memetic Algorithms (MA),
• Evolution Strategy (ES),
• Artificial Immune Systems (AIS),
• Cultural Algorithms (CA)
• Swarm Intelligence (SI),
• Particle Swarm Optimization
(PSO),
• Firefly Algorithm (FA),
• Ant Colony Optimization (ACO),
• Artificial Bee Colony (ABC),
• Cuckoo Search (CS),
• Artificial Weed Optimization (IWO),
• Bacterial Foraging
Optimization(BFO),
• Estimation of Distribution Alg. (EDA),
• Harmony Search (HS),
• Gravitational Search Algorithm
(GSA),
• Biogeography-based
Optimization(BBO),
• Differential Evolution (DE)
and its variants (jDE, L-SHADE,
DISH),
• ... and many more, including
hybrids.

Range of Applications of the Optimization Algorithms
• Meta-heuristics algorithms, applicable to:
• (architectural) morphology (re)construction
(vivo/technical),
• artificial life:
• modeling ecosystem and environmental living conditions,
• e.g.: (automatic) procedural tree modeling,
interactive ecosystem breeding.
• pattern recognition, image processing, computer vision,
• language/documents understanding, speech processing,
• robotics, bioinformatics, chemical engineering,
manufacturing,
• oil search, nuclear plant safety, finance, electrical
engineering,
• energy, big data, data mining, security, ocean/space
research,
• systems of systems, ..., artificial intelligence.

Differential Evolution (DE)
• A ﬂoating point encoding EA for global optimization over
continuous spaces,
• through generations,
the evolution process improves population of vectors,
• iteratively by combining a parent individual and
several other individuals of the same population,
using evolutionary operators.
• We choose the strategy jDE/rand/1/bin
• mutation: vi,G+1 = xr1,G + F × (xr2,G − xr3,G),
• crossover:
ui,j,G+1 =
(
vi,j,G+1 if rand(0, 1) ≤ CR or j = jrand
xi,j,G otherwise
,
• selection: xi,G+1 =
(
ui,G+1 if f(ui,G+1) < f(xi,G)
xi,G otherwise
,

Algorithm DE
1: algorithm canonical algorithm DE/rand/1/bin (Storn,
1997)
Require: f(x) – fitness function; D, NP, G – DE control parameters
Ensure: xbest – includes optimized parameters for the fitness function
2: Uniform randomly initialize the population (xi,0, i = 1..NP);
3: for DE generation loop g (until g < G) do
4: for DE iteration loop i (for all vectors xi,g in current population) do
5: DE trial vector computation xi,g (mutation, crossover):
6: vi,g+1 = xr1,g + F × (xr2,g − xr3,g);
7: ui,j,g+1 =
(
vi,j,g+1 if rand(0, 1) ≤ CR or j = jrand
xi,j,g otherwise
;
8: DE selection using fitness evaluation f(ui,G+1):
9: xi,g+1 =
(
ui,g+1 if f(ui,g+1) < f(xi,g)
xi,g otherwise
;
10: end for
11: end for
12: return best obtained vector (xbest);

Control Parameters Self-Adaptation
• Through more suitable values of control parameters the
search process exhibits a better convergence,
• therefore the search converges faster to better solutions,
which survive with greater probability and they create
more offspring and propagate their control parameters
• Recent study with cca. 10 million runs of SPSRDEMMS:
A. Zamuda, J. Brest. Self-adaptive control parameters’
randomization frequency and propagations in differential
evolution. Swarm and Evolutionary Computation, 2015,
vol. 25C, pp. 72-99.
DOI 10.1016/j.swevo.2015.10.007.
– SWEVO 2015 RAMONA / SNIP 5.220

Self-adaptive control parameters’ randomization
frequency and propagations in differential evolution
– Overview
• Randomization frequency
inﬂuences performance
(SPSRDEMMS on right)
• Suggesting values for
different problems
• 0.1 to 0.8 for τF,
0.05 to 0.25 for τCR
• Empirical insight into
operation of the
randomization mechanism

Listing Some More DE-Family Algorithms Proposed
• My algorithms (CEC – world championships on EAs):
• SA-DE (CEC 2005: SO) – book chapter JCR,
• MOjDE (CEC 2007: MO) – vs. DEMO 40/57 IR, 39/57 IH,
• DEMOwSA (CEC 2007: MO) – rank #3, 53 citations,
• DEwSAcc (CEC 2008: LSGO) – 63 citations,
• DEMOwSA-SQP (CEC 2009: CMO) – rank #2, 47 citations,
• DECMOSA-SQP (CEC 2009: CMO) – rank #3 at 2 functions,
• jDENP,MM (CEC 2011: RWIC) – LNCS SIDE 2012,
• SPSRDEMMS (CEC 2013: RPSOO); Large-scale @SWEVO.
• DISH (SWEVO 2019) – best CEC 2015 & 2017 results.
• Performance assessment of the algorithms at world EA
championships: several times best on some criteria
(also won CEC 2009 dynamic optimization competition).
• Performance assessment on several industry challenges
• procedural tree models reconstruction (ASOC 2011, INS
2013),
• underwater glider path planning (ASOC 2014),
• hydro-thermal energy scheduling (APEN 2015),
• RWIC (Real World Industry Challenges) - CEC 2011; 2013, ...

SPSRDEMMS: Example of Optimization Mechanisms
• SPSRDEMMS = Structured Population Size Reduction
Differential Evolution with Multiple Mutation Strategies
• canonical DE, upgraded with: mechanism of F and CR
control parameters self-adaptation, mutation strategy
ensembles, population structuring (distributed islands),
and population size reduction.
• is an extension of the jDENP,MM variant (Zamuda and
Brest, SIDE 2012) and was published at CEC 2013
(competition).
• The SPSRDEMMS, for a ﬁxed part of the population (NPbest
number of individuals at end of the entire population),
executes only the best/1 strategy.
• This part of population (which might be seen as a
sub-population) has a separate best vector index, xbest bestpop.
• The ﬁrst part of the population (mainpop) operates on target
vectors xi ∈ {x1...xNP−NPbest} and second part (bestpop)
operates on target vectors xi = {xNP−NPbest+1...xNP}.
• Both strategies generate mutation vectors using all vectors of
the population x1...xNP, i.e. r1, r2, r3 ∈ {1..NP}.

Self-adaptive control parameters’ randomization
frequency and propagations in differential evolution
– Methods
• G. Karafotias, M. Hoogendoorn, A. Eiben,
Parameter control in evolutionary algorithms:
trends and challenges, IEEE Trans. Evolut.
Comput. 19 (2) (2015) 167–187.
• A. Zamuda, J. Brest, E. Mezura-Montes,
Structured population size reduction
differential evolution with multiple mutation
strategies on CEC 2013 real parameter
optimization, in: Proceedings of the 2013 IEEE
Congress on Evolutionary Computation (CEC),
vol. 1, 2013, pp. 1925–1931.
• J. Brest, S. Greiner, B. Bošković, M. Mernik, V.
Žumer, Self-adapting control parameters in
differential evolution: a comparative study on
numerical benchmark problems, IEEE Trans.
Evolut. Comput. 10 (6) (2006) 646–657.
• Parameter control study
• Systematic approach to
answering questions about the
control parameters
mechanism
• For certain interesting
functions, deeper insight is
shown

Other Enhancements / Improvements / Mechanisms
in DE
DE, jDE, SaDE, ODE, DEGL, JADE, EPSDE; ϵ-DE, DDE, CDE; PDE,
GDE, DEMO, MOEA/D, ...
• Swagatam Das and Ponnuthurai Nagaratnam Suganthan.
”Differential evolution: a survey of the
state-of-the-art.” IEEE Transactions on Evolutionary
Computation 15(1), 2011: 4-31. DOI:
10.1109/TEVC.2010.2059031.
CoDE, Compact DE, L-SHADE, Binary DE,
Successful-Parent-Selecting Framework DE, ...
• Swagatam Das, Sankha Subhra Mullick, and Ponnuthurai
Nagaratnam Suganthan.
”Recent Advances in Differential Evolution –
An Updated Survey.”
Swarm and Evolutionary Computation, Volume 27, April
2016, Pages 1-30, 2016.
DOI: 10.1016/j.swevo.2016.01.004.
Several hybridizations, improvements, and general
mechanisms.

Load Balancing
Analytics (HPDA)
—
Part II: Hydro and Thermal
Powerplants Grid Scheduling
—

Introduction
Part I
Challenges
HPC Initiatives
EuroHPC Vega &
Deploying DAPHNE
Singularity, Intel oneAPI, DAPHNE
CI
DIFFERENTIAL EVOLUTION
HTS
CI: Hydro and Thermal Power Plant Scheduling
Conclusion
Subpart 1: HYDRO-THERMAL
SCHEDULING

HTS: Hydro Power Plants (HPPs) and Thermal Power
Plants (TPPs) Scheduling – Introduction

Hydro and Thermal Power Plants Systems Model:
Nomenclature

Deﬁnitions

Models
• The application
contributes to algorithms
development,
with the aims of:
• improving performance
of the electrical energy
production and
• emissions and carbon
footprint reduction
(thermal units),
• while simultaneously
satisfying a 24-hour
system demands in
scheduling energy
demand and all other
operational
requirements.

Nomenclature and Deﬁnitions
• Equality constraints for energy production (scheduling).
• Constraints handling during optimization: ϵ-comparison.
• Algorithm output: 24-hour settings plan for TPPs and
HPPs.

Introduction
Part I
Challenges
HPC Initiatives
EuroHPC Vega &
Deploying DAPHNE
CI
HTS
Conclusion
Subpart 2: PARALLEL DIFFEREN-
TIAL EVOLUTION

Additional DE Mechanisms and Parallelization of HTS
• Population size (NP) adjustment, multilevel parallelization,
• sub-population gathering and BmW offspring strategy:
• A. Glotić, A. Glotić, P. Kitak, J. Pihler, I. Tičar. Parallel self-adaptive differential evolution
algorithm for solving short-term hydro scheduling problem. IEEE Transactions on Power Systems
2014, 29 (5), pp. 2347–2358.

HTS Pallelization and Pre-processing (1/3)
• New approach in optimization for scheduling energy
production among units of HPPs (hydro) and TPPs
(thermo).
• The approach allows a faster computation than before:
• 1) The DE for HTS is parallelized.
• 2) The TPPs optimization part features a novel
architecture, including a pre-computed surrogate model,
• this model is same during optimization of the whole HTS
optimized model for hydro and thermo units
(pre-processing), and
• obtained parameter values (x) of the TPPs surrogate model
on practical accuracy are stored for re-use in the global HTS
optimization.

• Two algorithms are built for this approach:
• the ﬁrst algorithm (NPdynϵjDE) addresses a specialized
treatment of constraints handling and optimizes
scheduling for TPPs – thermo units parameters are
pre-computed up to practical accuracy,
• the second algorithm (PSADEs) utilizes the output of the
ﬁrst algorithm, in order to optimize combined
production of hydro units, where hydro units settings are
probed and thermo units settings are looked-up in the
surrogate model matrix output from the NPdynϵjDE;
• here, both algorithms use
practical accuracy of the parameters for time schedule
of the thermo units load.

• The results of testing this approach on established HTS
benchmarks from literature show:
a larger performance improvement on all scenarios
under all criteria, compared to the approaches known
before. (Still.)
• Literature article with detailed results coverage follows:
A. Glotić, A. Zamuda. Short-term combined economic and
emission hydrothermal optimization by surrogate
differential evolution. Applied Energy, 1 March 2015, vol.
141, pp. 42-56. DOI 10.1016/j.apenergy.2014.12.020.
IF=5.613

Introduction
Part I
Challenges
HPC Initiatives
EuroHPC Vega &
Deploying DAPHNE
CI
HTS
Conclusion
Subpart 3: SURROGATE DIFFER-
ENTIAL EVOLUTION

HTS Optimization: New DE Algorithms Architecture
Surrogate Matrix – Input and Output

Fitness Function and Practical Accuracy (discretized)

Fitness Function and Practical Accuracy: the Algorithm

Results and Comparisons (1/2)
ELS
@NPdynϵjDE
EES
@NPdynϵjDE
CEES
@NPdynϵjDE

Results and Comparisons (2/2)
• Results on different types of scheduling, compared to best
works from literature: the proposed approach
outperforms all by far, for all models: ELS, EES, and CEES,
• This approach exhibits as well the lowest time latency (due
to PSADEs parallelization and NPdynϵjDE pre-processing).

A. Glotić, A. Zamuda. Short-term combined economic and emission
hydrothermal optimization by surrogate differential evolution.
Applied Energy, 1 March 2015, vol. 141, pp. 42-56.
DOI 10.1016/j.apenergy.2014.12.020. IF=5.613

Load Balancing
Analytics (HPDA)
—
Part III: Conclusion
w/ Appendix
—

Conclusion
Summary: vectorized benchmarking, speed up, and impact
— in the context of superpower for the power grid & HTS.
Thanks!
Acknowledgement: this work is supported by ARRS programme P2-0041; and
DAPHNE, funded by the European Union’s Horizon 2020 research and innovation
programme under grant agreement No 957407.
Questions?

References
Biography and References: Organizations
• Associate Professor at University of Maribor, Slovenia
• Continuous research programme funded by Slovenian Research Agency,
P2-0041: Computer Systems, Methodologies, and Intelligent Services
• EU H2020 Research and Innovation project, holder for UM part: Integrated
Data Analysis Pipelines for Large-Scale Data Management, HPC, and
Machine Learning (DAPHNE), https://cordis.europa.eu/project/id/957407
• IEEE (Institute of Electrical and Electronics Engineers)
senior
• IEEE Computational Intelligence Society (CIS), senior member
• IEEE CIS Task Force on Benchmarking, chair Website link
• IEEE CIS, Slovenia Section Chapter (CH08873), chair
• IEEE Slovenia Section, 2018–2021 vice chair, 2018-21
• IEEE Young Professionals Slovenia, 2016-19 chair
• ACM SIGEVO (Special Interest Group on Genetic and Evolutionary Computation); EurAI; SLAIS
• Associate Editor: Swarm and Evolutionary Computation (2016-’22), Human-centric Computing and
Information Sciences, Frontiers in robotics and AI (section Robot Learning and Evolution)
• Co-operation in Science and Techology (COST) Association Management
Committee, member:
• CA COST Action CA15140: Improving Applicability of Nature-Inspired
Optimisation by Joining Theory and Practice (ImAppNIO), WG3 VC
• ICT COST Action IC1406 High-Performance Modelling and Simulation for
Big Data Applications (cHiPSet); OTHER: SI-HPC vice-chair; HPC-RIVR user; EuroHPC
Vega user

References
Biography and References: Top Publications
• Aleš Zamuda, Elena Lloret. Optimizing Data-Driven Models for Summarization as Parallel Tasks.
Journal of Computational Science, 2020, vol. 42, pp. 101101. DOI 10.1016/j.jocs.2020.101101.
• A. Zamuda, J. D. Hernández Sosa. Success history applied to expert system for underwater glider path
planning using differential evolution. Expert Systems with Applications, 2019, vol. 119, pp. 155-170. DOI
10.1016/j.eswa.2018.10.048
• C. Lucas, D. Hernández-Sosa, D. Greiner, A. Zamuda, R. Caldeira. An Approach to Multi-Objective Path
Planning Optimization for Underwater Gliders. Sensors, 2019, vol. 19, no. 24, pp. 5506. DOI
10.3390/s19245506.
• A. Viktorin, R. Senkerik, M. Pluhacek, T. Kadavy, A. Zamuda. Distance Based Parameter Adaptation for
Success-History based Differential Evolution. Swarm and Evolutionary Computation, 2019, vol. 50, pp.
100462. DOI 10.1016/j.swevo.2018.10.013.
• A. Zamuda, J. Brest. Self-adaptive control parameters’ randomization frequency and propagations
in differential evolution. Swarm and Evolutionary Computation, 2015, vol. 25C, pp. 72-99.
DOI 10.1016/j.swevo.2015.10.007.
• A. Zamuda, J. D. Hernández Sosa, L. Adler. Constrained Differential Evolution Optimization for
Underwater Glider Path Planning in Sub-mesoscale Eddy Sampling. Applied Soft Computing, 2016,
vol. 42, pp. 93-118. DOI 10.1016/j.asoc.2016.01.038.
• A. Zamuda, J. D. Hernández Sosa. Differential Evolution and Underwater Glider Path Planning
Applied to the Short-Term Opportunistic Sampling of Dynamic Mesoscale Ocean Structures.
Applied Soft Computing, vol. 24, November 2014, pp. 95-108. DOI 10.1016/j.asoc.2014.06.048.
• A. Zamuda, J. Brest. Vectorized Procedural Models for Animated Trees Reconstruction using
Differential Evolution. Information Sciences, vol. 278, pp. 1-21, 2014. DOI 10.1016/j.ins.2014.04.037.
• A. Zamuda, J. Brest. Environmental Framework to Visualize Emergent Artiﬁcial Forest Ecosystems.
Information Sciences, vol. 220, pp. 522-540, 2013. DOI 10.1016/j.ins.2012.07.031.
• A. Glotić, A. Zamuda. Short-term combined economic and emission hydrothermal optimization by
surrogate differential evolution. Applied Energy, 1 March 2015, vol. 141, pp. 42-56.
DOI 10.1016/j.apenergy.2014.12.020.
• H. Hamann, Y. Khaluf, J. Botev, M. Divband Soorati, E. Ferrante, O. Kosak, J.-M. Montanier, S. Mostaghim,
R. Redpath, J. Timmis, F. Veenstra, M. Wahby and A. Zamuda. Hybrid Societies: Challenges and
Perspectives in the Design of Collective Behavior in Self-organizing Systems. Frontiers in Robotics
and AI, 2016, vol. 3, no. 14. DOI 10.3389/frobt.2016.00014.
• J. Šilc, A. Zamuda. Special Issue on ”Bioinspired Optimization” (guest editors). Informatica - An
International Journal of Computing and Informatics, 2015, vol. 39, no. 2, pp. 1-122.

References
Biography and References: Bound Speciﬁc to HPC
PROJECTS:
• DAPHNE: Integrated Data Analysis Pipelines for Large-Scale Data Management,
HPC, and Machine Learning
• ICT COST Action IC1406 High-Performance Modelling and Simulation for Big
Data Applications
• SLING: Slovenian national supercomputing network
• SI-HPC: Slovenian corsortium for High-Performance Computing
• UM HPC-RIVR: Supercomputer at UM, https://www.hpc-rivr.si/
• SmartVillages: Smart digital transformation of villages in the Alpine Space
• Interreg Alpine Space,
https://www.alpine-space.eu/projects/smartvillages/en/home
• Interactive multimedia digital signage (PKP, Adin DS)
EDITOR:
• SWEVO (Top Journal), Associate Editor
• Mathematics-MDPI, Special Issue: Evolutionary Algorithms in Engineering Design Optimization
• Journal of advanced engineering and computation (member of editorial board since 2019). Viet Nam: Ton
Duc Thang University, 2017-. ISSN 2588-123X.
• Cloud Computing and Data Science (Associate Editor, since 2019). Universal Wiser Publisher Pte.Ltd.
• D. Gleich, P. Planinšič, A. Zamuda. 2018 25th International Conference on Systems, Signals and Image
Processing (IWSSIP). IEEE Xplore, Maribor, 20-22 June 2018.
• General Chair: 7-th Joint International Conferences on Swarm, Evolutionary and Memetic Computing
Conference (SEMCCO 2019) & Fuzzy And Neural Computing Conference (FANCCO 2019), Maribor, Slovenia,
EU, 10-12 July 2019; co-editors: Aleš Zamuda, Swagatam Das, Ponnuthurai Nagaratnam Suganthan, Bijaya
Ketan Panigrahi.
• Organizers member: GECCO 2022, GECCO 2023

References
Biography and References: More Publications on HPC
• Aleš Zamuda, Elena Lloret. Optimizing Data-Driven Models for Summarization as Parallel Tasks.
Journal of Computational Science, 2020, vol. 42, pp. 101101. DOI 10.1016/j.jocs.2020.101101.
• Patrick Damme, Marius Birkenbach, Constantinos Bitsakos, Matthias Boehm, Philippe Bonnet, Florina
Ciorba, Mark Dokter, Pawel Dowgiallo, Ahmed Eleliemy, Christian Faerber, Georgios Goumas, Dirk Habich,
Niclas Hedam, Marlies Hofer, Wenjun Huang, Kevin Innerebner, Vasileios Karakostas, Roman Kern, Tomaž
Kosar, Alexander Krause, Daniel Krems, Andreas Laber, Wolfgang Lehner, Eric Mier, Marcus Paradies,
Bernhard Peischl, Gabrielle Poerwawinata, Stratos Psomadakis, Tilmann Rabl, Piotr Ratuszniak, Pedro
Silva, Nikolai Skuppin, Andreas Starzacher, Benjamin Steinwender, Ilin Tolovski, Pınar Tözün, Wojciech
Ulatowski, Yuanyuan Wang, Izajasz Wrosz, Aleš Zamuda, Ce Zhang, Xiao Xiang Zhu. DAPHNE: An Open
and Extensible System Infrastructure for Integrated Data Analysis Pipelines. 12th Conference on
Innovative Data Systems Research, CIDR 2022, Chaminade, CA, January 9-12, 2022.
• Aleš Zamuda, Vincenzo Crescimanna, Juan C. Burguillo, Joana Matos Dias, Katarzyna Wegrzyn-Wolska,
Imen Rached, Horacio González-Vélez, Roman Senkerik, Claudia Pop, Tudor Cioara, Ioan Salomie, Andrea
Bracciali. Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the
State-of-the-Art in the Cloud Era. Kołodziej J., González-Vélez H. (eds) High-Performance Modelling and
Simulation for Big Data Applications. Lecture Notes in Computer Science, vol 11400, 2019, pp. 325-349.
DOI 10.1007/978-3-030-16272-6 12.
• Nenad Karolija, Aleš Zamuda. On cloud-supported web-based integrated development environment
for programming dataﬂow architectures. MILUTINOVIĆ, Veljko (ur.), KOTLAR, Milos (ur.). Exploring the
DataFlow supercomputing paradigm: example algorithms for selected applications, (Computer
communications and networks (Internet), ISSN 2197-8433), 2019, pp. 41-51. DOI
10.1007/978-3-030-13803-5 2.
• Simone Spolaor, Marco Gribaudo, Mauro Iacono, Tomas Kadavy, Zuzana Komı́nková Oplatková, Giancarlo
Mauri, Sabri Pllana, Roman Senkerik, Natalija Stojanovic, Esko Turunen, Adam Viktorin, Salvatore Vitabile,
Aleš Zamuda, Marco S. Nobile. Towards Human Cell Simulation. Kołodziej J., González-Vélez H. (eds)
High-Performance Modelling and Simulation for Big Data Applications. Lecture Notes in Computer
Science, vol 11400, 2019, pp. 221-249. DOI 10.1007/978-3-030-16272-6 8.
• A. Zamuda, J. D. Hernandez Sosa, L. Adler. Improving Constrained Glider Trajectories for Ocean Eddy
Border Sampling within Extended Mission Planning Time. IEEE Congress on Evolutionary Computation
(CEC) 2016, 2016, pp. 1727-1734.
• A. Zamuda. Function evaluations upto 1e+12 and large population sizes assessed in distance-based
success history differential evolution for 100-digit challenge and numerical optimization scenarios
(DISHchain 1e+12): a competition entry for ”100-digit challenge, and four other numerical optimization
competitions” at the genetic and evolutionary computation conference (GECCO) 2019. Proceedings of the
Genetic and Evolutionary Computation Conference Companion (GECCO 2019), 2019, pp. 11-12.
• ... several more experiments for papers run using HPCs.
• ... also, pedagogic materials in Slovenian and English — see Conclusion .

References
Promo materials: Calls for Papers, Informational
Websites
CS FERI WWW
CIS TFoB
CFPs WWW
LinkedIn
Twitter

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